University Of Florida

NSF Awards · FY 2024 · 2024-11

Biological phenomena are often driven by complex dynamic regulatory networks. In natural or engineered systems, complicated structures can be generated from simpler building blocks, or modules. This notion of complex systems built from modules is also prevalent in modern systems biology. However, a clear theoretical foundation of modularity, including useful definitions of basic concepts and mechanisms, is still missing. This research project will fill this gap by defining modular structures in biological systems in a mathematically rigorous way. The research will determine why modularity can be advantageous to an organism and elucidate how modularity can be leveraged to advance our understanding of molecular systems. Studying the modularity of specific gene regulatory networks underlying salamander limb regeneration as well as hormone regulation in plants harbors the potential to reveal novel biological insights. Through involvement of students in all aspects of the research, this project contributes to the interdisciplinary training of STEM workforce. The dissemination of results through a dedicated project website and webinars enables anyone to analyze biological network models. The foundation of this project is a rigorous, structure-based definition of modularity in the context of Boolean networks, a common modeling framework in systems biology. Through computational, experimental, and theoretical studies, it will be shown that this definition of modularity (i) is biologically meaningful, (ii) implies a decomposition of the dynamics of Boolean networks, which can be employed to efficiently compute their dynamics, and (iii) that modular networks can be controlled effectively. The theoretical results, including theorems and implemented algorithms for practical computation, will advance the body of knowledge in the fields of network analysis, systems biology, and developmental biology. The validity of the project will be demonstrated through (1) in vivo analyses in the model plant Arabidopsis, (2) in silico analyses in an emerging animal model, axolotl. This will yield novel biological insights regarding (1) the interplay between phytohormones during Arabidopsis organogenesis, and (2) gene regulatory networks directing fibroblast reprogramming in axolotls. 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 · 2024-11

Functional diversity refers to the range of biological functions and ecological roles that organisms within a community perform. While functional diversity has been measured in various modern communities, it remains unknown how it changes over long timescales or how it responds to sudden shifts in climate. The Paleocene-Eocene Thermal Maximum (PETM) was a period of rapid, intense global warming ~56 million years ago that is well preserved in the Bighorn Basin, WY. This PETM record documents changes in temperature and aridity, fossil leaves and pollen, and a sample of 20,000+ fossil vertebrates in a precise temporal framework. This project analyzes changes in the functional diversity of mammal communities from before, during, and after the PETM. Elucidating the relationship between mammal functional diversity, floral composition, and abiotic factors such as temperature is key to understanding the biotic response to changes in climate. To achieve these goals, this project develops new, publicly-available AI tools with broad flexibility for paleontologists, recruits undergraduate students for training in STEM research, and implements a fieldwork education program designed to train the next generation of paleontologists. To characterize mammalian functional diversity, this project generates rich 3D datasets from micro-CT scans of mammalian molars, tarsal elements, and distal phalanges. A new set of AI tools will be trained on diverse pilot datasets to rapidly isolate, segment, and crop 3D surfaces of these elements for functional measurements. Measurements include molar dental topography, tarsal facet curvature, and linear measurements of distal phalanges from a stratigraphically-resolved fossil sample spanning the PETM. Measurement of functional diversity permits characterization of the mammalian fauna without resolving many challenging and complex questions about taxonomic affiliations while directly testing whether abiotic drivers such as temperature or biotic drivers such as plant community composition have a greater influence on mammalian functional diversity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This IUSE Level 1 Engaged Student Learning project aims to serve the national interest by improving training in construction engineering-related courses through VirtuAI-Story - an innovative tool to be created through this project to enable personalized learning in a Virtual Reality (VR) environment enhanced by Artificial Intelligence (AI). By combining the immersive experience of VR with adaptive storytelling from large language models (LLMs), the project intends to provide engaging educational content and create a safe and controlled space for practical training while addressing the need for safer work practices. VirtuAI-Story is intended to advance learner preparation for real-world jobs, especially in high-risk fields where practical skills are crucial. The project's insights could apply more broadly to various fields where hands-on training is essential. The project also aims to understand how learners interact with an AI-driven virtual agent, providing valuable insights into human-AI dynamics in education. Importantly, the project will aim to recruit underrepresented groups and low-income students, enhancing their participation in STEM programs. Through development and evaluation of VirtuAI-Story, this project aims to to address three research questions. Research Question #1: How effectively can situated learning through VR combined with personalization through LLMs be delivered through adaptive storytelling? Research Question #2: How does VirtuAI-Story impact students' engagement (i.e. cognitive, behavioral, affective)? Research Question #3: How does VirtuAI-Story impact students' learning outcomes (i.e. factual knowledge, conceptual knowledge, procedural knowledge, transfer of knowledge)? Grounded in Situated Cognition and the Cognitive Affective Model of Immersive Learning (CAMIL), the project plans to explore how personalized immersive environments affect learning and engagement. The research methodology involves content creation, developing VR and LLM components, pilot testing, and comprehensive assessments to evaluate usability, learning outcomes, and student engagement. A goal of the project is to discover how personalized immersive environments impact learning processes, engagement, and outcomes. By applying this technology to construction safety instruction, the project intends to produce empirical evidence on the effectiveness of situated learning environments. Furthermore, the project endeavors to integrate AI-driven assessments with human evaluations to thoroughly analyze various aspects of student learning and engagement. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Machine learning is increasingly being integrated into wireless network applications, such as video surveillance, smart healthcare and industrial internet of things, to derive actionable intelligence from the rich data collected or generated by wireless devices. To support real-time machine learning and decision making in these applications, a large amount of data has to be transferred from the data source to the destination and processed by an inference algorithm in a timely manner. Traditionally, data transfer and machine learning inference are treated as two separate optimization tasks, but such approaches are inefficient as they ignored the interaction between data transfer and machine learning inference, resulting in either a large data transfer delay or a large inference delay. This CAREER project overcomes the limitations of existing approaches by proposing wireless InferNets, a new wireless network architecture that enables collaborative machine learning inference among network nodes on the data transfer path. The successful completion of this CAREER project will promote the understanding of the synergy between distributed inference and networking, and catalyze a paradigm shift of future wireless networks to support emerging applications and services in security, healthcare and other technological domains. The project also contains a significant educational component and provides abundant opportunities to nurture and attract students, especially from underrepresented groups, to engage in computer science and engineering. This CAREER project develops models, algorithms and protocols to realize the core functions of wireless InferNets and address challenges caused by network and device heterogeneity, dynamic and imperfect network states, and multiple user contention for the limited resources via three main research aims. Specifically, it aims at (i) developing practical distributed inference routing algorithms and protocols and conducting theoretical analysis to understand the performance limits; (ii) developing new multi-armed bandit algorithms to perform inference routing with uncertain network information; (iii) developing distributed multi-agent deep reinforcement learning algorithms for inference routing in multi-user wireless InferNets. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project is a collaborative effort that brings together expertise in formal methods, machine learning, computer-aided design, and fabrication of in-memory computing systems. The main goal of the project is to create formal methods that can synthesize neural networks in the memory of the computer and also prove their correctness. The project pursues tasks that include the verification of neural networks accelerated using analog in-memory computing (IMC) and the synthesis of hybrid analog-digital IMC for neural networks using formal methods and machine learning. The project demonstrates these innovations using in-field fabrication of IMC systems. The effort creates new algorithms for enabling the deployment of robust AI models on emerging in-memory hardware technologies that may be more prone to errors than traditional CMOS technologies. The project would also allow the training of neural networks with reduced power consumption. This is particularly important given the larger adoption of AI and the need to train more and more powerful neural networks. The endeavor enables several other contributions to the research community, including enhancing the reliability of neural networks on in-memory circuits, increasing diversity in computer engineering and computer science, and fostering interdisciplinary collaboration across formal methods, machine learning, and hardware design. The project focuses on advancing formal methods to tackle real-world challenges encountered in emerging in-memory computing systems. By leveraging recent innovations in machine learning and formal methods, the project synthesizes crossbars for neural nets using decision diagrams, neural nets, and reinforcement learning. It verifies bidirectional digital IMC circuits before demonstrating such in-memory computing systems through fabrication. This effort expands our understanding of the capabilities and limitations of in-memory computing systems and creates innovations in fields such as in-memory computing, formal methods, and artificial intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Silicon is the cornerstone material for building microelectronics, essential in a wide range of devices from smartphones and personal computers to electric vehicles. However, its inherent material limitations also pose challenges for advancing future computational technologies. As the thickness of silicon decreases to sub-3-nm range, its carriers suffer from significant scattering, leading to dramatic performance degradation. This limitation results in a “silicon-impossible” territory. According to the International Roadmap for Devices and Systems (IRDS), further scaling of silicon technology nodes will reach a plateau at physical channel lengths of 12 nm by 2037. In contrast, the atomically thin body thickness of two-dimensional (2D) semiconductors offers superior immunity to aggressive scaling, presenting a distinct advantage for advancing transistor technology. This program aims to experimentally demonstrate wafer-scale 2D transistors in the “silicon-impossible” territory (sub-5-nm channel length) and investigate their fundamental limits through a combination of experimental and theoretical efforts. Key metrics of the extremely scaled 2D transistors will be benchmarked with the IRDS projections for both high performance and low power applications, as well as the state-of-the-art industrial technology nodes. This program will generate critical knowledge and technologies for next generation of energy efficient computing and a roadmap of further optimization of device structures and material designs. Additionally, this program will provide training opportunities for the future workforce in the semiconductor industry, covering K-12, undergraduate and graduate students. The evolution of silicon complementary metal-oxide-semiconductor (CMOS) transistor technology, driven by Moore’s law scaling, has led to impressive advancements in computing, communications, robotics, and healthcare. To keep up with the shrinking lateral dimensions of transistors, their vertical dimensions must also be reduced in order to prevent short channel effects. Nonetheless, as silicon thickness approaches sub-3-nm scales, the presence of dangling bonds leads to substantial scattering of charge carriers and degrades the carrier mobility in silicon. Therefore, there remains a challenging "silicon-impossible" zone, defined by parameters including a channel body thickness of less than 3 nm and a channel length of less than 5 nm. The atomic thickness of 2D semiconductors, particularly 2D transition metal dichalcogenides, makes them highly suitable for ultimate scaling of transistor technology. Unlike silicon, 2D semiconductors benefit from their van der Waals bonding, which eliminates dangling bonds and keeps carrier mobility immune to thickness scaling. This makes them a promising alternative for advancing Moore’s law into the “silicon-impossible” domain, offering excellent subthreshold performance and ultra-low power consumption. The proposed program aims to use a combined experimental and theoretical approach to systematically investigate the fundamental limits of 2D transistors in the “silicon-impossible” territory, with the following activities: (1) developing a high-throughput, scalable technique for fabricating wafer-scale 2D transistor arrays with physical channel lengths below 5 nm; (2) creating a quantum transport simulator to elucidate the key device physics affecting sub-5-nm 2D transistors and to outline a strategy for further optimization of device designs; and (3) establishing an open-access database to systematically catalog the subthreshold and on-state properties of highly scaled 2D transistors. This program will broadly impact multiple disciplines including electrical engineering, physics and materials science and offer unique outreach and educational opportunities to undergraduate and graduate students and K-12 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 · 2024-10

