Iowa State University

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The functional annotation of proteins is a major bottleneck of biological discovery in the post-genomic era. We are able to accurately generate large swaths of genomic and metagenomic sequence data. We are also able, to a lesser extent, to assemble those sequences correctly, and identify open reading frames. Our capability to accurately generate biological knowledge from genomic data drops precipitously at the third step: assigning the biological function to proteins. The Critical Assessment of Functional Annotation, or CAFA is a computational challenge that involves a community of computational biologists, data scientists, ontologists, and biocurators working together to improve and distribute protein function prediction algorithms. Here we propose (i) to sustain and enrich the CAFA community of practice by continuing the CAFA challenges, while involving biocurators and computer scientists not regularly associated with this community. This will be accomplished by increasing the engagement with other communities and by incentivizing the development of containerized and Open Source software to be incorporated into continuous use in UniProt; (ii) to drive continuous improvement in gene function prediction and annotation by transitioning CAFA to a continuous event and by developing algorithms that prioritize proteins for biocuration and experimental annotation; (iii) to use annotation extensions and subsequently the Gene Ontology Causal Activity Model to capture causal relationships between the functionality of proteins, and then challenge the function prediction algorithms to adopt causal annotation models. This project shifts the field of computational function prediction to drive the accurate annotation of protein function in a fine-grained, context-dependent, and causal manner.

NIH Research Projects · FY 2025 · 2024-09

Despite recent advances in gene editing, the ability to make small changes in the genomes of model organisms remains a challenge. In zebrafish, enhanced oligo design, base-editing and prime-editing show promise to increase the frequencies of small changes but their application is still cumbersome due to low germline transmission rates, requiring extensive and time-consuming screening. This bottleneck prevents the efficient targeted production single nucleotide polymorphisms (SNPs) for disease modeling, limits our ability to perform mechanistic structure-function studies, and prevents rigorous determination and the assessment of variants of uncertain significance (VUS) that promote human disease. The overarching goal of the proposed experiments is to create a gene editing method to efficiently introduce SNPs and VAPs into the zebrafish genome for modeling human health. We have used short regions of homology surrounding a CRISPR/Cas9 double strand breaks to efficiently target integration of DNA cargos for mutagenesis, protein tagging, Gal4 reporters, Cre-drivers and conditional alleles, using a method we have coined GeneWeld. Key to this methodology is repair at a double strand break in genome produced by injection of Cas9 mRNA and a genomic gRNA with a plasmid template. The circular plasmid template is cut in vivo with a gRNA, called a universal gRNA (3), that recognizes sequences adjacent to the short homology to produce a linear template for integration. The repair and subsequent integration of the cargo is likely utilizing microhomology-mediated end joining (MMEJ) or a single-strand annealing (SSA) mechanism and results in germline transmission of the targeted integration events in approximately 50% of the F0 injected embryos to the next generation. Since this methodology remains more efficient that other strategies for targeted CRISPR/Cas9-targeted integration in zebrafish, we propose that GeneWeld can be used to efficiently target ingression of SNPs and VUS into the zebrafish genome. For this, we will test the ability of GeneWeld to introduce SNPs at three different loci. We will also examine whether co-selection of RFP repair correlates with ingression of small changes in the genome at a separate gene. Additionally, we will enhance the ability to generate SNPs and VUS in zebrafish by overexpression of DNA repair enzymes and dominant mutations in repair enzymes. Key preliminary data indicates that overexpression of a variant of Rad51 reproducibly enhances repair. We will use this approach to examine the activity of Rad51 for the ingression of small nucleotide changes. The proposed experiments are expected to develop simple and efficient methods to target SNPs and VUS into any gene in the zebrafish genome. In addition, we will generate an important model with a dead RFP knock-in that will be of great utility to assess editing and may provide a means for co-selection of editing events. Based on the ability of GeneWeld to be used in other organisms, we expect these methodologies to be broadly applicable to animal models used by the NIH.

NSF Awards · FY 2024 · 2024-09

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, and the Office of Strategic Initiatives in the Directorate for Mathematical & Physical Sciences, Prof. Brett VanVeller of Iowa State University will develop new classes of polymeric systems. The design of these polymer systems fundamentally differs from conventional approaches, with characteristics of antimicrobial surface, cell-penetrating agents, and polyelectrolyte materials. The World Health Organization (WHO) projects that antimicrobial resistance is on course to overtake cancer as the leading cause of death worldwide by 2050, increasing the urgency for new approaches in antimicrobial design. The VanVeller team is also well-positioned to provide the highest level of education and training for students from underrepresented groups in science. Plans are in place to enhance scientific literacy and critical thinking skills among high school students. Our goal in this application is to develop efficient and robust methods to harness an unexplored functional group in polymer chemistry to access more diverse functional polymers. Our central hypothesis is that sulfur-containing groups can serve as a synthetic branch point to introduce and tune the properties of polymers. Accomplishing our goals will create new knowledge about the polymerization and reactivity of largely unknown functional groups in polymer chemistry. The specific objectives of this project will (1) Identify factors that impact the polymerization of sulfur-containing monomers, (2) develop methods for post-polymerization installation of other functional groups, and (3) expand the functional groups that are accessible on radical chain-growth polymer scaffolds. Collectively, the proposed activities will explore methods to access three distinct functional groups in polymers. The outcomes of this work will expand both our knowledge of radical chemistry and the tunability of sulfur- and nitrogen-containing structures. These outcomes will thereby serve to enhance our understanding of polymeric reactivity and 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 2024 · 2024-09

This project at Iowa State University uses computer simulations to study planet formation and to teach the core principles of astronomy and computational science in a way that aligns with educational standards. There are two crucial, yet unresolved, questions regarding how planets form. First, how can parcels of protoplanetary disk material trade angular momentum? Second, how do planetesimals (e.g., asteroids and comets) form? The grant also supports the development of a novel website comprised of simulation movies and interactive widgets, named the “Computational Astrophysics Lab” (CAL). It will result in the teaching of students (ranging from high school students from underrepresented and underserved populations to graduate students) in astronomy and scientific research. The streaming instability is the current leading model for planetesimal formation, and previous simulations of this process overproduce planetesimals compared with Solar System observations. To solve this issue, the investigators propose a novel mechanism involving gas turbulence that regulates planetesimal formation. This project also focuses on studies of angular momentum transport through numerical simulations and comparisons with observations. Thus, the proposed research would address (1) how angular momentum is transported outward in protoplanetary disks and (2) whether gas turbulence in disks can regulate the formation of planetesimals. The development of the new educational tool will provide a friendly user interface to a sophisticated code. It will be employed at three different levels: (1) The CAL will provide a basic understanding of astronomy and scientific computing to high school students from underrepresented & underserved populations from across Iowa. (2) In introductory astronomy courses, the CAL will produce visualizations of astrophysical processes, which will make complex processes understandable on an intuitive, visually based learning level. (3) Finally, the CAL will provide graduate students with a hands-on approach to learning difficult concepts in computational physics via source code and tutorials. 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

