Iowa State University

NSF Awards · FY 2025 · 2025-09

Among cardiovascular diseases, ischemic heart disease remains a leading cause of mortality worldwide. While revascularization shows promise as an effective therapeutic method, the large patient variability in genetics, comorbidities, and response to growth factors increases the complexity of standardized regenerative therapies. This project focuses on developing a novel digital twin of blood vessel growth after cardiac injury, based on genetic information and live imaging data. This will make it possible to reverse engineer the precise genetic interventions needed to produce the desired vasculature, as well as to safely test and improve gene-editing techniques in silico. The project will have broad societal and educational applications. All software packages will be made open-source, and a web interface will be created to help in clinical settings. Immersive educational tools for students will be developed to visualize 3D simulations of vascular growth in partnership with Iowa State’s Virtual Reality Center. The project’s integration of mathematics, gene editing, and computational modeling will help train a new generation of scientists at the nexus of mathematics and medicine. This project develops a novel multiscale digital twin framework to predict and control blood vessel growth by integrating molecular signaling dynamics, cellular migration behavior, sprouting patterns, and tissue-level growth and remodeling. The research will develop (1) a multiscale molecular-to-cellular modeling framework for vascular sprouting and remodeling that integrates VEGF and Notch signaling cascades to predict the biophysical behaviors of endothelial tip and stalk cells; (2) a novel machine learning architecture for procedural volumetric T-spline models of vascular networks from 2D sprouting prediction and couple elastic deformation of tissues with growth to capture blood vessel growth; (3) a novel applied analysis framework to prevent singularity formation in the chemotaxis equations and steer angiogenesis via PDE-based optimization; and (4) a model to quantify uncertainties at each length-scale. A closed-loop control scheme uses real-time imaging feedback to guide CRISPR-based gene edits, dynamically refining the model and therapeutic interventions. The validated model will be used to recommend gene-edits (to VEGF, Notch, related pathways) that improve vascular regeneration outcomes in clinical settings such as post-infarct cardiac repair. This project is jointly funded by the Division of Mathematical Sciences and the CBET Engineering of Biomedical Systems program. 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 2025 · 2025-09

Growth of renewable energy leads to new challenges for electric power grid planning and operation. Many renewable energy resources, such as solar and wind, heavily depend on the weather conditions that are inherently uncertain. Such uncertainty is usually revealed progressively over time. Consequently, the grid planning and operation decisions need be adjusted accordingly across multiple stages to achieve optimal efficiency. The multistage decision structure calls for study on multistage grid optimization algorithms that can accommodate the discrete decisions, such as battery charging versus discharging decisions, and scale well with the number of renewable resources, which can go up to tens of thousands. Moreover, several major tripping and disturbance incidences in the past decade have underscored the heightened stability concerns of a power grid with high renewable penetration. In contrast to conventional thermal generators that have large rotating masses to stabilize themselves, renewable resources are typically power electronics-interfaced resources, which lead to lower system inertia, faster grid dynamics, more frequent disturbances, and greater control difficulty. Hence, it is increasingly essential to integrate stability considerations into grid optimization algorithms to enhance reliable power system operation. This research will include open-source implementations of the algorithms developed, which can provide a computational infrastructure and benchmark for assessing long-term energy integration plans, or for evaluating the daily operational efficiency and reliability of power grids. To address these critical challenges of uncertainty and stability, this project aims to develop novel dynamic grid optimization algorithms and modeling tools to effectively accommodate high penetration of renewable energy and ensure reliable grid operation. The first part of this project is focused on a class of algorithms, called stochastic dual dynamic programming, for multistage stochastic optimization models. The investigators will fundamentally advance these algorithms to handle both continuous and discrete grid decisions effectively, and to enable better statistical guarantees by exploiting the structure of grid optimization with renewable uncertainty. The second part of this project plans to directly integrate stability considerations into the objective function and constraints of grid optimization, establishing a framework of stability-augmented grid optimization. Such framework enhances conventional grid planning and operation decisions to be stability-informed and optimizes both the economic efficiency and dynamic stability performance. 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 2025 · 2025-09

This I-Corps project is based on the development of key additives that are compostable and turn brittle, plant-based plastics into durable materials suitable for everyday products. Currently, unmodified bioplastics like polylactic acid (PLA) break on impact, limiting use despite global demand for sustainable packaging. Each year, more than one million tons of PLA are produced, however, to improve impact strength, it must be blended with petroleum-derived additives, which negates compostability. This technology addresses the challenge by raising PLA’s impact strength by an order of magnitude while maintaining industrial compostable certification. Also, the additive may be supplied as pellets, allowing compounders, film converters, and molders to create tough, monolayer parts without changing equipment. The technology uses glycerol, which is a low-cost by-product of U.S. biodiesel, as its sole feedstock, so it may potentially displace non-degradable modifiers, streamline manufacturing, and enable new markets for bioplastics. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of compostable core-shell particles (CCSPs) as biodegradable impact modifiers for polymer composites. The technology focuses on scalable monomer synthesis, polymerization, and integration of CCSPs into high-dosage master batches, ensuring compatibility with existing processing techniques like extrusion and compounding. The CCSPs are synthesized from glycerol-ketal acrylate monomers and the technology employs seed-fed semi-batch emulsion polymerization to produce 200–350 nm particles with an ester cross-linked rubbery core and an acrylic shell that bonds seamlessly with the bioplastic matrix. At modest loading, CCSP-modified PLA exceeds 200 J m⁻¹ Izod impact strength, which is ten times unmodified PLA strength, without sacrificing compostability. Hydrolysis studies show 90% mass loss within ten weeks under accelerated compost conditions, confirming rapid end-of-life degradation. The technology addresses key limitations of bioplastics: brittleness and limited durability. Compostable CCSPs may offer a sustainable alternative to conventional impact modifiers, enabling industries to enhance the mechanical properties of biodegradable plastics without compromising environmental responsibility. 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 2025 · 2025-09