South Florida is home to the Everglades, which is a mosaic of diverse habitats, ranging from sawgrass sloughs to mangrove forests. It is recognized as a World Heritage site and a vital wetland. However, this fragile ecosystem is threatened by excess phosphorus (P) from upstream watersheds, resulting in impaired water quality, loss of native vegetation and wading bird habitat. Decades of P control programs have not significantly reduced the amount of P delivered to the Everglades, but advanced, materials-based treatment technologies to mitigate P are not yet deployed. This project will identify promising technologies for P mitigation in South Florida by engaging with stakeholders to understand technological needs and perspectives of this unique hydroscape. The work will not only protect the sensitive Everglades ecosystem but will build on the over 35-year history of Everglades restoration efforts. This will lead to the successful stakeholder adoption and implementation of clean water technologies in other systems, such as drinking water and wastewater systems. The project also aligns with the goals of NSF’s Directorate for Technology, Innovation and Partnerships and other philanthropic funders that seek to create mutually beneficial research and technology partnerships among stakeholders across sectoral boundaries. This Treatment Technologies for Phosphorus Mitigation (T2PM) project aims to identify promising treatment technologies for P capture and potential recovery in high-volume, low-concentration hydrologic systems, such as the Stormwater Treatment Areas of South Florida. T2PM will evaluate the potential of using both existing commercially available products and new emerging P-sorbing products and technologies via a combination of laboratory and literature research, and stakeholder engagement. Findings will be shared with key regional stakeholders who, through focus groups and stakeholder meetings, will provide perspectives on and develop ratings of the appropriateness of these technologies for use in the South Florida context. Stakeholder engagement will entail: (i) one-on-one interactions; (ii) group learning sessions; (iii) focus group meetings; (iv) site-visits; (v) a workshop; and (v) leveraging extension services. This process will yield selection criteria designed to provide a thorough and equitable method for the initial screening of novel P capture technologies that might warrant further investigation by NSF-ReDDDoT Phase 2 funding as demonstration projects within South Florida. An iterative stakeholder engagement process will allow for early integration of stakeholders’ perspectives, and their values placed on innovation will drive effective adoption and sustainability of new interventions, thus ensuring the readiness of new materials and technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Achieving widespread decarbonization in the United States requires the development of energy-efficient technologies for separating carbon dioxide (CO2) from natural gas and flue gas produced by fossil fuel combustion. Membrane-based CO2 separations can potentially lower the energy demands and cost of carbon capture compared to other technologies like absorption. However, this necessitates the development of membrane materials that maximize throughput and separation efficiency while remaining robust under variable operating conditions. "Facilitated Transport Membranes" (FTMs), using polymers tailored for specific molecular interactions with CO2, show promise in meeting these goals. Yet, the fundamental transport mechanisms of CO2 in many of these materials are not fully understood, particularly in the presence of water vapor, which is common in many carbon capture applications. This project aims to develop a new modular synthetic strategy for controlling CO2-membrane interactions in FTMs using the versatility of "click chemistry." These polymer membranes will be used to investigate the underlying mechanisms of CO2 selective transport at both microscopic (≤ 1 μm) and macroscopic (~100 μm) scales in the presence and absence of water vapor, utilizing advanced nuclear magnetic resonance (NMR) spectroscopy and other unique experimental tools. The fundamental knowledge gained from this project will enable the strategic design of FTMs for highly efficient CO2 capture from a wide range of gas streams, advancing long-term goals for energy sector decarbonization and promoting sustainability. The research will also be integrated into graduate and undergraduate training and course curricula at the University of Florida. Additionally, new outreach activities aimed at high school teachers across Florida will educate the broader community about how STEM researchers are developing solutions for environmental challenges and global sustainability. This research project aims to design a versatile strategy for functionalizing polymer ionic liquid (PIL) membranes to enable systematic structure-property studies of CO2 transport over different length scales. PIL networks will be formed by copolymerizing polyethylene glycol (PEG)-based monomers with co-monomers containing active ester groups, which can be readily post-functionalized in solution with a wide range of protic PIL ligands (Lewis bases) via a simple click reaction. Gas sorption and permeation measurements will reveal macroscopic gas transport properties across the length scale of the membrane thickness, while advanced NMR spectroscopy tools will quantify gas self-diffusion and dynamics at micrometer and sub-micron length scales. Investigating transport over length scales spanning different orders of magnitude will reveal how CO2-ligand interactions and percolated domains of PIL ligands control diffusion barriers for CO2 transport. In particular, creating interconnected domains of ligands with modest CO2 interactions should increase both CO2 sorption and diffusion, whereas disconnected domains or interactions that are too strong may inhibit CO2 transport. Both macroscopic transport and microscale NMR experiments will then be extended to mixed gas-water vapor mixtures to reveal how sorbed water alters CO2 diffusion rates compared with those of other gases like nitrogen and methane. These findings will reveal how to strategically leverage FTM functionality and architecture to optimize CO2-selective separations of dry and humid gas streams. Achieving the goals of this project will ultimately guide the future development and upscaling of advanced carbon capture membranes, leading to reduced carbon emissions and increased awareness of sustainability challenges in the United States. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Traumatic brain injury (TBI) remains a growing public health concern, with an annual prevalence of about 1.7 million cases and a yearly cost of about $40.6 billion in the United States alone. Despite an extensive body of work on TBI in biomechanics and neuroscience domains, there is still much progress to be made to advance the knowledge of TBI mechanics and associated motor impairments. In particular, fundamental knowledge of how and to what extent mechanical force impacts brain neuronal activity and, in turn, how and to what extent brain impairment affects neck muscle responses remains unknown. Therefore, this Faculty Early Career Development (CAREER) project seeks to develop a breakthrough computational framework that can mimic realistic brain-muscle activation dynamics and support discovering fundamental knowledge about TBI mechanics and associated interventions. This research requires methods and knowledge from various disciplines, including neuroscience, biomechanics, human factors, and control engineering, thus impacting the convergence of engineering and medicine. The project’s synergistic education and outreach activities outline a plan to strengthen the interdisciplinary field of neuro-biomechanics in bioengineering, industrial engineering, mechanical engineering, and chemical engineering through course development, involvement of graduate and undergraduate students in the research activities, and K-12 outreach activities. In addition, the outreach through webinars and video blogging will enhance scientific literacy levels about TBI among broad audiences, including first responders and TBI patients. The investigator’s long-term goal is to discover the fundamental relationship between brain multiphysics and neuromuscular dynamics in order to develop engineering technologies (i.e., helmet technologies, neuroprosthetics, etc.) and therapeutic interventions (e.g., TBI-focused rehabilitation, surgical treatments, etc.) to reduce TBI in sports, workplaces, and daily activities. In pursuit of this vision, this project will provide a Brain-Muscle-Interaction framework, called BMI-frame, a closed-loop framework composed of multiscale brain neuronal models, head-neck finite-element (FE) structures, proportional-integral-derivative (PID) algorithms, and neural network (NN) agents. This objective will be accomplished through three specific tasks: 1) investigate the effects of mechanical impacts on brain neuronal signals, 2) identify NN agents to predict brain and muscle PID gain parameters, and 3) characterize brain and muscle responses to mechanical impacts. Task 1 focuses on developing and validating brain electromechanical models to understand brain neuronal response to various (sub) traumatic impacts. Task 2 will explore novel NN algorithms in order to accurately tune brain signals and individual neck muscle activations with a minimal number of iterations and loop delays. Task 3 focuses on validating the BMI-frame platform by exploring the dynamics of brain-muscle interactions in response to various (sub) traumatic mechanical impacts and TBI conditions. The research is a breakthrough innovation as it transforms brain-muscle interaction dynamics into a mathematically-grounded engineering framework, with the purpose of creating unprecedented scientific knowledge about how (sub) traumatic mechanical impacts cause electromechanical disruptions of brain neuronal dynamics and, in turn, how brain neuronal disruptions affect neck muscle responses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Salt marshes provide important coastal services,for example, habitats for fishery species, buffers against damage due to rising sea levels and storm surges, and pools of soil organic carbon. Restoring coastal marshes has been recently listed as a “fundamental pillar of fighting the climate crisis” by the White House Nature-based Solutions Roadmap. One significant threat to coastal salt marshes is the collapse of ponds, which can have cascading effects on the overall health and resilience of the marsh. The detection of pond collapse is essential for effective conservation and management. Coastal research communities rely on a rapidly growing amount of high-resolution remote sensing imagery from drones, aerial planes, and satellites. Due to the sheer data volume, scientists urgently need automatic tools to analyze the Earth imagery data. This project aims to develop an integrated GeoAI toolset to train deep learning models for marsh feature mapping (channel networks and ponds). The tool also integrates spatial topological analysis and temporal change analysis of mapped marsh features. Results produced from the tool will be applied to geomorphology research through interdisciplinary collaboration. Educational activities include curriculum development, mentoring a broad group of high school students in data science seminars, as well as a year-long project for a selected number of high school students for the regional Science Fair competition. The main goal of the project is to build GeoAI toolset to generate a high-resolution (0.5m to 1m) marsh feature database (channel networks and ponds) for the most vulnerable estuaries across the U.S. coast. First, such a high-resolution marsh feature database enables the detection of locations and drivers of pond collapse. Second, it enables new geomorphology research opportunities by capturing the ability of small-scale (few meters) tidal networks to drain ponds, feed sediments, and restore vegetation. Third, the GeoAI toolset provides a weakly supervised learning framework to automatically train deep learning models from imperfect vector labels. Finally, the open-source tool will be coded in a general and transferrable fashion so that images from other estuarine regions can be used as input for the tool, enabling the same analysis to be performed. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Geosciences Directorate’s Division of Research, Innovation, Synergies, and Education and 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 · 2024-10