Maize breeders have significantly increased crop yields by optimizing plants for higher planting densities. Further improvements in crop productivity per unit of land can be achieved by modifying the structure of individual plants and their arrangement in fields. Our current technologies allow for scanning large quantities of plants, but the acquired data is often underused. The goal of this project is to use the scanned data to create a virtual model of maize (its digital twin), which will capture the maize geometry, reflectivity, and function. The digital twin will be able to simulate real plant growth and response to the environment by careful verification against the measured data. The digital twin will be used in hypothetical scenarios of changing climatic conditions to answer "what-if" questions, providing answers for better plant architecture and planting distributions. By using AI and automatic optimization, this project will attempt to identify genetic markers and candidate genes governing variation in the same traits, enabling efforts to breed or engineer plants with optimal canopy architectures. This innovative approach will advance our understanding of plant biology and contribute to meeting global food demands. This project takes an important step towards in silico optimization of maize canopy architecture. We propose to develop innovative data processing and advanced visualization tools to generate fundamental knowledge applicable to agriculture to advance food needs. Our tools will reconstruct maize into its digital twins (plant ideotypes), simulate configurations of individual plants and plant populations differing in leaf canopy-related traits, and evaluate how plant traits perform in varying environments. We will use the vast amount of gathered data from phenotyping facilities and gantry to reconstruct 3D plants into their simulation-ready digital twins, fine-tune computer simulations to visualize and optimize the plant structure and function and identify optimal canopy architectures for given sets of conditions. This work will be combined with genome-wide association study for leaf canopy architecture traits derived from 3D reconstructions of real populations to identify markers and candidate genes, enabling efforts to breed or engineer plants producing optimal canopy architectures. The results of this work will strongly impact agronomic and plant genetic research in both the public and private sectors. There is a critical need for models to predict how plant varieties will respond to different environments. The 3D interactive application will allow experimenting with complex situations at interactive frame rates on a standard desktop computer, something never achieved before. It will be connected to existing data pipelines that provide vast amounts of (often unused) data. We will develop a set of novel algorithms that reconstruct 3D maize plant shapes and functions from input data from varying sources (RGB, depth, point clouds). The developed system will also generate synthetic data suitable for AI training (labeled sets of plants and 3D geometries with proper lighting). The project will partner with The National Data Mine Network, an NSF-funded initiative and the Computer Science department at Purdue University to engage and recruit students in phenotyping, data analysis, algorithmic design, and deployment. 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

Variants of the celebrated Boltzmann equation can be used to model the dynamics of rarefied gases (i.e., collections of molecules that move around in space and interact through collisions), as well as plasma (i.e., collections of positively and negatively charged ions that move around in space and interact through collisions and electromagnetic forces). As such, solutions of the Boltzmann equation can be used to describe and predict the dynamics in various applications, such as flow in microfluidic devices, hypersonic and space vehicle aerodynamics, flow in magnetically confined fusion reactors, and particle acceleration in laser-plasma systems. A critical challenge is that computing solutions to the Boltzmann equation in realistic scenarios is prohibitively expensive, even on modern massively parallel computers. An important goal of this research is to develop reduced-order models that can capture important flow features but that can be more readily solved on modern computer architectures. The approach pursued in this research is to decompose the solution into a macroscopic portion that describes large-scale features and a microscopic portion that describes smaller-scale features; macroscopic features can be computed relatively cheaply and accurately, while microscopic features are expensive to compute. Various adaptive strategies are explored to reduce the expense of the microscopic portions. The primary objective of this research is to develop accurate and efficient computational methods for solving the kinetic Boltzmann and Vlasov equation for modeling rarefied gases and plasma. The main challenge in solving kinetic models is that solutions live in high-dimensional phase space and contain information over wide-ranging spatial and temporal scales. An important goal is to develop reduced models that capture the important physics and can be more readily solved on modern computer architectures. The approach pursued in this research is based on decomposing the kinetic particle density function into macroscopic and microscopic pieces, allowing for different computational techniques on each portion. This research leverages several key innovations, including (1) high-order discontinuous Galerkin finite element methods for spatial discretization, (2) novel explicit and semi-implicit time-stepping techniques, (3) adaptive refinement strategies to reduce the computational expense of the microscopic portion of the update, and (4) implementation of the resulting algorithms on massively parallel computer architectures. Verification and validation will be performed on several test cases relevant to the simulation of rarefied gases and plasma. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research seeks to understand how fish achieve rapid maneuvers, a capability that surpasses even the most advanced robotic systems. The work focuses on a central hypothesis: that fish use their muscles to dynamically control their body stiffness, the resistance to bending, and, more crucially, damping, the resistance to the speed of bending, a phenomenon that enables them to navigate complex and unpredictable aquatic environments. To test this hypothesis, the investigators will conduct experiments on swimming fishes and measure the mechanical properties of their bodies and isolated muscles, alongside parallel tests using a custom biorobot platform. This synergy between biological and engineering approaches will help us understand whether fish execute fast accelerations and rapid turning maneuvers by dynamically modulating body damping and stiffness. This research will also deepen an understanding of how fish maneuver, including their behavior, the biomechanics of their bodies, and how they interact with the water around them. The findings will also enable an understanding of how different fish species are specialized for different swimming and adapted to different environments. The research will pave the way for developing extremely agile biorobots, unlocking complex missions previously inaccessible, such as nearshore environmental monitoring, detailed inspection of underwater offshore infrastructures, and non-intrusive studies of ocean biodiversity. By integrating biological insights with robotic design, the research will engage the public and educate future scholars from K-12, highlighting the shared physics underlying fish movement and biorobot design. Fish can turn and accelerate faster than even the most advanced biomimetic robots. Prior work has attributed this extreme agility to the ability of fish to modulate their body stiffness (the resistance to bending), but this has produced limited results in biorobotics. The researchers argue that the modulation of damping (the resistance to the rate of bending) is crucial for performing extreme maneuvers. Preliminary data from mathematical models and swimming experiments suggest that fish cannot achieve agile maneuvers without muscle-induced damping modulation. The research will examine this modulation by conducting experiments on swimming fish and parallel tests using an advanced biorobot. In vivo swimming experiments will measure swimming performance and muscle behavior, which will be used to perform in vitro tests for measuring muscle power and body flexibility. Biorobotic experiments will measure the effects of dynamic tuning of body damping on maneuvering performance, energy dynamics, and fluid flow patterns. By integrating our robotic and biological findings, the researchers aim to demonstrate that dynamic damping is essential for the extreme maneuverability crucial to the survival of fish. This interdisciplinary research not only paves the way for developing highly maneuverable biorobots but also inspires future BioDesign innovators to move from simple biomimicry to innovations grounded in biological and physical principles. 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