This project will study the puzzling behavior of a type of star called a white dwarf. These are dense remnants of stars like our Sun. Some white dwarfs pulse with light, getting brighter and dimmer over time. But as they cool, these pulsations stop abruptly - and scientists don’t fully understand why. Recently, space telescopes like Kepler and TESS have discovered strange behavior in some of these stars, such as sudden outbursts and random changes in brightness. This project will use computer models to understand what causes these phenomena and why pulsations cease as white dwarfs cool. Alongside the research, the project will train two graduate students and involve undergraduates in hands-on astrophysics. Some of these students will explore real telescope data in the classroom and contribute to research projects. A study will track how these experiences affect students’ views of science and their academic paths. The investigator will conduct a comprehensive theoretical investigation into the cessation of pulsations and the origin of outbursts in cool DA (hydrogen-atmosphere) white dwarfs near the empirical red edge of the instability strip. The project combines large grids of evolutionary models generated using MESA, WDEC, and ISUEVO, with linear adiabatic and nonadiabatic pulsation calculations via GYRE and GNR1-based codes. The work will evaluate mode growth rates, explore sensitivity to convection prescriptions, and analyze amplitude coherence as a function of stellar mass and effective temperature. A key innovation of the project is the inclusion of nonlinear three-mode resonant coupling calculations to test whether these interactions can suppress observable pulsations or trigger episodic outbursts. The modeling will explore amplitude saturation, limit-cycle behavior, and energy deposition mechanisms that could reproduce observed features such as broadened power spectra and burst-like luminosity increases. The project also includes integration of white dwarf variability research into undergraduate astronomy coursework and a longitudinal study of student learning outcomes. 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 · 2025-09

The menopause transition is a key period of increased cardiovascular disease (CVD) risk in women, related in part to changes in vascular health.1 Vascular mechanisms underlying CVD risk during the menopause transition may also influence women’s disproportionate risk of later-life vascular dementia2,3 and Alzheimer’s disease and related dementia (ADRD), which impacts 3x more women than men.4 Vascular health in midlife and thus, the menopause transition, is highly predictive of later-life brain health and dementia risk.5-10 It is currently unknown how vascular mechanisms of brain health change across the stages of the menopause transition.11,12 This critical gap in knowledge limits mechanism-targeted preventive interventions in midlife to reduce the burden of ADRD1,13,14 in aging women. Although cerebral blood flow is a classic and important mechanism of ADRD, our team’s promising preliminary data suggest cerebral pulsatility may be a novel candidate mechanism by which the menopause transition accelerates brain aging and ADRD risk in women. Cerebral pulsatility describes the discontinuous nature of blood flow in the fragile cerebrovasculature that is a key mechanism of cerebrovascular disease.15 Higher cerebral pulsatility16,17 may damage vascular and structural components of the brain over time16-23 and compromise brain health.18-25 Our promising preliminary data shows women i) experience linear reductions in cerebral blood flow, but a disproportionate, non-linear increase in cerebral pulsatility between 45-55 yrs of age (typical age of the menopause transition),26 and ii) are more vulnerable to cerebral pulsatility than men.27 Exaggerated increases in cerebral pulsatility across the menopause transition may contribute to accelerated brain aging in women. This idea is supported by our work identifying vascular contributors to pulsatility (large artery stiffness and characteristic impedance) that are altered in women during midlife28 and the menopause transition.29,30 Our recent meta-analysis31 indicates cerebral pulsatility is poorly characterized across the stages of the menopause transition and identified key study design components to directly address this knowledge gap. Our team is uniquely positioned to study cerebral pulsatility across stages of the menopause transition as a plausible vascular mechanism of brain aging and ADRD risk in women,11,12,31 thereby informing future intervention development. These topics align with the scientific areas of interest in sex-specific aging trajectories and mechanisms of ADRD (NOT-AG-21-050/-21-039, NOT-OD-24-079) and key foci of the White House Initiative on Women’s Health Research.

NSF Awards · FY 2025 · 2025-09

Computational wave imaging, vital for uncovering hidden properties in diverse fields of science and engineering, such as materials science, medicine, and geoscience, faces significant challenges. Traditional methods struggle with the inherent complexity and computational demands of such problems. Although deep learning offers promise for these scientific inverse problems, its efficacy is hindered by the scarcity of labeled data, often due to costly experiments and expertise requirements. This underscores the need for innovative approaches that circumvent data limitations in wave imaging. This project seeks to optimize the potential of deep learning in computational wave imaging by introducing techniques to address data scarcity and improve generalizability, aiming to drastically lessen deep learning's dependence on extensive labeled datasets, efficiently generate high-quality training data, and greatly improve deep learning's capacity to solve real-world problems. It also emphasizes educational integration and interdisciplinary collaboration, and promotes the sharing of open-source computer codes and datasets, enhancing the broader scientific community’s ability to conduct research and providing educators with valuable tools for teaching computational and data-enabled science, engineering, and mathematics. Physical principles will be integrated with advanced deep learning models in hybrid learning strategies. Hybrid strategies involve efficient wave simulations results which can address the challenges of data and label scarcity, and the weak generalizability in computational wave imaging. A novel self-supervised learning method will be introduced, which can uncover hidden physical principles within the latent space. Preliminary investigations have revealed an “Auto-Linear” phenomenon, where features from different physical domains automatically correlate linearly. This discovery allows for simultaneous forward and inverse modeling, significantly enhancing performance in imaging tasks that lack paired data. Efficient wave simulations will also be developed. They will involve high-order methods for effective forward propagation and backpropagation, with explicit Runge-Kutta time stepping for non-stiff problems and A-stable implicit Runge-Kutta time stepping for stiff problems, combined with Fourier or spectral element spatial approximations. Furthermore, integral-based methods with asymptotic short-time Green's function will be developed for problems with point-source-like source functions. This configuration is designed to simulate wave propagation with high accuracy and minimal sampling requirements in both time and space, thus avoiding the pollution effect and promising a leap in simulation efficiency and quality. 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 2025 · 2025-09