Artificial Intelligence of Things (AIoT) and its applications are of paramount importance in the ongoing fourth industrial revolution, which is marked by the seamless fusion of physical and digital systems. This project aims to revolutionize Artificial Intelligence (AI) education by establishing a remotely accessible AIoT infrastructure, making state-of-the-art labs available to a broader student audience for immersive learning of AI with hands-on experiences. This project seeks to address several fundamental issues in AI education and workforce development. First, it will address the imbalance in AI education, which focuses on primarily building software skills, by integrating exposure to essential hardware components for a comprehensive understanding of the field. Second, it will address the resource constraints faced by many educational institutions, which limit their ability to offer state-of-the-art, hands-on AI learning experiences. Third, it will promote access for underserved minority groups, fostering diversity and innovation in the field. Through collaboration and resource sharing among participating universities, University of Florida, North Carolina A&T, and Prairie View A&M University, this project will directly impact over 30 instructors and 1,500 undergraduate students across institutions. Additionally, the project will leverage partnerships with industry leaders to align educational content with industry needs and standards, enhancing the relevance and applicability of AIoT education. From a broader perspective, the project will advance the AI field by developing foundational content through an integrated approach that highlights the interplay among AIoT components. It will also introduce immersive learning strategies to enhance student engagement and understanding, making AI education more inclusive and accessible. This inclusivity fosters diversity, creating a talent pool with varied perspectives that can drive innovation in the industry. Through the deployment of an innovative hardware-in-the-loop system, the project focuses on developing an immersive AIoT learning platform, which will be remotely accessible for students across institutes. This platform will include well-integrated modules on AIoT fundamentals, including security, connectivity, sensor design, and machine learning. The research will be guided by key questions aimed at enhancing AIoT technology, expanding its accessibility to a diverse student population, and investigating the educational impact of immersive technology in AIoT learning. To evaluate the impact of the immersive learning environment on student outcomes, relevant data will be collected and analyzed. Usability and feasibility studies will initially test the modules with qualitative analysis assessing their impact on learning and engagement. During classroom integration, student activity data will be analyzed using learning analytics and deep learning techniques to identify common challenges. Finally, the impact of the modules will be evaluated by comparing baseline data from unmodified courses with data from those incorporating AIoT modules. Paired t-tests will examine pre- and post-learning differences, while qualitative analysis of interview transcripts will offer supplementary insights. The project will help address workforce shortages, promote technological advancements, and help maintain global competitiveness in the evolving AI landscape by preparing a new generation of AI professionals. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Emerging polymer technologies include biogels, purification membranes, recyclable plastics, and advanced composites. However, progress in these areas is hindered by insufficient tooling for preparing and analyzing polymer simulations. This work leverages expertise in high-throughput computing, polymer physics, reaction dynamics, and scientific software development to enable efficient, reproducible modeling across multiple simulation engines and hardware architectures. The scientific and software needs of universities, national labs (NREL, LLNL, INL, NIST, AFRL), industry (Boeing, Bristol Myers Squibb) and international consortia (CECAM, FairMAT) are incorporated to maximize impact. The Multiscale Polymer Toolkit (MuPT) enables reproducible and extensible computational research on reacting polymer materials from Angström to micron length scales. MuPT is an expanding suite of Python software libraries and community recipes, built on top of an ecosystem of previously funded open-source tools. The effort pairs Open Molecular Software Foundation software developers with domain experts to develop software and tutorials in collaboration with application scientists in the community. Findability and accessibility are accomplished through conda-forge deployment and public workshops. MuPT deliverables include: (a) A multiscale, internal software representation for polymers that enables data conversion between major simulation engines at the same resolution scale, and tools for conversion between coarse-grained and higher resolution representations; (b) An interface for this representation that allows researchers to plug in existing software tools for polymer parameterization, building, and crosslinking; (c) A workflow interface that allows linking of existing software tools and enables users to programmatically generate simulation inputs by specifying the simulation engine, chemistries, reaction models, and molecular representations; (d) A searchable repository of community-vetted polymer simulation workflows, initially seeded and maintained by the principal investigators of the grant; (e) Documentation for best practice in polymer modeling with examples using MuPT libraries; (f) Improved materials and recommendations for training research software engineers. 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 · 2024-10