Modern scientific research relies on processing large amounts of data using programs that perform hundreds or thousands of operations in parallel. To make it easier to write these programs, scientists use tools called compilers that automatically translate ordinary, one-operation-at-a-time (“sequential”) programs into massively parallel ones. But these translations may be unreliable: Parallel programs are hard to understand and may suffer from unpredictable interactions. In addition, cutting-edge research in the field is leveraging large language models (LLMs) to generate parallel output, and these models come without any guarantees of correctness. This project aims to improve the reliability and the performance of parallelizing compilers by automatically checking that translated code has exactly the same functionality as the original code. This will involve precise modeling of the behavior of both sequential and parallel programs, developing a tool that can compare sequential and parallel programs, and mathematically proving that the tool’s output is always reliable. If a translation passes the check, the translated parallel code is guaranteed to behave in the same way as the sequential code. This project will improve the correctness of parallel programs across many fields of experimental science, increasing the pace of scientific advancement and reducing the harm from incorrect conclusions based on computational errors. The project will also support the training of the next generation of compiler writers and researchers, who will build more efficient and reliable tools for future scientific computing. As scientific researchers gain access to more powerful computers and larger amounts of data, they become increasingly reliant on correct and efficient parallel computing. Bugs in parallel code can lead to incorrect computations and misleading models -- and waste time, electricity, and money needed to run large-scale computations. The project will involve testing on a real-world LLM-based parallelizing compiler, to show that the tool can guarantee the correctness of its translations, as well as produce hints that help the translator produce more correct and efficient code. 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 This project aims to address the significant problem of bacterial pneumonia in goats, which is a major cause of morbidity and mortality and represents a significant welfare challenge for operations in the United States. Currently, there is only one FDA-approved antibiotic for the treatment of respiratory disease in goats, which requires daily administration. That decreases efficacy in situations where owner compliance with labeled instructions is lacking. To address this problem, the proposed research aims to determine an appropriate withdrawal period following administration of Draxxin® 25 in goats except breeding and lactating goats. The expected outcome is to have quantified data from incurred liver and injection site tissues that can be utilized to determine a meat withdrawal period. Following completion of this work, we will have completed all the work associated with all the necessary technical sections which we be able to submit to FDA CVM for consideration for a label approval. The label approval of Draxxin® 25 will make it easier for veterinarians to use tulathromycin in goats with confidence in scientifically based labeled slaughter withdrawal times.

NSF Awards · FY 2024 · 2024-09

This award supports research in commutative algebra – the study of the set of solutions of systems of multi-variate polynomial equations. Specifically, the project involves the study of free resolutions and Koszul algebras. Free resolutions are technical objects that allow us to approximate complicated algebraic objects by simpler ones. They can often be computed using computer algebra systems such as Macaulay2. Koszul algebras have particularly nice free resolutions and arise in a surprising number of contexts, especially in geometry and combinatorics. As part of this project, the PI seeks to classify certain Koszul algebras in several specific areas of interest. More broadly, the PI will supervise the training of graduate students and postdoctoral fellows. The PI will also begin work on a new textbook on commutative algebra with Macaulay2. A free resolution of a module over a commutative ring is an acyclic sequence of free modules whose zero-th homology equals the module. In the graded setting, resolutions are unique up to isomorphism and encode useful information about the module being resolved. Koszul algebras are graded algebras over a field such that the field has a linear free resolution over the algebra. The PI seeks to establish new classes Koszul algebras related to hyperplane arrangements (via Orlik-Solomon algebras), lattices and matroids (specifically Chow rings and graded Moebius algebras), toric rings (specifically matroid base rings, in connection to White’s Conjecture), and binomial edge ideals. Additionally, the PI will study the Eisenbud-Goto Conjecture in the normal setting, where it is still an open question. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This renewal three-year REU Site: Biological Materials and Processes (BioMaP) is hosted by Iowa State University. The focus of research is the cross-disciplinary areas of biological materials and biological processes within chemical and biological engineering. Ten participants each year will engage in ten-week summer research where they investigate the interface between biology and the design of materials with unique properties and the fundamental biological processes. Undergraduates will engage in cutting edge research in the areas of drug delivery, computational design of biomolecules, probiotics, gene editing, biomanufacturing and sustainable polymers. Students will participate in cohort activities such as short courses, lab meetings, seminars, and workshops. At the end of the program, REU students will present at a campus-wide poster symposium and will be encouraged to present their work at national meetings or in published articles. This project will focus on a new effort called Ideas to Impact, which features professional development activities that engage students on how to use the science and engineering that they are practicing for the good of society. Specifically, students will learn: communicating scientific results effectively (Data Analysis and Presentation), translating a research idea into intellectual property (Technology Transfer), and leveraging the results of research into a business opportunity (Entrepreneurship and Project Management). This renewal three-year REU Site: Biological Materials and Processes (BioMaP) is hosted by Iowa State University. The focus of research is the cross-disciplinary areas of biological materials and biological processes within chemical and biological engineering. Ten participants each year will study the interface between biology and materials (i.e., the design of robust materials with unique properties, activation of immune cells, the migratory behavior of cells, and the dynamic behavior of aptamers) and biological processes (i.e., processes critical to the future of the chemical industry, microbial robustness, probiotic engineering, and modeling photobioreactors). The REU students will be active members of multidisciplinary groups and will interact with faculty, post-docs, and graduate students. Students will participate in cohort activities such as short courses, lab meetings, seminars, and workshops. At the end of the program, REU students will present at a campus-wide poster symposium and will be encouraged to present their work at national meetings or in published articles. Professional social media will be used to develop networks across cohorts and research projects to enhance BioMaP’s long-term impact on participants, especially underrepresented groups. Our Ideas to Impact framework will teach students about technology transfer and entrepreneurship. The experience of working in an intensive research environment, where students will progress from dependent to independent contributors will enable the students to leave the program with life-long learning skills that will impact their contributions to science and engineering and to society. This project is jointly funded by the EEC REU and the Established Program to Stimulate Competitive Research (EPSCOR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Tensors, which naturally extend the concepts of vectors and matrices to multiple dimensions, have become ubiquitous due to the accessibility of numerous affordable and deployable sensors capable of collecting data on the same object or phenomenon from multiple perspectives. This proliferation is further accelerated by the powerful and flexible models that tensors can provide for representing multi-attribute data and multiway interactions, rendering tensors indispensable in modern data science across various fields of science and engineering. Among various applications, a fundamental task is to estimate tensors from highly incomplete measurements. This challenge emerges due to the exponential growth in the number of potential viewpoint combinations or multiway interactions, while our data collection capability increases only polynomially. Even if sufficient data could be collected, the amount of data may overwhelm the computational and storage resources of a single machine. Fortunately, in many practical applications, tensors often obey certain low-dimensional representations. This project will exploit these representations to address key challenges in modern data science across a spectrum of scenarios, spanning from centralized to distributed settings, while also accounting for the presence of maliciously perturbed measurements. By advancing the field of tensor analysis, this project has significant potential benefits across diverse areas such as signal processing, biomedical imaging, machine learning, and quantum information science. This project will develop a unified framework for exploiting low-dimensional structures for tensor recovery from incomplete measurements. To overcome computational challenges for large-scale tensors, this project will develop computationally and statistically efficient optimization methods that directly optimize over the low-dimensional structures (or factors in various tensor decomposition models). Our work will first study the stable embeddings of low-dimensional tensors from random yet structured linear measurements to determine the number of measurements needed for a stable recovery. This project will then leverage the stable embedding results to characterize the geometric landscape of the factorized nonconvex problems over the low-dimensional structures. The geometric landscape analysis will enable us to develop efficient local search algorithms with guaranteed convergence to the target tensors. In cases where privacy is a concern or the measurements overwhelm the computational and storage resources of a single machine, this project will enhance the algorithms with distributed optimization techniques that offer similar convergence and recovery guarantees, along with consensus guarantees. 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