Processes that involve the transport of electrically charged species in fluids consisting of multiple phases, such as liquids and gases, are fundamental in energy production, energy storage, healthcare, and manufacturing. Important examples include energy storage systems such as batteries, hydrogen production, medical diagnostic devices, and manufacturing processes. Despite their importance, accurately modeling these processes remains challenging due to their complex interactions and varied scales ranging from microscopic interfaces to large-scale systems. Addressing these challenges can significantly enhance the performance, efficiency, and affordability of critical technologies, particularly in energy production and storage. This project develops an accessible, advanced simulation framework that enables scientists and engineers to effectively model and optimize these vital electrochemical processes. By simplifying complex computational challenges, the project accelerates innovations across several crucial sectors, benefiting society through improved energy technologies, healthcare applications, and industrial processes. Educational activities and community training are integral components, aimed at increasing STEM participation and training a workforce skilled in cutting-edge technologies. This project develops an architecture-agnostic computational framework called FASTEST (Framework for Advanced Simulation of multiphaSe ElecTrochemical Systems). FASTEST provides scalable, robust, and accurate simulation capabilities for the complex, multiscale dynamics of multiphase electrochemical systems. FASTEST employs a domain-specific language (DSL) to enable domain experts to focus on scientific modeling while computational experts optimize performance and scalability. The framework combines scalable adaptive meshing, implicit numerical methods, and architecture-aware portability, leveraging modern computing resources such as multicore CPUs and GPUs. It addresses longstanding computational challenges in the modeling of electrochemical systems, such as stiff equations, multiscale adaptivity, and implicit solvers, with optimized numerical algorithms and iterative solvers. FASTEST improves computational speed and accuracy compared to current commercial and open-source solutions. The project emphasizes the development of robust numerical methods, scalable parallel algorithms, and a user-friendly interface to facilitate widespread adoption and application. Comprehensive validation, verification, and benchmarking efforts ensure accuracy and reliability, supporting broad applications in energy production, energy storage, manufacturing, and bioengineering. The outcomes advance simulation-based understanding and optimization of multiphase electrochemical systems, fostering innovation across multiple critical technological domains. This project is co-funded by the Office of Advanced Cyberinfrastructure (OAC) and the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI). 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 2025 · 2025-09

Remarkable advances in Artificial Intelligence (AI) have demonstrated near-human cognitive performance in various applications. However, state-of-the-art AI still exhibits a large (orders of magnitude) efficiency gap compared to human brains. Enabling efficient AI hardware/software systems will be the key to deploying AI in various domains, including transportation, healthcare, and defense. Taking cues from the biological brains, neuro-inspired computing recently emerges as a promising approach to addressing the computational efficiency challenges. However, neuro-inspired computing with the complementary metal-oxide-semiconductor (CMOS) digital hardware lacks flexibility and efficiency due to mismatch at various levels from device to architecture. This project will leverage novel magneto-electronic (spintronic) technologies to create efficient and robust computational components that emulate neural stochastic functionality. The components will be integrated into in-memory computing architectures and co-designed with bio-inspired learning algorithms to achieve advanced cognitive capabilities. This project will significantly advance the science of developing next-generation AI hardware with emerging technologies. By implementing device-to-system co-design for stochastic in-memory computing, this project will create interdisciplinary knowledge of device integration, computing architecture design, and algorithm development. Such knowledge is crucial for addressing the challenges of AI computation. In this project, two interesting attributes of biological brains, i.e., stochastic computing and processing in memory, will be exploited to develop novel computing systems for AI. Stochastic spin-orbit-torque magnetic tunnel junctions with high thermal stability will be customized to realize various functionalities of an in-memory computing system. Compared to the existing work with thermally unstable devices, the proposed high-stability devices minimize the influence of noises and device variations, leading to a scalable solution for the robust and efficient implementation of stochastic neural networks. The new device design will also drastically reduce the overhead of peripheral circuits in the in-memory processing elements. To further unleash the full potential of the proposed spin-based stochastic neuro-mimetic components, we explore device-to-algorithm co-design, including a hardware-in-the-loop architecture search to develop neural network models that could better match the hardware characteristics. We will demonstrate prototypes of domain-specific stochastic neuromorphic computing systems for general deep neural networks. Corresponding circuit design and simulation tools will be developed as a part of this 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.

NIH Research Projects · FY 2025 · 2025-09

Valvular heart diseases, such as mitral valve regurgitation, significantly impact patient health and require personalized therapeutic strategies for long-term success. Current interventions involve valve repair or replacement through surgical or minimally invasive procedures aimed at restoring normal valve function. However, optimizing these treatments for individual patients remains challenging due to the complexity of cardiac and valvular mechanics. Although computational modeling, particularly finite element analysis, has enhanced ou r understanding of cardiovascular dynamics, its computati onal intensity limits practical use in real-time clinical decision-making and long-term patient monitoring. This research aims to advance personalized cardiovascular care by developing a rapid, predictive, and adaptive digital twin technology capable of forecasting heart valve disease progression, optimizing interventions, and improving therapeutic outcomes. Our central hypothesis is that integrating advanced scientific machine learning (SciML) with computational modeling will substantially accelerate simulation speeds, enabling fast and predictive modeling of cardiac biomechanics, hemodynamics, and fluid-structure interactions (FSI), which are essential for long-term optimization of heart valve interventions. To achieve this, we propose developing a neural network finite element (NNFE) framework to significantly accelerate complex multiphysics heart valve simulations for adaptive, personalized treatment planning. Specifically, Aim 1 focuses on developing an efficient neural network-driven FE model for structural cardiac mechanics and valve dynamics, while Aim 2 extends this NNFE technology to simulate multiphysics cardiovascular interactions involving patient-specific hemodynamics. Aim 3 integrates these NNFE models into a comprehensive digital twin platform ta rgeting mitral valve regurgitation, assessing the effectiveness of Transcatheter Edge-to-Edg e Repair (T E E R), and examining the impacts of pre- and post-operative valve geometry, mechanics, and blood flow on long-term clinical performance. Ultimately, the proposed heart valve digital twin will enable clinicians to predict disease progression, optimize valve interventions, and improve patient outcomes. This project represents a transformative advancement in personalized medicine, aiming to improve healthcare delivery, reduce costs, and enhance the quality of life for cardiovascular patients. RELEVANCE (See instructions): Heart valve disease is a serious condition that can greatly affect a person's health and daily life if not managed properly. This research uses advanced computer simulations and artificial intelligence to create a digital heart model that quickly and accurately predicts how individual patients will respond to different treatments. By enabling personalized therapies and providing faster insights into treatment outcomes, this approach could improve patient care, lower healthcare costs, and enhance overall quality of life.