This project is motivated by the need to efficiently execute complex queries on massive databases in a way that minimizes the use of communication resources while preserving the privacy of the entity that initiated the query. Such queries are functions of the data points that are stored at remote servers; for example, bi-linear operations are widely used fundamental primitives for building the complex queries that support on-line big-data analytics and data mining procedures. In scenarios such as mobile-edge computing, it is too resource-consuming to download locally all the input variables in order to compute the desired output value. Instead, it is desirable to directly download the result of the desired output function, which should also be kept private. This project develops a principled and holistic framework for the problem of privately retrieving, at distributed cache-aided nodes, the output of functions based on both data that is locally computed and data that is received from multiple servers. This problem is at the intersection of areas that individually have received significant attention lately, namely, distributed coded caching and private information retrieval. This project aims to significantly advance the state-of-the-art of private function retrieval in distributed settings from both an information theory and an algorithm design perspective, thus establishing a foundation of private caching, computing and communication. The project also features a rich educational component. The novel findings from this project will be incorporated into the education offerings, in both undergraduate and graduate levels, at the three collaborating institutions. The project objectives are organized in three main research thrusts: (1) design optimal coded caching schemes for user-private function retrieval; (2) motivated by distributed settings in which a user may also be a sender, devise optimal server-private function retrieval strategies; and (3) overcome complexity bottlenecks in practical distributed computing systems with server- and/or user-privacy. The designed codes and algorithms will be implemented on Amazon EC2 and POWDER (5G platform) to provide a proof-of-concept that the proposed solutions have a practical impact at scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Modern embedded systems rely on hardware as the root of trust and are utilized across industries such as healthcare, transportation, and communication. However, the increase in use and complexity of these systems has led to a rise in security-critical hardware vulnerabilities that can be exploited by cross-layer attacks, disrupting traditional threat models that assume either hardware-only or software-only adversaries. These attacks do not only threaten the reputation of companies and cause monetary damage, but the attacks also undermine the safety, security, and resilience of critical infrastructure in the nation and society at large. Existing hardware validation and verification techniques neither scale to large designs nor achieve sufficient coverage. This project aims to improve scalability and effectiveness of hardware security verification. The results are disseminated through organizing a new generation of Hack@EVENT hardware security competitions and outreach activities at venues such as Grace Hopper Celebration of Computing. The team uses the developed techniques to empower validation and verification efforts both in educational and industrial contexts. Fuzzing, an automated input generation technique, has recently been adapted to hardware security validation. Although fuzzing achieves better scalability compared to traditional validation techniques, existing hardware fuzzing approaches do not achieve sufficient design coverage to provide high assurance. The team develops novel fuzzing techniques through the orchestration of formal verification, symbolic execution, and static analysis in providing guidance for effective input state space exploration. The team also models the patterns of various hardware vulnerability types such as information leakage, denial of service, and micro-architectural vulnerabilities to support fuzzing without relying on golden models or property specifications. This project further automates hardware bug injection. The team leverages their experience in organizing and participating in hardware security competitions and integrating large language model with fuzzing to facilitate bug injection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