Designing and deploying artificial intelligence (AI) tools in agriculture represents an exciting opportunity for international collaboration, uniting diverse expertise and resources to tackle global challenges. Our project aims to impact agriculture by developing, deploying, and democratizing AI tools to help farmers manage pests and stressors more effectively, making farming less risky, more profitable, and more sustainable. We plan to create AI-driven tools that provide personalized management advice, enhance crop yields, and support sustainable farming practices. This initiative will bring together scientists and practitioners from the US, India, and Japan, fostering international collaboration and innovation. The AI-driven approaches will benefit small and medium-sized farmers, offering easy-to-use, accessible technology to help them pursue climate-smart agriculture. The project also includes educational components and multilateral engagements to inspire the next generation of agricultural and AI experts. This EAGER project seeks to pursue multilateral research partnerships between the US, India, and Japan to develop and deploy AI-driven tools to enhance agricultural productivity. This team will work across two areas of collaborative effort: (i) developing hybrid machine learning models that combine sensor (proximal and remote) data with biophysical knowledge for yield and stress prediction, and (ii) utilizing agronomic data -- both biotic (insects, weeds, diseases) and abiotic (nutrient deficiencies, herbicide injury) -- to fine-tune and deploy large vision and language models developed by AIIRA (one of the five NIFA-funded National AI Institutes) in the US, led by Iowa State University (ISU). By collaborating with international partners that span diverse environments, we aim to develop and validate a robust, scalable framework for agricultural management that supports real-time decision-making and fosters sustainable agricultural practices globally. The initiative also emphasizes educational outreach, promoting interdisciplinary learning and broadening participation in AI-driven agriculture. This project is funded as part of the Quad AI-ENGAGE initiative, a collaboration of the National Science Foundation, Commonwealth Scientific and Industrial Research Organization of Australia, Indian Council of Agricultural Research, and Japan Science and Technology Agency to advance innovation to empower next generation agriculture. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project uses two different methods to study borehole breakouts, which are features formed in wells drilled to obtain oil, gas, and water. These features can be detected as distortions in the shape of the well’s circumference. These distorted shapes are used to understand stresses within the Earth’s crust, including in California near the San Andreas fault, which is important for estimating earthquake hazards. However, the inference of stress from breakouts is challenging when the well goes through strongly layered rocks. The first method for this project is a laboratory approach, where breakouts are created in analog materials such as sand, gelatin, and wax and the layering conditions can be controlled. The second method is a study of information collected at the time wells were drilled to look how breakouts across an area relate to rock layering. The team will develop new statistical tools to compare datasets from the project, which may prove useful in many other situations. The project will involve the training of one graduate student and multiple undergraduate students as well as curricular materials for high school students. Borehole breakouts are used to infer stress directions in the shallow crust. By assuming rocks are homogeneous and isotropic, breakout directions are inferred to form parallel to the minimum horizontal stress. However, the rocks in which breakouts are commonly found are often layered mudstones, shales, and sandstones in sedimentary basins, all of which are anisotropic. This proposal uses two research methods to study how anisotropy, due to rock layering, impacts stress inferences from breakouts. The first approach relies on physical experiments to investigate how and when anisotropy—in the form of layer orientation, layer thickness, and material strength—may impact breakout directions. The advantage of an analogue modeling approach is that the problem can be simplified to isolate a variable of interest, namely, the orientation of layering versus the applied stresses. The second approach relies on the analysis of well logs, seeking to amass a rich dataset of breakout azimuths, local bedding directions, and lithologic information from a natural system—in central California—to look for patterns between these datasets. Breakout data from this region provided critical support for the “weak-fault hypothesis” for the San Andreas fault, reflecting the importance of studying this region. Preliminary re-analysis of published data shows it is not straightforward to replicate the prior results. Further, breakouts are often parallel to bedding strike or dip direction, suggesting that local anisotropy may play a larger role than previously recognized. Statistical tools will be developed to analyze data from both projects because the data types are not straightforward to compare. The experimental results will be leveraged to better understand the conditions in which anisotropy might impact breakout directions from the natural dataset from California, which may help identify breakouts that reflect local conditions like layering rather than regional stresses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY The human oral microbiome is home to a unique group of bacteria with the ability to affect the overall health of the human host: Streptococci spp. This group is involved in a wide range of diseases, from dental caries to infective endocarditis. They include important oral pathogens such as the cariogenic Streptococcus mutans and commensal organisms such as Streptococcus mitis. These important oral members produce a large array of radical-S-adenosylmethionine ribosomally synthesized and post-translationally modified peptides (RaS-RiPPs). The newly identified compounds include a wealth of chemical structures and have been found to inhibit the growth of other oral Streptococci, as well as modulate the behavior of producer organisms themselves. The initial goal of this proposal is to elucidate the mechanism of action of the RaS-RiPP tryglysin from S. mutans, and to examine its impact on the functional oral microbiome. Upon defining tryglysin’s mechanism of action on oral Streptococci and the oral microbiome, this study will be expanded to examine the biological significance of other RaS-RiPPs produced by Streptococci: streptosactin, suisactin, rotapeptides, and NxxC family peptides. While the chemical structure and synthesis of these peptides has been defined, the biological significance of these identified RaS-RiPPs is unexplored. Given the important status of Streptococci spp. to the health of the human oral microbiota, this represents a major gap in knowledge about a class of peptides with huge potential impacts on overall oral health. This proposal aims to create a research platform for studying RaS-RiPPs from oral Streptococci, defining their function, and examining shifts in the functional oral microbiome in response to their production. Executing this research program at the University of Illinois at Chicago (UIC) will allow for the establishment of the candidate as an independent researcher and provide avenues for the achievement of the candidate’s career goals: establishing a productive and first-rate research laboratory and securing a tenured faculty position at a major research institution.