NSF Awards · FY 2025 · 2025-09

The analysis of imaging outcomes is a dynamic and rapidly evolving research field, driven by the growing accessibility of large-scale biomedical imaging databases. Imaging data, often characterized as functional data, presents unique opportunities and challenges for statistical analysis. Existing methods, however, are insufficient for handling the computational demands of large-scale medical imaging data or addressing issues such as unmeasured confounding and population heterogeneity in causal analysis. This research will develop advanced statistical tools to overcome these critical hurdles. By developing new techniques that efficiently process large-scale imaging information and provide more accurate causal insights, this work will advance national interests in scientific innovation and evidence-based decision-making. It will promote scientific progress in a vital area of imaging data analysis and aims to advance public health by enabling a deeper understanding of treatment effects from observational studies. The developed data analytics tools also have broad applicability across various fields, including aging research, digital health, and plant science, addressing challenges faced by modern society. Furthermore, the project will benefit the broader research community through the release of freely available software tools and will support STEM education by involving undergraduate and graduate students in hands-on research and integrating project findings into curriculum development. This project aims to develop a general functional data analysis (FDA) framework for analyzing large-scale imaging data and uncovering causal relationships between treatments/exposures and imaging responses. Specifically, the project will address challenges in large-scale observational imaging studies via three aims. First, it will develop functional regression models for imaging responses based on a distributed learning framework, enabling scalable yet accurate estimation and inference. Second, it will introduce an image-on-scalar instrumental variable regression to mitigate confounding bias in observational studies. Third, it will propose an image-on-scalar doubly robust regression method leveraging functional pseudo-outcomes to address population heterogeneity. The proposed methods will be rigorously evaluated using existing imaging studies and are expected to significantly advance the methodology, theory, and computation of FDA and causal inference. Additionally, by releasing open-source software, the project will empower researchers to harness vast amounts of imaging and functional data from publicly available repositories. 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 2025 · 2025-09

Real-world systems are composed of interconnected entities that collaborate to perform diverse yet interrelated tasks, often requiring sequential decision-making. Over the past decade, multi-task learning has emerged as a powerful paradigm for collaborative learning, significantly enhancing efficiency while enabling privacy-preserving knowledge sharing. One of the most promising approaches to learning-based control is dynamic sequential learning, such as reinforcement learning, which learns through interactions with the environment. However, reinforcement learning faces three critical challenges in real-world dynamical systems: data scarcity and heterogeneity, scalability and communication efficiency, and safety. Moreover, achieving provable guarantees in joint learning often requires leveraging underlying problem structures. This CAREER project will develop a unified approach to multi-task representation learning by leveraging the shared representations to offer a viable solution to these challenges, enabling privacy-preserved joint learning in dynamic environments. Research and education will be synergistically integrated to train students in the interdisciplinary field of data science and control, addressing the pressing need for skilled workforce development in this emerging area of societal importance. The central objective of this project is to develop provable methods for multi-task representation learning in bandit and reinforcement learning settings. At its core is a novel algorithm, (de)centralized Alternating Gradient Descent and Minimization (AltGDmin), designed to address the challenges of non-convex, under-sampled, and constrained problems. This approach enables fast and federated representation learning while effectively tackling data heterogeneity, scalability, communication efficiency, and safety concerns. The project technical plan includes three research thrusts together with several validation activities and an integrated education plan. The first goal is to create a provable few-shot personalized federated multi-task representation learning framework for bandit and reinforcement learning. The second goal is to develop a fully decentralized, federated multi-task representation learning framework for bandit and reinforcement learning over a networked architecture among agents. Our approach will eliminate the central server, enhancing scalability, communication efficiency, and robustness by removing single points of failure. The third goal is to develop an innovative safe multi-task representation learning framework to learn optimal policies while incorporating the safety constraints. The main application domains of interest are control and automation in smart farms, which will guide the problem formulation and validate the algorithms using real-world implementation, testing, and numerical experiments. The overarching goal of the integrated education plan is to provide a pathway for K-12 to college students to receive rigorous math training and hands-on experience, including coding skills for ML-based intelligent system design. 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 2025 · 2025-09

The objective of this project is to support research on creating, implementing, and testing a novel online serious game on emergency response. Iowa State University (ISU) and Polk County Emergency Management (PCEM) in Iowa collaboratively develop the Disaster Multiplayer Online Game (DMOG). Emergency response is a complex process, involving multiple organizations and decision makers with different roles and responsibilities, objectives, and resources. DMOG intends to improve the efficiency and effectiveness of emergency management (EM) training by engaging key decision makers within an online computer game. Each player in DMOG assumes a role (e.g., county emergency manager, law enforcement, fire department, emergency medical services). DMOG then simulates a sudden onset of a Midwest disaster and forces players to grapple with the uncertainty and trade-offs in their actions. Important research questions are answered, informing the next generation of online games for EM applications. The plan is to expand DMOG to other types of disasters and geographic locations. The learning objectives of DMOG are to: (i) increase knowledge about roles in EM, (ii) enhance EM decision-making competencies, and (iii) foster collaboration and communication among EM decision makers. The goal of creating and evaluating DMOG is to empower decision makers with the skills to prioritize among competing demands and allocate resources during a high-pressure, high-stakes situation, thereby enhancing their communities’ disaster preparedness. The fully functional game at the end of Stage 2 features a set of decisions and consequences around a derecho scenario, creation of a virtual environment of the farmers’ market, and use of generative artificial intelligence (AI) to produce short videos, audio, and text for the game. The complexity and realism of the game in combination with the virtual environment creation and the AI-assisted content generation makes DMOG a truly innovative training tool. The project also assesses if playing the online game results in more informed and better trained decision makers. This project is jointly funded by Division of Civil, Mechanical and Manufacturing Innovation and Advancing Informal STEM Learning Program. 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 2025 · 2025-09