Project Summary/Abstract Medical marijuana is rapidly becoming a popular treatment for chronic pain among older adults despite limited evidence on its benefits and risks for this vulnerable population. Our research indicates the presence of gene interaction modules for the pain-endocannabinoid system, and differing pain, psychological, and behavioral responses to medical marijuana treatment. Additionally, we have shown that measures of allostatic load, indicating individual health status or whole person health, are more informative than chronic pain severity in understanding the heterogeneity of health outcomes in individuals with chronic musculoskeletal pain.Our interdisciplinary team has expertise on the key proposed areas including: pain pathology and assessment, allostatic load, inflammation, machine learning applied to genomics, patient subgroup characterization, and gene expression analysis. The overall goal of the study is to delineate multi-level factors contributing to heterogeneous responses to medical marijuana treatment involving: (a) genetic, through targeted gene expression; (b) sociodemographic and (c) psychological/behavioral including consideration for comorbidities and medical marijuana treatment regimen confirmed by blood tests. We will extend the scientific merit of a recently funded prospective cohort study (R01 AG071729-01A) and aim to study the 3-month effects of medical marijuana among older adults with chronic musculoskeletal pain. A total of 80 patients who are seeking to initiate medical marijuana, and 40 who are not, will provide additional data at baseline and 3 months. Importantly, we will study the multi-level individual factors influencing the response to medical marijuana in older adults with chronic musculoskeletal pain via three Specific Aims: To identify gene modules associated (over- or under-expression) to medical marijuana treatment and change in chronic pain; AIM 2: To detect associations between changes in chronic pain severity and changes in allostatic load; AIM 3: To develop a casual, personalized medicine prediction model that can help identify patients who are more likely to benefit from medical marijuana treatment. Funding of this project will support a much-needed multi-level analysis of individual factors contributing to the heterogeneous responses to medical marijuana treatment in adults with chronic musculoskeletal pain. Findings from this study will provide valuable data on the key genetic, sociodemographic, psychological/behavioral factors predictive of response to medical marijuana treatment and ultimately inform and improve patient care through a personalized medicine approach.

NIH Research Projects · FY 2025 · 2024-09

Abstract Chronic limb threatening ischemia (CLTI) is the most severe form of the atherosclerotic disease peripheral arterial disease (PAD). Unfortunately, there is only one approved therapy, Cilostazol, a mild vasodilatory agent, that modestly improves limb function in PAD patients, but is not widely used in CLTI patients. For CLTI patients, surgical interventions are commonly employed, but eventual limb amputation is still common evidenced by the ~45% amputation rate in the BEST-CLI trial. Several angiogenic therapies have been unsuccessful in clinical trials, suggesting that while improving limb blood flow should remain a primary treatment focus CLTI limb pathobiology involves pathological changes to other non-vascular cell types within the affected limb. In this regard, the replacement of skeletal muscle with intramuscular adipose tissue (IMAT) and fibrotic tissue is prominent in the CLTI population. However, it is not currently known how IMAT and fibrosis impact limb function in CLTI or whether therapeutic targeting of IMAT/fibrosis is a viable option. In this predoctoral fellowship proposal, I will test the hypothesis that IMAT contributes to the CLTI pathobiology by negatively impacting vessel growth and promoting muscle dysfunction. To test this hypothesis, I will employ rigorous genetic and pharmacologic approaches that either increase or decrease IMAT in mice subjected to femoral artery ligation (FAL). Considering that IMAT and fibrosis is believed to be formed by the differentiation of fibro-adipogenic progenitor cells (FAPs) that reside within the limb muscles, I will also perform experiments to define what drives this adipogenic conversion of FAPs in the CLTI condition. These experiments will leverage temporal single nuclei/cell RNA sequencing in mice with CLTI, as well as primary FAP cultures in hypoxia/normoxia to identify intercellular signaling molecules that drives FAP differentiation to IMAT and to define the secretome of FAPs which will allow me to explore how FAPs communicate with other cells in the CLTI limb. A detailed training program with strong mentorship that involves specific research skill enhancement in molecular genetics, stem- cell biology, and multi-omics, has been developed. I will receive additional career mentoring involving grant/manuscript writing, presentation skills, and professional development opportunities. Completion of this proposal will result in exceptional scientific and professional training which will provide a strong foundation for my career goals.