NSF Awards · FY 2024 · 2024-08

Monolayered 2D materials have a thickness of less than 1 nanometer. When they are stacked to form homogeneous junctions and heterogeneous junctions with various twist angles, new exotic properties and functionalities will arise. Relevant applications include microelectronics and sensor development. Although computer modeling has uncovered very strong twist angle effect on the interlayer thermal conductance at homo- and heterojunctions, no experimental work has been reported yet. This is due to the extreme experimental challenges in measuring the temperature difference and heat flux across a homo- or heterojunction. The project is designed to overcome these extreme challenges and provide the first-time experimental understanding of twist angle effect on interfacial thermal conductance of homo- and heterojunctions. New Raman techniques with specifically designed energy transport states will be used. The overall project will involve extensive training of graduate and undergraduate students and feature tight integration with education and outreach to K-12 graders. The goal of the project is to investigate how the twist angle affects the interlayer thermal conductance and junction-substrate interfacial thermal conductance of 2D homo- and heterojunctions, investigate the effect of temperature, and provide deep physics understanding about the twist angle effect via atomistic modeling and machine learning. This project represents the first high-accuracy investigation about the twist angle effect on the thermal conductance of homo- and heterojunctions of 2D monolayers. The outcome will significantly advance current knowledge that is solely developed by modeling and theoretical analysis. It will uncover how the twist angle influences the interfacial thermal conductance, identify the angles that give the highest and lowest thermal conductance, and unravel how temperature variation will change this effect. The knowledge to be developed in this project will substantially advance the scientific understanding and provide the critical knowledge for material and device design in 2D material-related microelectronics and sensors. The broad impact activities are designed to significantly expand undergraduates' view of modern science, stimulate their interests in research, and expose K-12 graders to nanoscience and technology, especially in energy transport and control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Internet of Things (IoT) sensors-based monitoring enables data-driven decision-making in ‘smart’ Building/City/Factory/Agriculture/Healthcare settings, optimizing energy/resource utilization and minimizing the carbon footprint. One challenge with their wide adoption is limited battery life, requiring manual battery replacement, some of which could be in hazardous areas. This proposal aims to introduce and develop a fundamental realizable limit (FRL) power output vibration energy harvester to support milliwatts level of power requirement of IoT devices, offering a maintenance-free and green batteryless alternative, addressing the existing challenges, namely, (i) Narrow bandwidth operation, requiring harvester’s fine tuning per the source vibration, and (ii) Low energy conversion efficiency. The proposed harvester will offer a ‘plug & play’ universal solution for real-world vibration energy extraction at FRL, far surpassing the state-of-the-art energy efficiency. The developed technology will be for two different settings of harnessing machine versus structural vibrations for their remote monitoring, generating novel battery-free IoTs (for milliwatt level systems) with advanced application-specific integrated circuits (ASICs), contributing to ongoing national mission to strengthen semiconductor manufacturing and design ecosystem. The educational impact of the project will be through workforce training in cutting-edge areas of energy harvesting system and ASIC design, and STEM outreach activities at K-12 level. The specific contributions of this proposed research are (i) Theorizing the Fundamental Realizable Limit (FRL) energy output as a function of vibration energy harvester parameters and input excitation characteristics; (ii) Developing mathematical approach to design a vibration energy harvester with self-tuned non-linear optimal displacement trajectory so as to attain FRL energy transduction irrespective of external excitation profile; (iii) Novel mathematical development of the interplay between the mechanical and electrical forces in a transducer for the co-design of an optimal electro-mechanical strategy for FRL operation and subsequent energy transfer to any desired electrical-load; (iv) A new Bayesian inferencing algorithm to realize ‘causal’ FRL operation for irregular vibrations having local extrema; v) Demonstrate FRL transduction strategy on piezoelectric-cantilever-based harvester by an integrated on-chip controller, with a novel switched energy extraction circuit to offer a dynamic optimal electrical loading to maximize transduction and fully transfer the transduced energy to the onboard storage; (vi) A mathematical framework to quantify the effect of various system design parameters on the energy conversion efficiency; (vii) Developing general design guidelines for harvester integrated IoTs by prototyping for self-powered machine- as well as structure-health monitoring applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project addresses alarming concerns raised by growing plastic pollution observed in rivers around the world. Microplastic particles, with sizes typically below 100 microns, account for over 80% of the plastic waste in rivers. A large amount of microplastic particles ends up within the pores of the bottom riverbed. This leads to the contamination of aquatic habitats and the food chain, as the small size of these particles (about the same size as plankton) makes them easily ingested by fish, oysters, and other animals. Despite these concerns, the processes controlling the trapping of microplastic particles in riverbeds are not fully understood. While effects related to turbulence at the interface between river stream and sediment bed, particle inertia, and biofouling-induced sticking are expected to have a large impact on the retention of microplastic particles within riverbed pores, there are limited studies that investigate these effects systematically. These knowledge gaps will be addressed in this research. Using high-fidelity numerical simulations and theoretical modeling, this project will reveal the physical processes involved in the trapping of microplastic particles in riverbeds and build a reduced-order model to predict the trapping rates efficiently and accurately. This research will enable a more