University Of California Santa Cruz

NSF Awards · FY 2025 · 2025-04

This I-Corps project focuses on the potential commercialization of a miniaturized, integrated laboratory system that enhances tissue culture and preclinical research. This technology enables automated biological experimentation by combining microfluidics, imaging, automation, and electrophysiology within a cloud-connected framework. By improving experimental reproducibility, minimizing tissue disruption, and stabilizing cultures, the system addresses critical challenges in biological research. The platform facilitates remote access to experiments and standardized protocol sharing, promoting global scientific collaboration. The solution lowers barriers to advanced research techniques, making them more accessible to additional laboratories. The system's ability to generate physiologically relevant data enhances understanding of human biology, advancing drug discovery while reducing the reliance on animal testing. By automating complex biological workflows, the technology accelerates therapeutic development and enhances preclinical drug testing reliability. This innovation also supports workforce development by introducing user-friendly interfaces that enable training in advanced research techniques without requiring extensive technical expertise. By fostering accessibility, collaboration, and data-driven scientific advancements, this project contributes to the broader goal of improving human health through next-generation research tools. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an automated tissue culture system that integrates microfluidics, real-time imaging, and electrophysiological monitoring within a cloud-connected environment. By seamlessly combining biological experimentation with engineering advances, the system enables continuous, high-throughput data collection while minimizing manual intervention. The modular design allows for customization across various research applications, including drug discovery and disease modeling. Advanced data analytics, powered by artificial intelligence, provide deeper insights into drug-tissue interactions and cellular responses. This approach enhances experimental reproducibility, reduces variability, and improves the predictive accuracy of preclinical research. The integration of cloud computing and remote accessibility expands the potential user base, ensuring the technology can be widely adopted across research institutions and industry sectors. By improving data quality, automating workflows, and enabling real-time experimental monitoring, this innovation represents a significant step forward in preclinical research methodologies and therapeutic development. 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 · 2025-04

Geohazards pose large risks at geologically active continental margins. These geohazards are interconnected and thus difficult to study in isolation. The goal of this project is to bring together experts to develop plans for an integrated array of instruments to observe these hazards. The array will be designed using Chile as a case study. This is a unique location where frequent events and existing networks provide a global understanding of interacting hazards. Teams of experts in computer modeling and technical planning will design sub-arrays for earthquake, volcanic, and landslide observations. Teams will also compile new catalogs of earthquakes and landslide susceptibility in the study area. The teams will meet in a 3-day workshop to synthesize results. Broad input from the scientific community will be solicited through a series of webinars. New models and catalogs will be shared openly through the SZ4D website and data repositories to benefit communities exposed to subduction-related hazards in the U.S. and internationally. Subduction of ocean lithosphere results in the largest earthquakes, volcanic activity, and landscapes highly prone to destructive landslides. For decades research related to subduction and related geohazards has proceeded piecemeal. This research will provide the basis for an overarching framework for integrated studies that can directly address the linkages between earthquake, volcano, tsunami, and landslide geohazards. This award will support a series of modeling studies and technical planning that will be used to design three overlapping arrays of instrumentation at the Chile Subduction Zone. Chile is unique in combining a high level of geological activity and good logistical access. The instrument array will be designed to observe a broad range of earthquake, volcanic, and landslide processes. The work is organized into ten work packages. Five will assess and plan various aspects of the seismic detection and geodetic network. Two will address sediment and hydrologic transport for landslides. Two will address using seismicity to forecast volcanic processes. The final work package will bring together the others with a three-day workshop and with scientific community input via a series of webinars. The connection between this research and the SZ4D initiative makes very clear the connection of this planning activity to benefit people who live with subduction-related geohazards in the U.S. and globally. 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 · 2025-03