With this award, Professors Chen, Dutta, Li, and Curtzwiler of the University of Maryland and Iowa State University are studying how to leverage artificial intelligence to accelerate the discovery of high-performance and biodegradable polymer nanocomposites with tunable properties. The team will develop an integrated platform that combines robotic platforms, artificial intelligence, and materials chemistry to accelerate the process to identify, design, and test polymer composite films with customizable properties, such as mechanical strength, optical clarity, and moisture absorption. A public, open-access database and user-friendly interface will support broad engagement across scientific, industrial, and policy communities. In parallel, the project will foster workforce development through K-12 research internships, undergraduate mentorship, and the integration of findings into university curricula. These efforts will aim to cultivate the next generation of scientists and engineers equipped to lead innovation in artificial intelligence-accelerated materials discovery. With this award, Professors Chen, Dutta, Li, and Curtzwiler of the University of Maryland and Iowa State University are studying how to integrate high-throughput robotic experimentation, explainable machine learning, and multiscale simulations to enable predictive design of biopolymer nanocomposites. The project will develop a multi-attribute descriptor framework to encode molecular structure, processing conditions, and life cycle assessment metrics for multiple biopolymer components that are generally recognized as safe. These descriptors will be used to generate and analyze thousands of composite formulations via a robotics-enabled workflow. Data from optical, mechanical, and dielectric characterization will train an ensemble of neural network models capable of accurately predicting properties. The project will apply counterfactual explanation algorithms to identify key formulation and processing features that drive high performance and support inverse design. Complementary molecular dynamics and density functional theory simulations will provide atomistic insight into the effects of ion binding and chemical modifications. The data, tools, and models from this project will be disseminated through a cloud-based platform that enables forward and reverse materials design. This framework will expand the accessible design space for biodegradable polymers and accelerate the development of next-generation materials that combine high performance with low impact on resources. This Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics (MFS-SPEED) award is co-funded by the NSF through the Division of Chemistry (CHE), the Directorate for Mathematical and Physical Sciences (MPS), and the Division of Innovation and Technology Ecosystems (ITE) in the Directorate for Technology, Innovation, and Partnerships (TIP). Additional MFS-SPEED funding is provided by Procter & Gamble, PepsiCo, Dow, BASF, and IBM. 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 2025 · 2025-09

The goal of the project is to develop novel advanced materials integrated with real-time process feedback, assisted by a machine learning algorithm, to enable scalable, autonomous in-situ manufacturing of electronics. The technology will provide capabilities for on-demand fabrication, adaptive repair, and dynamic reconfiguration of circuits, functions that are particularly critical for long-duration space missions where resupply is difficult. These enhanced materials and manufacturing processes will support future space exploration initiatives. Beyond space applications, the methods developed here may also transform multiple technology sectors including flexible hybrid electronics for wearable devices, neuromorphic computing systems that mimic brain functions, and distributed manufacturing solutions for remote or resource-limited environments. The research incorporates workforce development initiatives to train students in cutting-edge techniques spanning materials science, artificial intelligence, and advanced manufacturing. Participants will gain hands-on experience in functional materials synthesis, intelligent process control systems, and semiconductor device fabrication, skills directly aligned with emerging needs in the advanced manufacturing sector. The project specifically addresses national workforce development priorities in critical technology areas including additive manufacturing, semiconductor processing, and autonomous production systems. This project develops a new method to manufacture electronics in space using 2D materials like molybdenum disulfide (MoS₂). These ultra-thin materials are ideal for space applications because they are lightweight, radiation-resistant, and energy-efficient. The key innovation combines three critical components: (1) specially designed chemical inks that transform into functional electronics at relatively low temperatures, (2) an artificial intelligence (AI)-controlled printing system that adjusts in real-time to produce perfectly aligned layers, and (3) precision laser processing that fine tune the material's properties after printing. First, new ink materials and formulations will be created, where the molecular structure determines how well the material performs in final functional semiconductor devices. Then AI systems will be implemented to monitor and optimize the printing process, catching and correcting any defects in real-time. Finally, laser sintering will be utilized to control and enhance the material's electrical properties, enabling complete electronic device processing onsite. This integrated approach solves a major challenge in space manufacturing by eliminating the need for complex equipment or high temperature processing. The methods could enable in space manufacturing of electronics during long missions without relying on Earth-based supplies. The same technology may also improve manufacturing of flexible electronics and advanced computing systems on Earth. 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 2025 · 2025-09

The ability to debate on a topic; that is, the process of generating and evaluating claims, is a core competency for both academic success and civic life. Students traditionally learn these skills by participating in classroom discussions, during which they share, justify, and challenge each other's ideas. However, such discussions may be rare in classrooms and challenging for teachers. Without proper facilitation the discussion can get off track and even become heated. Unfortunately, teachers lack opportunities to improve these important facilitation skills. Coaching is an effective strategy for helping teachers learn facilitation, but it is hard to deliver at scale due to the lack of qualified coaches, time constraints, and teacher discomfort with being observed by a human coach. This project will address the challenges of teacher professional development by designing a simulated classroom environment in which elementary school teachers can practice facilitating discussions with AI-driven student avatars. The system will offer multiple low-cost and low-stakes opportunities for teachers to practice facilitation and receive expert-informed feedback from an automated coach. This project will provide a much-needed solution to one of the most pressing issues confronting education today: scaling up effective pedagogy that fosters students' skills. To this end, this project will integrate research in artificial intelligence, argumentation, and teacher learning to create a novel automated professional development system for teachers. A key technological innovation of this system is its hybrid AI architecture: a transparent, rule-based symbolic inference engine will structure simulated classroom dialogue and provide meaningful feedback to the teacher, while large language models will generate realistic responses from AI-driven student avatars. The system will formalize and integrate two established frameworks to support the analysis of teacher facilitation and student argumentation: the Argumentation Rating Tool and the Rational Force Model. Using design-based research, the researchers will develop effective design principles for AI-supported teacher learning. A randomized controlled trial will assess whether the system improves teachers' facilitation skills and confidence in facilitating discussions. In addition to contributing to scalable, high-quality professional development, the project will inform the design of ethical and transparent AI systems for education, with potential applications in intelligent tutoring and instructional coaching. 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 2025 · 2025-09