accurate assessment of the impact of microplastic pollution on ecosystems and inform potential remediation strategies, such as new filtration methods implemented near sources of pollution (e.g., in wastewater treatment plants). Further, by involving undergraduate researchers hired from the diverse pool of students at Arizona State University (a Hispanic-serving institution) and Iowa State University, this project will support diversity in engineering and promote teaching, training, and learning. The goal of this project is to reveal processes that control the trapping of microplastic particles (MP) in riverbeds and model the trapping rates. The research leverages synergistic Pore-Resolving Direct Numerical Simulations (PR-DNS) and theoretical modeling. PR-DNS of MP-laden turbulent flow over a model riverbed will be carried out to reveal the microscopic effects at the pore-level. Scaling laws for the MP effective diffusivity that account for stream turbulence, MP inertia, and sticking will be extracted from simulations. Predictive models for the MP retention rate will be derived using Population Balance Modeling and simulation data. These models will incorporate turbulence and inertial effects from first principles. The combined numerical and modeling work will make it possible to predict the retention rates of MPs for a wide variety of flow, bed, and particle configurations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Elucidating Context-Specific FERONIA Receptor Kinase Signaling Project Summary/Abstract Understanding how cellular signaling pathways achieve specificity is an important question in developmental biology. FERONIA (FER) receptor kinase is universally expressed and a critical regulator in plant growth, development and stress responses. Loss-of-function fer mutant has pleotropic effects with reduced plant growth, fertility and altered responses to stress. The goal of the proposal is to understand how FER signaling achieves specificity using root as a system in genetic model plant Arabidopsis. The primary roots offer both spatial and temporal information of root development, and our preliminary single cell RNA-sequencing (scRNA- seq) showed that FER-regulated gene expression in root is high cell type-specific, which makes root an ideal system to study FER signaling specificity. Multiple lines of evidence indicated that FER phosphorylates transcription factors to regulate gene expression and various biological processes. First, recent multiomics study demonstrated that FER regulates the expression of thousands of genes including large number of transcription factors (TFs), suggesting that transcriptome reprogramming is critical for FER-mediated signaling. Second, it has been demonstrated that FER phosphorylates transcription factors, MYC2 and ABI5, to regulate plant immunity and seed germination, respectively. Third, scRNA-seq analysis with fer mutant revealed that FER affects the expression of ~3,000 genes in roots in a highly cell type- and developmental stage-specific manner. Fourth, AKS (Abscisic acid-responsive Kinase Substrate) family of TFs are FER kinase substrates. Further genetic and functional data showed that they are major transcription factors functioning downstream of FER and FER is required for their transcriptional activity. AKS genes have cell type- or developmental stage- specific expression patterns while FER has a more universal expression, suggesting that FER modulates these TFs in different cell types in regulating their target gene expression and FER signaling specificity. Based on these results, it’s hypothesized FER phosphorylates and modulates the AKS TFs in specific cell types and developmental stages to regulate their target gene expression in achieving FER signaling specificity. scRNA- seq analysis, tissue-specific gene manipulation by CRISPR/cas9 and complementation, target gene identifications and transcriptional network analysis are proposed to test the hypothesis. In Research strategy section1, the genetic, genomic and functional interactions of FER and AKSs will be characterized by constructing genetic mutants, phosphosite mutations and identifying cell type-specific transcriptional targets. In Research strategy section 2, scRNA-seq of fer and aks mutants under control and salt treatment will be performed. In addition to the cell type-specific FER CRISPR knockout mutants we have already successfully constructed, we will also generate cell type-specific FER complementation lines. Further characterization of the cell type-specific CRISPR knockouts and complementation lines along with the gene regulatory network analysis will provide unprecedent new insights into how FER achieves signaling specificity.

NSF Awards · FY 2024 · 2024-08

Every day, people use scientific information to make decisions that affect their lives. Consequently, it is critical that the results of scientific research on topics impacting the public are communicated effectively. Data visualization is an important part of scientific communication, yet much guidance on the clear and effective design of data graphics has not been tested among a representative population of U.S. adults. The researchers examine how accurately users interpret the information conveyed in different types of data visualizations. The findings support evidence-based recommendations that aid in the communication of scientific information across the United States. This research advances understanding of data visualization as a method of communication and provide comprehensive data on viewer interpretation and understanding of data graphics among the adult U.S. population. The researchers implement an online survey through a web portal to 2,000 respondents from a probability-based, nationally representative panel of U.S. adults. Respondents are shown multiple types of data visualizations with various design elements, such as bar and line charts with varying levels of supporting context and asked questions that measure their understanding and interpretation of the data presented. Survey questions then ask about basic interpretation of the values presented in a chart and open-ended responses about what conclusions respondents can draw about the data shown. The researchers analyze the data using generalized linear models, natural language processing, and text analysis techniques to determine the effect of different elements of data visualization design, such as the type of chart or supporting context, on user understanding, and the extent to which understanding may vary across demographic groups. The project is co-funded by the Science of Science: Discovery, Communications, and Impact and the Advancing Informal STEM Learning Programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