This award will support participation of early career scientists in Ribosomes 2025, this year's triennial international conference on ribosome research. The conference will be held at the Asilomar Conference Grounds in Pacific Grove, CA on June 22-27, 2025. Ribosomes are the molecular machines that translate genetic information carried in messenger RNA into protein in all living organisms, thus serving as the vital link between genotype and phenotype. The purpose of this conference is to bring together researchers from all over the world to communicate their latest findings to each other, and to stimulate critical discussion and promote collaboration. The gathering will provide a unique opportunity for graduate students and postdoctoral scholars to present new results of their research and to advance knowledge through engagement with peers and leaders in the ribosome field. Ribosomes are the largest, most complex asymmetric biomolecular structures, presenting many daunting challenges to scientists investigating their form and function in living systems. The conference will enable researchers to share expertise across many disciplines, including foundational knowledge and new technological developments that advance ribosome biology and the fields of protein synthesis and gene expression at large. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-03

PROJECT SUMMARY Biofilms, surface-attached microbial communities encased in an extracellular matrix, enhance the environmental survival, transmission, pathogenicity, and antibiotic resistance of pathogenic microorganisms. A key regulator of biofilm formation in bacteria is the broadly conserved nucleotide-based second messenger, cyclic dimeric guanosine monophosphate (c-di-GMP). c-di-GMP is produced by diguanylate cyclases (DGCs), degraded by phosphodiesterases (PDEs), and sensed by c-di-GMP receptor proteins. This proposal examines the molecular mechanisms and consequences of biofilm formation in Vibrio cholerae, the bacterium causing the disease cholera, an important public health problem worldwide. While interfering with biofilm formation might mitigate the global health impact of diseases like cholera, how c-di-GMP controls this process remains unclear. Our objective is to address major gaps in our understanding of the interplay between the flagellum, pili, and c-di-GMP in the motile-to-sessile switch, biofilm formation, and V. cholerae infection. Specific Aim 1 focuses on determining how c-di-GMP and specific DGC and PDE proteins modulate the torque-speed relationship of the sheathed flagellum in V. cholerae using biophysical approaches and novel reporters to quantify c-di-GMP levels. We will also examine how c-di-GMP receptor proteins, particularly PilZ domain proteins, regulate motility and biofilm formation through genetic, biochemical, and structural analyses. Specific Aim 2 examines the molecular mechanism(s) through which c-di-GMP controls the production and activity of the mannose-sensitive haemagglutinin type IV pili (MSHA), the primary mediator of initial surface attachment, which leads to biofilm formation, using structural and biochemical approaches. Specific Aim 3 dissects the role of c-di-GMP signaling in V. cholerae infection using state-of-the-art imaging tools and novel c-di-GMP sensors. This research promises molecular and mechanistic insights into c-di-GMP signal transduction pathways governing motility and biofilm formation, ultimately allowing us to devise ways to inhibit V. cholerae infection cycle. Understanding these mechanisms will also lead to new strategies for disrupting biofilms and controlling motility in other pathogens, thereby improving treatments for biofilm-related bacterial infections.

NSF Awards · FY 2025 · 2025-01

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). There is a strong record of the study of discrete structures leading to advances not only within mathematics, but also in science and industry. This project studies and develops discrete structures arising from the interplay between combinatorics and algebraic geometry. Additionally, the interdisciplinary potential of combinatorics is utilized in two ways. First, to study the complexity of a model from machine learning using a recent geometric rephrasing. Second, to develop an algorithm used to help identify novel complex genetic structures driving health and longevity outcomes in humans. At the same time, the project’s educational program will increase the participation in mathematics of those traditionally underrepresented, recruit talented students from underserved communities to Ph.D. programs in mathematics, and disseminate modern research in combinatorics. More explicitly, the objectives of the research component are to advance the theory of combinatorial subvarieties of the flag variety (namely Hessenberg varieties and Kazhdan-Lusztig varieties), propose a notion of duality for Newton-Okounkov bodies, and study applications of generalized permutahedra to machine learning. The broader impacts of the proposal include interdisciplinary projects that have the potential to benefit society and activities that broaden the participation of underrepresented groups in mathematics. The educational component of this proposal consists of a research experiences program for Washington University’s Joint Post-Baccalaureate Program in Mathematics and the combinatorics summer school ECCO in Colombia. The project will also support research by undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