NIH Research Projects · FY 2024 · 2024-09

PROJECT ABSTRACT Read aligners first build an index from one or more reference genome(s) and subsequently use it to find and extend matched subsequences between sequence reads and the reference(s). The bottleneck of using these read aligners to index thousands of human reference genomes is the space and time needed for construct and store the index. Hence, in the case of the human genome, it is common to restrict interest to alignment of the standard reference genome, i.e., GChr38. Yet, the absence of diversity in this single reference genome can cause substandard results in downstream analysis, impacting the ability to identify and study genetic variation. To address the shortcomings associated with using a single reference genome, the concept of a pange- nomics reference genome has been introduced and adopted. For example, Giraffe, VG, and Moni all aim to index a population of genomes in a manner that enables read alignment. Although, these pangenomics aligners have been shown to improve on the accuracy over standard read aligners (e.g., BWA and Bowtie) there exists several challenges that prevent these methods from being used in practice for downstream anal- ysis, such as variant calling. The goal of this proposal is to develop algorithms to address these challenges and fully enable pangenomics alignment. In particular, we will create methods for selecting (from a large population) a subset genomes that will achieve the most accurate alignment results, develop a pangenomics scoring scheme that will enable the alignments from a pangenome to be attained, and disseminate our methods in a user-friendly manner that enables automated updates.