2425024 Martin NON-TECHNICAL DESCRIPTION: This project is conducting research on new glasses than can be used in the development of new batteries that are safer and more energy dense than those currently available. While lithium batteries are powerful, they present a dangerous fire hazard that have resulted in many injuries. In this project, new glasses are being prepared and studied that will help create a new type of all solid-state battery that substitutes the hazardous liquid component in and can hold up to 10 times more energy than lithium batteries. New sodium-based glasses that mix (cation) glass formers and glass forming anions will be prepared to form new glassy solid electrolytes that are expected to have very high Na+ ion conductivities and be chemically and electrochemically stable. Furthermore, by being based on Na, these new all solid-state sodium batteries have the potential to be significantly cheaper. New Graduate and undergraduate students working on the project are learning how to conduct battery research, work in international multi-disciplinary research teams, and present and discuss their research findings. Students supported by this project will conduct informal science education to K-Gray audiences numbering more than 2500 each year by using the Iowa State University Gaffers Guild Glass Blowing Studio. TECHNICAL SUMMARY: At the very core of every lithium battery is a highly flammable organic liquid electrolyte. When a lithium battery is overcharged, overheated, or draws too much current too rapidly, the organic liquid electrolyte can catch fire and cause serious injury. In this new project, fundamental research is being conducted to study new kinds of sodium-based mixed glass former (MGF) mixed oxy-sulfide-nitride (MOSN) glassy solid electrolytes (GSEs). The project will examine the hypothesis that high Na+ ion conductivity GSEs can be developed by carefully studying the underlying materials chemistry of mixing silicon and phosphorous glass formers and mixing oxygen, sulfur, and nitrogen anions. In turn, this understanding of the fundamental causes of the much lower Na+ ion conductivities compared to Li+ ion conductivities will create fundamental, foundational, and transformative knowledge to enable the development of new Na-based GSEs that have Na+ ion conductivities that meet and even exceed those of Li+ ions. This hypothesis will be explored by conducting basic research along two synergistic and parallel pathways: (1) The preparation and characterization of these never-before-prepared Na-based MGF MOSN GSEs, and (2) The fundamental study of Na+ ion conduction in these MGF MOSN GSEs to develop foundational and transformative knowledge of Na+ conduction mechanisms and their associated conduction pathway energy barriers. This research project trains graduate and undergraduate students in state-of-the-art glass synthesis, materials characterization, and solid-state electrochemistry of GSEs and as such broadens the cadre of new knowledge workers in the critical field of energy storage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Emerging plant diseases can incur economic and environmental costs by leading to epidemics or the persistent appearance of low levels of new diseases. Identifying the risk factors favoring the emergence of plant pathogens is important to predicting, monitoring, mitigating, and managing diseases. The transmission of bacterial plant pathogens by beetles may pose an unrecognized, yet major, risk for disease emergence because beetles pick up and spread bacteria with relative ease, namely by feeding with subsequent fecal transmission. Beetles also harbor bacterial species that are well adapted to both the insect gut and plant environments, and they are prone to invasiveness, geographic range expansion, and pathogen spread among multiple plant species due to often indiscriminate feeding. The goal of this project is to understand the factors driving the adaptation, persistence, and emergence of beetle-vectored bacterial pathogens. As a study system, the project will use a bacterial wilt pathogen of commercially important cucurbit species (e.g., cucumbers, squash and muskmelons), beetles that transmit the pathogen, and bacteriophages that kill the pathogen. The project will explore mechanisms influencing the short-term evolutionary potential of the pathogen, and mechanisms driving change in present-day populations, by examining genetic bottlenecks, barriers to acquisition and spread by beetles, and genomic variation in native populations in plants and beetles. Based on the discovery of pathogen-specific bacteriophages within beetles, the project will also explore integrating bacteriophages into strategies to manage beetle-transmitted bacterial plant pathogens. Collectively, the results will provide critical data to help assess the emergence risk for such pathogens. Insects are responsible for vectoring some of the costliest bacterial diseases of crops. Although the best studied among these are the piercing-sucking hemipterans, non-hemipterans such as beetles may pose a higher risk for disease emergence due to their comparatively simple mode of pathogen acquisition and transmission. To understand the ecological and evolutionary processes impacting non-hemipteran-vectored bacterial pathogens, this study will use the wilt pathogen Erwinia tracheiphila, its cucumber beetle vectors, virulent bacteriophages, and cucurbits as a model system. The first aim is to evaluate the impact of vector–plant and plant–vector transmission events and aggregative beetle feeding on genetic variation within pathogen populations. Experiments will use neutrally barcoded pathogen libraries to track strain-level population dynamics under natural routes of transmission. The second aim is to map patterns of genomic variation in wild pathogen populations in plants and beetles in the field, including during overwintering in beetles. Experiments will use whole-population whole-genome sequencing to investigate the molecular mechanisms driving genomic changes. The final aim will interrogate bacteriophage acquisition, persistence, and predation in beetles, fitness tradeoffs that may accompany bacteriophage resistance, and the potential for beetle-mediated bacteriophage-based pathogen control. These studies will generate some of the first quantitative data characterizing bacteriophage–pathogen dynamics for a vector-transmitted phytopathogen. The societal significance of this project is its translational relevance to predicting emergence and managing insect-vectored bacterial diseases, and its educational relevance in promoting science literacy and public awareness of the importance of plant–microbe–insect interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