Wildfires in the United States are becoming increasingly destructive, threatening public health, infrastructures, and human lives, especially in areas where communities border wildlands, known as the wildland-urban interface (WUI). Although prescribed fires (controlled burns) are among the most effective tools for reducing wildfire risk, they remain underused in the WUI due to liability concerns, smoke-related opposition, and logistical barriers. This project addresses a critical challenge: how to design prescribed fire strategies that are both effective and socially acceptable in vulnerable communities. The research team will work directly with local residents, fire managers, and practitioners to develop community-centered planning approaches that reflect real-world needs and constraints. The broader impacts of this project include advancing science-based tools for wildfire mitigation, supporting public dialogue on the trade-offs of prescribed fire, and enhancing STEM education and workforce development in Iowa, an EPSCoR designated state. The project includes four integrated components: (1) a large-scale public perception survey to understand how WUI communities in the Western and Southeastern U.S. perceive prescribed fires and smoke; (2) a statistical wildfire risk model that accounts for uncertainty in fire intensity and spread; (3) a fast-computing, physics-informed smoke propagation model that quantifies uncertainty under varying environmental conditions; and (4) a flexible and probabilistic optimization model that identifies cost-effective and community-informed prescribed fire strategies. By combining these elements, the project will equip fire managers with robust tools to plan more beneficial and publicly supported prescribed burns. This interdisciplinary work brings together expertise in risk communication, statistical modeling, and operations research to address a pressing environmental challenge. This project is jointly funded by the GEO/RISE Fire Science Innovations through Research and Education (FIRE) program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Infectious disease medicine is poised at the cusp of a revolutionary change in diagnostics, from simple binary diagnostics (presence/absence) directed at individual pathogens to universal tests for all pathogens along with instantaneous genomic characterization. Not only will there be benefits to sick individuals, but the entire population will benefit from better disease monitoring and knowledge of circulating strains. Nevertheless, there is still much work required to achieve genome-level diagnostic information. To achieve high sensitivity at low cost, we assume amplification of input RNA/DNA is necessary. In this context, we are faced with serious computational challenges to design sequence specific oligonucleotides, statistical challenges to account for the biases of sampling when perfect oligonucleotides are inevitably elusive, and diagnostic failures as tests lose sensitivity in the face of evolving pathogens. Recent protocol advances help to separate the confounded biases in traditional sequencing based assays, providing an opportunity for mathematical models to aid in solving the remaining challenges. We propose a novel branching process model exquisitely matched to the emerging sequencing protocols, along with novel statistical estimation methods to link the model to data (Aim 1). Aim 2 proposes to use this system to iteratively optimize a protocol for RNA virus diagnostics. Once initialized, the protocol is expected to be able to solve the oligonucleotide design problem in a data-driven manner. We also propose statistical methods for viral characterization (genotyping) in the face of inevitable biased sampling due to amplification; current methods fail to account for this bias. Aim 3 tackles longer term goals include (1) exploring the generalizability of our findings to related, but distinct protocols, (2) developing methods to monitor and update an active diagnostic protocol in the face of pathogen evolution, and (3) scaling our methods to large diagnostic datasets, such as those produced in diagnostic laboratories, by considering flexible neural networks to model the relationship between sequence and diagnostic detection. RELEVANCE (See instructions): Achieving the goals of this proposal will facilitate the development of many kinds of modern diagnostics for human and animal health. The proposed methodology can assist diagnostic test development, improvement, and maintenance in the face of evolving pathogen threats. Modern diagnostics that monitor infections and the genomic sequence of the infecting agent will facilitate pathogen-personalized treatments and better intervention strategies to protect a vulnerable population.

NSF Awards · FY 2025 · 2025-09

This Research Experiences for Undergraduates (REU) opportunity at Iowa State University aims to broaden participation in aerospace engineering by engaging high-potential undergraduate students in hands-on research and professional development. Titled Launching Aerospace’s Undergraduates into the Next Chapter – Space Technology (LAUNCH-SPACE), the program seeks to foster student interest in graduate studies and careers in aerospace through immersive, mentored research experiences in cutting-edge space technology. Participants will benefit from a structured 10-week summer program that includes five core components: (1) collaborative research projects, (2) professional development workshops, (3) field trips and lab tours, (4) research luncheon seminars, and (5) social and networking events. Recruitment efforts will focus on identifying qualified candidates from a range of institutions, including community colleges and universities across the country. The program emphasizes academic merit, research potential, and student interest in aerospace as key selection criteria. By offering meaningful research experiences and mentorship in a supportive academic environment, LAUNCH-SPACE will contribute to the national effort to develop a highly skilled and innovation-driven STEM workforce prepared to address the complex challenges of space technology and exploration. 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 2025 · 2025-09

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Aaron Rossini at Iowa State University is developing solid-state nuclear magnetic resonance (SSNMR) spectroscopy approaches to enable the atomic-level structural characterization of formulated pharmaceuticals. A persistent challenge in the pharmaceutical industry is the successful design of formulations for the administration of active pharmaceutical ingredients (APIs). The performance of pharmaceutical formulations is ultimately determined by the atomic-level structure of the APIs. However, atomic-level structure determination of formulated APIs is challenging because of the low API loading within formulations that is often 10 weight percent or less and molecular interactions between the APIs and other components of the formulation. This project will enable the atomic-level characterization of formulated pharmaceuticals and solid APIs by first developing methods that improve SSNMR sensitivity and resolution. Second, these methods will be used to perform SSNMR spectroscopy experiments with isotopes such as 14N, 17O, 19F and 119Sn to access novel structural information that cannot be obtained by conventional spectroscopic methods or diffraction techniques. Graduate and undergraduate students participating in the research will learn how to perform NMR spectroscopy experiments, prepare pharmaceuticals and computationally model the atomic structures of APIs. The proposed research will be executed in collaboration with scientists at Genentech, Boehringer-Ingelheim and Colgate-Palmolive, ensuring its relevance to the pharmaceutical industry. Students will collaborate with industrial scientists, enhancing their training and giving them insight into potential industrial career paths. 1H and 13C SSNMR spectroscopy are now routine methods for the structural characterization of pure and formulated solid APIs. However, structure determination by 1H or 13C NMR spectroscopy requires known reference structures obtained from X-ray diffraction or model structures obtained by computational methods. To overcome these limitations, 1D and 2D NMR experiments will be performed with exotic and unreceptive NMR isotopes such as 14N, 17O, 19F and 119Sn. 1D and 2D NMR experiments with these isotopes will directly reveal the molecular and macroscopic structure within solid drug forms and formulated drug products. For example, analysis of complementary 2D 1H-17O and 1H-14N SSNMR spectra can be used to directly reveal the structure and hydrogen bonding patterns within solid APIs. 2D 119Sn-17O NMR experiments can be used to directly determine the interactions between tin ions and oxygen atoms present in different ingredients within formulated toothpastes. The proposed research will include demonstrating facile techniques for 17O isotope enrichment to enable 17O SSNMR experiments on multi-component solid APIs, solid solutions and formulated APIs. Instrumentation such as ultra-high magnetic fields, dynamic nuclear polarization (DNP) and fast magic angle spinning (MAS) combined with indirect detection will be used to enhance the sensitivity and resolution of SSNMR spectroscopy. Sensitivity enhancement by these techniques is crucial to extend SSNMR experiments to systems which feature low API loading such as commercial drug formulations or solid solutions. 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 2025 · 2025-09