NIH Research Projects · FY 2026 · 2024-09

PROJECT SUMMARY Pharmacogenetic (PGx) testing may be particularly beneficial in patients who visit the emergency department (ED) because most presenting patients are experiencing significant problems related to their disease and/or associated medication(s). Moreover, most of the patients visiting the ED will either be admitted to the hospital or receive follow-up outpatient care in the near future. Our long-term goal is to identify patient populations where precision medicine can improve clinical outcomes and reduce healthcare costs. The overall objective of this application is to assess the feasibility, effectiveness, and economic impact of clinically implementing PGx testing, with appropriate prescribing decision support, in the ED setting. The central hypothesis is that providing such an intervention will reduce ED return visits and thus healthcare costs. The rationale for the proposed research is that PGx results, with appropriate prescribing decision support, should empower prescribers to further individualize their medication prescribing, which should decrease the rate of medication ineffectiveness and side effects. We plan to test the central hypothesis and accomplish the overall objective of this application by pursuing three specific aims. The first aim is to expand PGx-based decision support resources for clinicians in the emergency and post-emergency settings. We will accomplish this aim by expanding PGx decision support to include drug-drug-gene interactions, pharmacist medication reviews, additional gene-drug pairs, and expanding prescribing recommendations to outside the health system will increase clinical uptake of precision medicine. The second aim is to determine the feasibility and effectiveness of implementing PGx testing in high-risk patients who frequently visit the emergency department. We will accomplish this aim by completing a randomized, pragmatic clinical trial comparing high-risk ED patients receiving PGx testing to those receiving usual care. We will assess ED return rates, hospitalization rates, as well as key implementation metrics. The third aim is to quantify the cost-effectiveness and net benefit associated with implementation of PGx testing given various levels of emergency department return rate reduction. We will accomplish this aim with three analyses first estimating the cost-effectiveness of this approach and then comparing the cost-benefit using cost-per-(averted) revisit as the main outcome and comparing intervention to usual care. The proposed research is significant because it should help to identify methods to improve care while reducing costs and address multiple established barriers to PGx implementation. The proposed research is innovative because it will research an understudied, yet at-risk patient population. Ultimately, we expect to have important effectiveness, feasibility, and economic data regarding expanding precision medicine efforts in the ED setting. These results should have a positive impact by informing future clinical implementation efforts and larger multi-site clinical trials, ideally reducing both cost and medication-related healthcare disparities.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Excess redox-active iron, and consequent oxidative stress, contribute to the morbidity and mortality associated with β-thalassemia. Iron overload develops due to low hepcidin and excessive intestinal iron absorption, and from blood transfusions. Anemia and tissue hypoxia, due to ineffective erythropoiesis, also typify β-thalassemia. Pregnancy in β-thalassemia is becoming more common but is considered high risk. The relative contributions of iron overload/oxidative stress and anemia/hypoxia to adverse fetal outcomes are, however, unknown. Given this gap in clinical knowledge, investigation in this area of scientific pursuit is warranted. We utilized a pre-clinical model of β-thalassemia, Th3/+ mice, to test the hypothesis that thalassemic pregnancy disrupts maternal, placental, and fetal oxygen and iron balance. Indeed, several pathophysiological perturbations were observed in Th3/+ dams (iron overload, anemia), placentas (placentomegaly, iron loading, hypoxia), and fetuses (growth restriction, iron loading, oxidative stress, hypoxia and global hypomethylation of DNA in brain), all as compared to WT pregnancies. Notably, abnormalities were seen even in WT fetuses (and pups) from Th3/+ dams. Elucidating how these in utero physiological disturbances impact fetal and postnatal development is an overarching goal of this proposal. In three specific aims, we will expand our experimental analyses of thalassemic pregnancies to define molecular pathways mediating adverse fetal and postnatal effects. In Aim 1, we will test the hypothesis that iron loading exacerbates pathological fetal outcomes in Th3/+ dams. We postulate that high fetal iron increases the risk for oxidative stress, which can cause molecular, cellular, tissue and organ damage. The rationale is that Th3/+ dams have elevated plasma iron throughout pregnancy, which contributes to iron loading of Th3/+ and WT fetuses. We also predict that exposure of developing tissues to oxidative stress in utero increases the risk for postnatal pathologies. In Aim 2, the hypothesis that hypoxia exacerbates pathological fetal outcomes in Th3/+ dams will be tested. We postulate that hypoxia plays an important role in the development of postnatal pathologies in mice born to Th3/+ dams, either by itself or in synergy with iron loading/oxidative stress. The rationale is that ineffective erythropoiesis and anemia lead to fetal tissue hypoxia, which would be predicted to lead to a shift in energy metabolism and cause other metabolic disturbances. Aim 3 will test the hypothesis that lowering plasma iron in Th3/+ dams will prevent fetal iron overload. The rationale is that hyperferremia in Th3/+ dams precipitates fetal iron loading. The approach entails in vivo inhibition of intestinal iron absorption and iron release from stores. The overarching goal of this investigation is to define the pathophysiological perturbations associated with thalassemic pregnancy, which may reveal novel molecular pathways that could be targeted in the future to improve outcomes.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Historically, osteoarthritis (OA) pathology is defined by the breakdown of articular cartilage. While cartilage loss remains a hallmark of OA, the pathology of OA is now widely recognized to be a disease of the entire joint, including cartilage, bone, ligaments, menisci, and synovium. This definition of OA pathology provides a whole joint perspective; however, there is another problem with this definition of OA – the discordance between OA pathology and symptoms. Simply put, more degeneration of the joint does not necessarily mean more pain. This issue markedly complicates the development of pain-relieving therapies for OA, as the collection of all people with joint degeneration is far larger than the people with OA pathology and painful symptoms. Then, within the collection of people with OA pathology and symptoms, the etiology of OA is typically unknown and joint pathology tends to be a poor predictor of symptomatic progression. Thus, when focusing on the development of therapies for OA pain, the question becomes: Is a model of OA pathology sufficient to model the heterogeneity and complexity of OA pain? Clearly, disease models are needed for drug discovery, mechanistic testing, and the translation of new therapies from the laboratory to the clinic. For OA pain, a good model should also reflect the heterogeneity of the clinical OA pain experience described above. Thus, we propose that the best model of OA pain is a veterinary clinical population that replicates the breadth of OA cases and heterogeneity of OA symptoms. In fact, equine OA patients capture the years-long cascade of OA disease progression, model the heterogeneity of human OA pain reports, and provide unique behavioral parallels for the deep phenotyping assays currently being used in humans. However, a critical technological gap needs to be closed for studies in equine OA populations. While detailed behavioral protocols to evaluate pain-related experiences have been developed and validated for human patient populations, these assessments of behavioral assay reliability and validity have never been conducted for the horse. This is not to say that pain has not been studied in horses; gait analysis, sensory function, activity, and heart rate monitoring (among other assays) have all been conducted in horses with OA. However, pain-related behaviors are typically studied in isolation, and these assays are rarely assessed for their reliability or validity across studies. A goal of RFA-NS-22-070 is to recapitulate the behavioral aspects of human pain disorders in large animal models, and thereby provide well-validated measures that facilitate the development of non-opioid analgesic therapies with little or no addiction liability in the future. In response to this program, we propose to rigorously test the reliability and validity of pain-related behavioral metrics in the horse through psychometric analyses, including assessments of quantitative sensory tests (Aim 1), locomotion (Aim 2), and activity and spontaneous behavior (Aim 3). In doing so, we aim to close gaps between pain assessments in the horse and the human, while providing a thorough assessment of behavioral metrics that are translatable across the translational pipeline for emerging pain therapies.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY NIH RFA AG-24-041 requests applications to determine the neural mechanisms that underlie the association between gait and cognition in aging and Alzheimer's disease (AD) and Alzheimer's disease-related dementias (ADRD). Mobility declines precede mild cognitive impairment (MCI) in most older adults. Understanding the neural control of gait in these individuals will inform the use of gait changes as an early biomarker for AD/ADRD and lead to new, early interventions. Our well-composed, cross disciplinary team has the requisite expertise in aging, cognitive decline, sensorimotor neuroscience, and spatial navigation to address this topic. We propose a novel and transformational perspective that the relationship between mobility disability and MCI lies in vestibular and hippocampal contributions to gait and cognition. Vestibular function declines with aging and is even more impacted in individuals with MCI and Alzheimer's disease (AD). These declines are linked to falls in both typical aging and AD, a major health concern with often devastating consequences in aging. It is well known that vestibular inputs project to the brainstem, cerebellum, and vestibular cortex. What is less understood are vestibular projections to the hippocampus, primary motor cortex, and premotor areas. This is a critical knowledge gap, as the hippocampus and other temporal lobe structures play a key role in spatial navigation, a behavior which also declines in MCI and AD. The hippocampus shows rapid and early atrophy in AD. Here, we test the novel hypothesis that vestibular declines impact walking in those with subjective cognitive decline coupled with a family history of AD (placing them at high risk for AD). We propose that spatial navigation performance during walking is more impaired in this population due to simultaneous cognitive and motor demands on declining vestibular inputs. Aim 1 is to determine whether brain structure and network segregation (how independently a network functions) of vestibular-motor and vestibular-hippocampal brain regions are reduced in individuals with subjective cognitive decline. We will assess whether these brain metrics are linked to declines in mobility and spatial navigation. Under Aim 2, we use cutting edge, mobile EEG approaches to identify spectral power differences between those with subjective cognitive decline and typical aging during actual walking and spatial navigation. In Aim 3, we will determine whether vestibular network segregation can be restored with bilateral vestibular cortical transcranial direct current stimulation. We will further determine whether blood biomarkers for phosphorylated tau and amyloid beta mediate brain-behavior associations in Aim 4. The results will lead to a greater understanding of the neural control of gait and cognitive-motor interactions in subjective cognitive decline, providing insights for new, early biomarkers and interventions for impending declines.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT This K23 application is to support the research and career development of Dr. Dayane Oliveira by providing her with an intensive mentored experience in order for her to complete the transition to an independent clinician- scientist with a patient-centered research program focused on an unexplored area of designing and implementing novel dental biomaterials using red light in their application. Light-cured materials revolutionized dentistry as they allowed to control the setting of the materials in a timely manner simply upon light exposure. The use of light-cured materials in dentistry was made possible by adding photoinitiators to the composition of dental materials. Although blue light has been routinely used to cure dental restorative materials for over 50 years, there are still potential risks to dental patients. These risks include gingiva burn/recession and pulp inflammation that can lead to necrosis in more severe situations caused/induced by the heat generated by this short wavelength. As well as the direct effects on the gingival and pulpal cells, including the induction of irreversible reactive oxygen species levels imbalance, damage to the mitochondrial DNA, and collagen degradation. On the other hand, long wavelengths (such as red light) are known to induce less heat and have opposing effects on cellular function, reducing inflammation and increasing cell proliferation. Although the blue- light hazards in dentistry are well known, little can be done as to date as there were no photoinitiators capable of absorbing longer wavelengths. Our group has overcome this hurdle by synthesizing a dental photoinitiator activated by red light, thus opening the door for red light to be used in restorative dental procedures. Our in vitro preliminary data demonstrate the positive effects of red light on temperature during the curing process as well as on fibroblast and odontoblast viability and their initial signaling responses. Thus, as the next step in filling this clinical need, we here propose to conduct an exploratory Phase 1 medical device study combined with a further ex vivo mechanistic study as the next logical steps in translating this improved clinical care from laboratory findings to clinical practice. Our central hypothesis is that longer wavelengths will mitigate pulpal and gingival damage during dental restorative procedures. The results of this work will lay the groundwork to support a future medical device Phase 2 study application in order to achieve the long-term goal of using red light as a new strategy for light-curing in Dentistry. Thus, ensuring a safer and more effective treatment strategy to improve oral health. Dr. Oliveira has established a multi-disciplinary mentoring team to provide expertise in translational research training and mentorship in her career development. A training plan including didactic, hands-on, and career development experiences has been put forth to enable this transition together with this strong research plan.