NON-TECHNICAL SUMMARY This award supports theory and computation, as well as education to advance the engineering of nanoparticle based materials. Nanoparticles are solids consisting of a small number of atoms, in such a way that at least one of the three dimensions is of the order of a few nanometers (1nm=10-9 m). Quantum dots, for example, are a type of nanoparticle that emits light in different colors depending on their size. Over the last decade a new paradigm for materials engineering has emerged, where instead of directly assembling atoms or molecules into materials, first nanoparticles are made, and then, the nanoparticles themselves are assembled into functional materials. This allows an unprecedented level of control, but also, the ability to design materials with new functions, with potential for game changing applications in energy harvesting technologies, quantum information, new display devices, as well as medical imaging, just to name a few. This project aims at providing a robust platform for designing nanoparticle based materials. Driven by the ability to synthesize large numbers of nanoparticles with diverse sizes, composition and the implementation of a broad range of strategies to assemble them in actual materials, the field has had an extraordinary experimental progress over the last decade. What is needed to accelerate this process of discovery is a comprehensive computational/theoretical framework that will identify what combination of design parameters will lead to materials with new functions. The project aims to fill this gap by developing a general framework (mean-field model), complemented with numerical simulations that will enable the prediction of structure and in this way, a rational exploration of the optimal parameters for designing new materials. The developed framework will be made available through a software package for the benefit of the entire community. Broader impacts of the proposed research will continue and strengthen a collaboration with the SUCCESS program in the Des Moines school district, which identifies kids transitioning into middle school that are at risk of dropping out of school. It also includes developing material for teaching nanoscience with a strong emphasis involving undergraduates in research, as well as engagement by the project personnel in graduate and undergraduate training in theoretical and computational nanoscience. Theoretically-oriented students will be exposed to broader soft materials disciplines through a close coupling with experimental groups at Indiana University and ETH-Zurich in Switzerland, as well as with the University of Buenos Aires in Argentina. TECHNICAL SUMMARY This award supports theory and computation, and education to advance structure prediction in functional nanomaterials. The project aims to develop a comprehensive mean field model, complemented with simulations that will provide a first principles prediction of structure. Preliminary results have shown that the proposed approach successfully predicts the known phenomenology in single component systems consisting of spherical-like nanocrystals functionalized with simple alkane ligands, so the framework will be extended to other nanoparticle shapes, multicomponent systems and more general ligands, and will be made available to the broader community, with the expectation that it will become a routine tool for structure prediction similarly as density functional has become for atom based materials. Collaboration with the group of Mario Tagliazucchi at University of Buenos Aires in Argentina will enhance the capabilities of the mean field model. Experimental work in the group of Xingchen Ye at Indiana and Maksym Kovalenko at ETH Zurich will be specifically developed to rigorously verify the theory. The main broader impact activity of the proposed research will consist in developing activities for the SUCCESS program of the Des Moines School district transitioning from 5th grade into middle school. The students will make regular visits to the Iowa State Campus, and attend hands-on lectures on nanoscience through the year, thus making them familiar with the subject and equally important, get to experience a college campus. The program also includes extensive participation of undergrads in research, an area where the PI has had major success over the last decade. The PI will also develop course materials to enhance the knowledge and potential of nanotechnology. All participants will contribute to the vibrant education and outreach programs at Iowa State and the national and international collaborations that will result from this project. STATEMENT OF MERIT REVIEW This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

The overarching goal of this proposal is to create conditional gene animal resources for modeling human health and disease in zebrafish through improved genome editing tools. Zebrafish (Danio rerio) is second only to mouse as the most commonly published model system for human health. In 2023, NIH supports over 150 Principal Investigators in >200 Research and Resource Zebrafish projects (>$80 million) across 19 institutes. Zebrafish possess high fecundity for genetic studies and optically clear embryos for live imaging and visualization of developmental processes. Readily accessible methods have been established to modify gene expression or edit the genome by injection of reagents into the single cell embryo. Zebrafish is increasingly used for post-embryonic and adult models of disease and regeneration, requiring strategies for inducible genetic manipulation with tight spatial and temporal control across the lifespan. The ability to fully utilize this powerful animal model is limited by a lack of tools for tissue and cell-type specific gene functional studies, reproducible and accurate transgene expression, and reliable integration of nucleotide variants and short sequences into the nuclear genome. Epigenome regulation and energy metabolism are rapidly emerging areas impacting development, disease, and regeneration, yet few zebrafish genetic alleles are available to study either in zebrafish. Moreover, the ubiquitous expression of epigenetic and nuclear mitochondrial genes requires conditional alleles to model tissue and organ specific pathologies associated with these fundamental biological processes. For zebrafish to remain at the forefront of animal models that address the research mission of institutes across the NIH, innovative methods are needed for precision genome editing and expanding the conditional genetic toolbox to these understudies areas. Building on our pioneering strategies in zebrafish genome editing, the improvements and conditional genetics resources we propose to develop here will have a significant, lasting impact on zebrafish. We propose the following aims: Aim 1. Expand the zebrafish community resource for Cre recombinase conditional gene studies to established fields of zebrafish studies (embryonic development, major tissues and organ systems) and understudied, rapidly emerging areas (epigenetics, mitochondrial biology). Aim 2. Further develop and improve methods for precision targeted integration to increase the accessibility of our GeneWeld and DonorGuide methods for integration of long DNA fragments and short sequences. Aim 3. Continue development and distribution of targeted integration resources to the zebrafish community through seminars, conferences, social media, and virtual and hands on training. We expect by the end of the funding period to significantly expand our Cre and conditional allele resource. Our methods to improve GeneWeld and DonorGuide precision genome editing in zebrafish will be widely applicable, given the near universal application of CRISPR genome editing in model and non-model systems.

NSF Awards · FY 2024 · 2024-08

Many U.S. cities experience combined sewer overflows (CSOs) during wet weather. The resulting release of sewage and stormwater can have negative environmental and health consequences. Of particular concern are marginalized communities residing historically in flood-prone zones, which face heightened susceptibility to CSO impacts. Current urban wastewater systems were designed to withstand peak flows derived from outdated precipitation records. With climate change producing more frequent and intense rainfall, cities face urgent challenges to manage and mitigate CSOs in an equitable manner. This project will directly address these challenges by developing a model of the Des Moines, IA combined sewer system using real-world data. This system is similar to that of many other major U.S. cities, and therefore provides a framework for researchers to study the resilience of wastewater systems. By prioritizing equity in the scientific approach and proactively integrating future climate conditions, this research bridges the gap in system-level resilience assessment for wastewater systems, and will provide insights into the vulnerability of marginalized communities to CSOs amid climate change. This research aims to analyze the resilience of combined sewer systems in response to climate change and assess potential CSO exposures and impacts on marginalized communities. An integrated modeling framework will be created enabled by cutting-edge development in below-ground drainage modeling and it will be coupled with a qualitative-quantitative survey method to perform relevant, local phenomena-based research. The project is expected to 1) advance data analytics and modeling methodologies for urban wastewater systems; 2) assess system-level resilience of the test-bed system (i.e., Des Moines, IA) to wet weather conditions under the influence of climate change; and 3) uncover the vulnerability of marginalized communities to future likely CSO incidents. This research aims to catalyze equitable solutions in the management and mitigation of CSOs, fostering enduring benefits for marginalized communities. In addition, it will provide unique opportunities for graduate and undergraduate students to work collaboratively across universities and directly with industry collaborators, enabling a pipeline for both personnel and research to move more rapidly in and out of academia. This project is funded jointly by the CBET Environmental Sustainability program and the CBET Environmental Engineering 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.