The Eastern Hemisphere tropics and subtropics are highly sensitive to variations in regional monsoon precipitation, which provides the majority of freshwater for the ~40% of the world population who reside there. Understanding interannual to multidecadal variations in the monsoon is critical for managing water resources. This project will compile data from cave deposits, corals and tree rings, as well as develop new records from caves in the Philippines and northern Australia, to produce a reconstruction of the behavior of the Austral-Asian monsoon for the last 1000 years. This reconstruction will guide a set of climate model simulations to identify the drivers of monsoon variability. The results will improve decadal prediction the Austral-Asian monsoon. The project will support the participation of a postdoc and undergraduate students in the research, an art-science collaboration, K-12 education, and public outreach to primary and secondary students in Iowa, California, New Mexico and Northern Australia. The goal of this project is to synthesize existing data from stalagmites, corals and tree rings with new cave records from the Philippines and northern Australia to reconstruct the Austral-Asian monsoon for the last 1000 years. The resulting multi-proxy reconstruction will guide a suite of climate model simulations, including large ensembles and isotope-enabled models, to identify drivers of monsoon variability and quantify the relative contributions of external (solar, aerosol, greenhouse gas) and internal (tropical basin interactions) forcings. The results will improve decadal prediction the Austral-Asian monsoon. The project will support the participation of a postdoc and undergraduate students in the research, an art-science collaboration, K-12 education, and public outreach to primary and secondary students in Iowa, California, New Mexico and Northern Australia. 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 2025 · 2025-08

The AI-AgARENA project will develop a national-scale planning roadmap for an artificial intelligence (AI)-ready agricultural research testbed. The proposed infrastructure is designed to support the development and testing of advanced AI technologies in real-world farming environments, enabling breakthroughs in resilient agriculture. The project will unify geographically distributed testbeds across Iowa, Arizona, and Ohio, allowing researchers to evaluate AI methods across a range of crops, soil types, and climatic conditions. By integrating autonomous robotics, wireless networking, and multimodal sensing with cyberinfrastructure, AI-AgARENA aims to democratize access to agricultural data and test environments for a broad research community. The project addresses pressing challenges in food security and agricultural resilience while supporting U.S. leadership in emerging AI and wireless technologies. The planning effort will engage scientists, educators, industry stakeholders, and government agencies to ensure the testbed is extensible, and responsive to national priorities. Education, outreach, and workforce development are integral to this effort, with special attention to training future AI-agriculture innovators. This work supports NSF’s mission by advancing science and technology, strengthening U.S. economic competitiveness, and promoting societal well-being through sustainable food systems. The project will plan and design AI-AgARENA, an AI-ready cyber-physical testbed that enables deployment, validation, and benchmarking of AI tools in real agricultural settings. The effort will integrate existing agricultural infrastructure (such as sensing platforms, drones, robots, and communication networks) across three institutions, coordinated via shared cyberinfrastructure built on the NSF-funded CyVerse platform. The planning activities include team formation and governance design; prototyping cyberinfrastructure for real-time data access and model deployment; defining financial sustainability models for long-term operation; developing a community engagement strategy through workshops, surveys, and early demonstrations; and identifying use cases for AI research in plant phenotyping, autonomous field operations, multi-agent coordination, and digital twins for crop management. The testbed will be designed to support high-throughput multimodal data collection, AI model training and inference, secure and reproducible experimentation, and shared access for academic, industry, and agency partners. Outcomes will include a well-scoped implementation plan, beta-tested prototypes, and a set of priorities aligned with the needs of the AI, agriculture, and wireless research 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 2025 · 2025-08

Artificial intelligence (AI) chatbots are transforming education, but their impact on student learning and assessing academic merit remains unclear. This research investigates why students use AI and how using AI while taking online exams affects student performance, whether the outcomes of the exams remain valid, and whether it promotes or hinders learning over the longer term. With the widespread use of AI chatbots by students, understanding the impact of AI on learning and assessment is key to ensuring effective education and evaluation. If successful, results from this project have potential translation to the classroom in helping educators promote learning and design effective online exams in the age of AI. In addition to the research, the project provides workshops for educators and creates research opportunities for undergraduate students in cognitive and computer sciences, preparing them for STEM careers in an AI-driven world. This project frames the use of AI as a form of cognitive offloading and investigates the factors that drive students to use AI chatbots for online exams. Furthermore, the project tests whether or not this leads to gains in performance, and if this promotes or hinders long-term learning. A series of studies are planned that test key predictions that cognitive effort, digital literacy, and content knowledge play essential roles in students’ decisions to use AI tools and also relate to its efficacy. Advanced analyses of student-AI interactions identify what makes some prompts more effective than others for performance and learning. The project also examines whether AI-resistant question designs can help maintain exam validity. The overall goal of this research is to better understand AI in terms of cognitive offloading and how this may be used in educational settings. 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 · 2025-08