NIH Research Projects · FY 2024 · 2024-09

ABSTRACT Dental caries, the most common microbial disease, is caused by overgrowth of acidogenic and aciduric bacteria including Streptococcus mutans. Childhood caries incidence in the U.S. is high and there is a clear imperative to better understand caries pathogenesis. Cariogenic organisms thrive in biofilm environments. Amyloid was first identified in the context of pathology but does not always represent a protein mis-folding pathway. Functional amyloid is also recognized. Amyloid aggregates are evolutionarily conserved cross -sheet quaternary structures with common biophysical properties enabling their detection and study. Multiple microorganisms are now known to produce functional amyloids within biofilm environments. Our group was the first to discover Streptococcus mutans amyloids. We have now identified four amyloid-forming proteins in this bacterium. Three of these, P1 (AgI/II), WapA, and Cnm are sortase-localized adhesins whose extracellular truncation derivatives are amyloidogenic. The previously unknown fourth protein, Smu_63, serves as a negative regulator of biofilm cell density and genetic competence. We have provided extensive tertiary and quaternary structural characterization of adhesin P1 and structural characterization of other proteins is in progress. We have provided definitive X-ray fiber diffraction evidence of a classical stacked -sheet amyloid structure for S. mutans amyloids. Furthermore, our work contributes to a new paradigm for multiple streptococcal and staphylococcal amyloids. Naturally-occurring truncation products play two key roles within these organisms' biofilm life cycles. First by promoting adherence to cognate ligands in their monomeric forms via quaternary interactions with the parent adhesins linked to the cell surface, and second by facilitating detachment of biofilm cells and extracellular matrix components from aging biofilms in their amyloid form. The left-handed Z-configuration of extracellular DNA within biofilms was recently associated with biofilm stability whereas right-handed B-DNA disrupted extant biofilms. The amyloid, but not monomeric form of neuropathologic A, drives conversion of Z-DNA to B-DNA. Cardiolipin-rich mitochondrial membranes modulate amyloidogeneis of -synuclein and Htt involved in Parkinson's and Huntingtin Diseases. We have identified cardiolipin as a prevalent lipid in S. mutans cytoplasmic membranes and extracellular membrane vesicles. In this renewal application we will explore relationships between S. mutans amyloid-forming proteins and B- and Z-forms of DNA in vitro and in vivo within adherent and detaching biofilms (Aim 1), determine the impact of membrane lipid composition on S. mutans amyloid formation within aging biofilms and assess interactions of amyloidogenic proteins with specific lipids of interest (Aim 2), and continue to use state of the art methods including solution and solid-state NMR to identify and characterize structural transitions reflective of monomer to amyloid conversion and determine if amyloid signatures for each protein are impacted by exposure to different DNA configurations, lipids, or other amyloidogenic proteins (Aim 3).

NSF Awards · FY 2024 · 2024-09

Nearly one-third of the Earth's land surface is covered by forests, which host the majority of terrestrial biodiversity. Accurate mapping and monitoring of forests across large regions and over time is critical for mitigating climate and natural hazards, managing natural resources and protection of vital ecosystems. While traditional ground-based measurements of plant species and size provide the most accurate data on forest structure and above ground biomass, these methods become impractical when covering large areas with high-frequency repeat cycles. Airborne and Space-based remote sensing techniques provide a timely and cost-effective way to assess forest structure and biomass on regional to global scales. Satellite missions from NASA and ESA have sensors that gather data with significantly more frequent repeat cycles compared to in situ measurements or aerial surveys. While these satellite missions offer global coverage, some provide only sparse data on forest structure and need to be combined with other data sources for producing comprehensive and accurate wall-to-wall maps. There is a lack of efficient frameworks that utilize multi-source remote sensing data to produce wall-to-wall forest structure or above ground biomass at temporal and spatial scales necessary for effective forest management or use in hazard mitigation and monitoring applications. Without significant improvements to existing methodologies and looking beyond traditional data sources, efficient and accurate monitoring of forest structure and above ground biomass will remain limited. OpenForest4D will allow a wide range of users to generate on-demand and up-to-date research-grade forest structure and above ground biomass estimates across a range of timescales. This will be achieved by applying novel statistical models and artificial intelligence methodologies on a fusion of multi-source remote sensing data from ground, airborne and spaceborne platforms. Providing these cyberinfrastructure services through easily accessible interactive web-based interfaces, along with educational resources focused on the underlying domain science, will facilitate transformative research in forest sciences and ecology and encourage broad community participation. OpenForest4D's web-based educational resources, published curriculum materials, and live webinars will help develop a diverse, globally competitive STEM workforce. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Biological Infrastructure within the Directorate for Biological Sciences and NSF's National Discovery Cloud for Climate initiative. 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

Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce, a matter of national security, by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse Florida, Kansas, and Texas high schools. This project will leverage the partnership with the Scientist for Every Florida School network and nurture new relationships with industry partners. The goal of this project is to engage about 500 high-school students and approximately 25 teachers from under-resourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow. This project will benefit society with its timely and accessible high-school curriculum that integrates Computer Science and Engineering using the rich context of microelectronics and AI. The curriculum will be accessible because it has no prerequisites for programming or hardware knowledge. Every module is centered around a real-world application of microelectronics and AI with direct implications for improving the quality of life in local communities, making learning relevant and place-based. All course materials and resources will be disseminated as open source via the platforms popular among K-12 stakeholders, broadening access and inspiring the next generation of AI practitioners. The focus of this design-based implementation research program is to conduct a systematic inquiry into the effective conditions for designing and integrating curricula and technologies that foster engineering identity development and conceptual understanding of AI in embedded systems as an important trend in engineering. To this end, the research is informed by both qualitative research questions (How are the altruism informed activities perceived and used by students?) and quantitative questions (What are the quantifiable impacts of this approach on students’ motivation and conceptions of edge AI and microelectronics?) The research plan will employ a concurrent triangulation mixed-method research design, incorporating phenomenology, comparative case studies, and mixed-effects modeling. Specifically, the researchers will conduct classroom observations, interviews with students, teachers, and parents or caregivers, surveys, and learning tests to examine the uses and effects of the proposed approach in high school classrooms. This research will contribute new data for building theories on a) altruism as a motivation framework for supporting engineering education, and b) negotiation of engineering identities when engaging students in community-relevant AI and microelectronics projects. This Design and Development project is funded by the Discovery Research preK-12 (DRK-12) program, 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. 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.