Project Summary/Abstract Malaria remains a major global health burden with 608,000 deaths and 249 million cases annually. Sexual reproduction of Plasmodium parasites in the mosquito vector is essential for successful transmission and continuation of the malaria life cycle and constitutes a bottleneck in the parasite life cycle. Male and female gamete fertilization and its central cell fusion event are an essential part of sexual reproduction but the molecular mechanisms underlying fertilization are poorly understood. Yet, this process constitutes a valuable target for the development of vaccines that can block transmission. We have recently discovered that Plasmodium has a hybrid fertilization machinery with components from both plant and animal sexual reproduction and gamete fusion machinery suggesting presence of gamete fusion complexes like those present in animals and plants. We have identified three proteins, PfHAP2, PfHAP2p and PfHAP2p2 in the human malaria parasite Plasmodium falciparum (Pf) that are similar to HAP2/GCS1 proteins, which in plants and other eukaryotes, are involved in gamete fusion. We have demonstrated that both PfHAP2 and PfHAP2p are essential for transmission. Discovery of a third HAP2-like protein (HAP2p2) in very interesting as no other organism has three gamete fusion proteins in their genome. PfHAP2p2 is expressed in both male and female gametocytes, gametes and continues to be expressed during fertilization which makes HAP2p2 a target of potent transmission- blocking antibodies. In this application, we will further elucidate the role of PfHAP2p2 in Pf fertilization. In aim 1, we will delineate the functional importance and cell biology of PfHAP2p2. We will also demonstrate its cell fusogenic function. In aim 2, we will establish whether PfHAP2, PfHAP2p and PfHAP2p2 together function in a complex and identify potential additional components of the male and female gamete fusion complexes. We will also establish the utility of PfHAP2p2 as straightforward transmission blocking targets. We will validate these gamete fusion complex proteins for their role in fertilization and establish their relationship to the gamete fusion complex. Together, this work constitutes a critical step towards a comprehensive molecular understanding of Plasmodium gamete fertilization and will inform new ways by which transmission of the parasite can be blocked with vaccines.

NSF Awards · FY 2025 · 2025-08

Fruits, grains and seeds account for most calories entering animal and human food systems. These vital commodities are produced by flowers that develop from plant stem cell tissues called meristems. Floral meristems (FMs) are unique in that they terminate after stem cells have been used up in the production of floral organs. Timing and proper completion of FM termination can have major impacts on fitness and yield. FM termination must be synchronized with the growing period in a tightly regulated process to ensure that complete sets of floral organs are produced. Fully formed flowers ultimately ensure reproductive success and produce food. This project will address fundamental questions in plant biology concerning floral patterning and meristem termination in maize (corn) by shedding light on an agronomically and economically crucial process in an indispensable U.S. and global crop species. A set of naturally occurring maize mutants defective in FM termination will be used to determine the genes and networks that underlie how normal flowers develop. The project will provide high-quality training and mentoring for trainees in genetics, genomics and developmental biology. A course-based undergraduate research experience will offer authentic hands-on research opportunities to students as part of their normal coursework. Annual science events will bring tangible activities related to floral biology and development to high school students interested in pursuing paths in STEM fields. Finally, an experiential learning module will be developed for the community to illustrate the importance of FMs in agriculture and the links between FM activity and floral morphology. Across billions of arable hectares worldwide, proper regulation of floral meristem (FM) termination can have major impacts on yield in crop plants, including maize. However, despite decades of research on transcriptional regulation of floral development and meristem determinacy in non-cereal model plants, extensive gene regulatory network (GRN) analyses or genome-wide chromatin maps of FM progression are lacking, leaving downstream genes and modes of regulation open for discovery. Furthermore, despite the fundamental importance of flower development to reproduction and yield, a comprehensive understanding about genes, genomic regions, and/or GRNs that regulate FM termination in maize and other cereal crops is lacking. This project will utilize a multi-omics approach with a core set of classical and newly characterized maize floral mutants to identify novel regulators of FM activity and elucidate the genomic circuitry that governs FM termination. The research will produce developmentally staged transcriptomes and determine the chromatin and cis-regulatory landscapes across maize floral development, information that will then be integrated to generate a transcriptional GRN. Future applications of these data could be leveraged to generate genome- and/or developmentally informed inducible synthetic factors to increase environmental flexibility during floral development. The work will generate an unprecedented resource for the plant science and breeding communities, further enabling functional plant genomics and crop improvement and linking basic research to applied outcomes in agriculture. Project outcomes will be disseminated broadly through publications and long-term, through public data and genetic resource repositories. 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 2025 · 2025-08

Filamentous fungi have a dramatic impact on the global economy (by one estimate, trillions of dollars annually) through both beneficial applications, such as pharmaceutical production and sustainable biomaterials, as well as harmful effects including crop destruction and human disease. In all these cases, fungi depend critically on their protective cell wall for success. Despite this importance, it is not fully understood how fungi respond to, and recover from, cell wall damage. This research investigates the fundamental biological question of how fungi detect wall stress, survive initial damage, and eventually restore normal growth. The research uses advanced microscopy, genetic tools, and computational modeling to uncover the molecular mechanisms that coordinate these responses in a model fungus. Understanding these processes will eventually enable "tuning" of fungal cell-wall properties for diverse applications, including: increasing productivity in bioprocess manufacturing, improving the physical properties of renewable mycelium-based materials that could replace petroleum-based products, and identifying new targets for antifungal drugs to protect crops and improve human health. The research also provides significant educational opportunities, training both undergraduate and graduate students in interdisciplinary approaches that combine biology, engineering, and computational sciences through collaborative teams across three universities. This project investigates how filamentous fungi respond to cell-wall stress, focusing on the model fungus Aspergillus nidulans. The molecular mechanisms involved in both immediate survival responses and subsequent recovery from wall damage are characterized using (i) advanced microscopy to visualize actin localization and dynamics during stress, (ii) genetic manipulation to identify key regulatory proteins, (iii) systems biology approaches to discover novel components, and (iv) mathematical modeling to integrate these findings into a cohesive network model. Specifically, the fungal response to inhibition of β-glucan biosynthesis is being characterized by testing the hypothesis that a two-phase response is involved. This includes an initial "survival phase," with rapid actin redistribution to form protective septa, which is followed by a "recovery phase" involving expression of specific proteins enabling growth resumption. In addition, a core set of stress regulators is being identified from proteomic analysis by comparing responses across multiple wall stressors, distinguishing universal responses from stressor-specific reactions. Finally, a hybrid modeling approach is being developed which integrates both mechanistic and machine-learning methods to infer the topology of regulatory pathways and their interconnections. 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.