In a changing climate, accurate and efficient Earth system models are needed to understand the impacts of climate change on atmospheric dynamics and extreme weather events - e.g., heat waves, floods, and droughts. This project proposes a mathematically rigorous method to develop AI-based stable and physically consistent global atmospheric models that can be reliably used to perform both short-term forecasting and long-term climate modeling at a fraction of the computational cost of current physics-based models, which may take years to develop otherwise. The proposed methodology leverages principles in deep learning theory and atmospheric physics to ensure physical consistency of the proposed AI models. Furthermore, the project aims to develop a framework for systematic evaluation of such physical consistency of AI-based models of the atmosphere that are grounded in physics, akin to traditional climate model development. This project contributes to a new paradigm of simulation sciences for geophysics, which is driven entirely through models trained on data. This would train and develop an interdisciplinary workforce that would perform cutting-edge research in atmospheric physics, scientific computing, and AI with a greater goal of addressing the pressing challenges of climate change. Current AI models are trained on reanalysis data and demonstrate prediction skills that outperform numerical weather prediction models. Despite their superior performance in predicting certain atmospheric variables at short time scales, their long-term performance degrades quickly, and all these models either show numerical blow-up, or unphysical hallucinations beyond 15 day or 20-day lead times. The reason for such unphysical behavior in these models is the inability to preserve physical consistency of the predicted states especially in the small scales as well as the inability to integrate the states in time by considering the numerical stability criteria of such integrators. Hence, these models fail to provide any scientific insight into the climate statistics which require one to integrate the states of the atmosphere for hundreds of years, e.g., to estimate the risk of extreme events with long return periods, low-frequency variability of key climate processes, or estimate the response to external forcing. We propose to develop a physically-consistent long- term stable deep learning-based atmospheric emulator that incorporates boundary conditions and integrates the states of the atmosphere for hundreds of years. The key innovations in enabling such an emulator are (a) the understanding of limitations of deep learning models in preserving small-scale physical consistency and then alleviating it through hard- and soft-constraints inside the architecture of the model in an architecture-agnostic fashion and (b) enabling stability analysis of these models akin to traditional numerical stability analysis to restrict the model’s fastest growing eigenvalues. Such emulators, which are 10000x faster than Earth system models, will allow us to seamlessly generate a large ensemble of integrated states of the atmosphere enabling rigorous estimates of extremes and their uncertainties, computing responses to external forcings, and more. This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the National Discovery Cloud for Climate initiative within the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering. 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 · 2025-01

This grant partially supports the participation of Ph.D. students from US institutions in the Doctoral Consortium at the 2025 IEEE Winter Conference on Applications of Computer Vision (WACV). WACV is the premier annual conference focused on the applications of computer vision. It is held in the United States and attended by members of the international research community. The goal of the PhD Forum is to highlight the work of PhD students working in computer vision and to give these students an opportunity to discuss their research and career options with senior researchers in the field. The broader impacts of this project include supporting the career development of some of the brightest junior researchers in computer vision, contributing to the research community in general by drawing attention to an important aspect of graduate student development, potentially increasing the number of active researchers and educators in STEM, and ensuring that the computer vision community, through its recent graduates, makes fast advances in solving problems that will benefit society as a whole. The Doctoral Consortium aims to have representation from a diverse group of participants in terms of gender, ethnic background, academic institution, and geographic location. Support from the National Science Foundation covers some of the costs for 17 selected US-based graduate students to attend the conference. The selection of participants receiving travel support is conducted by the 2025 WACV Doctoral Consortium Chairs. For graduate students, this event offers a chance to receive guidance on their research and career plans from experts from various institutions, potentially offering diverse perspectives compared to their own research advisor. For senior researchers involved, the event serves as an opportunity to engage in meaningful discussions with promising young researchers and to contribute to the community through mentorship. This year's Doctoral Consortium features a poster session, one-on-one mentoring, and a panel discussion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract The Russell Lab seeks to understand how bacterial symbionts of eukaryotic hosts are transmitted from one host generation to the next, and how these strategies evolve over time. Our research focuses on the intracellular inherited alphaproteobacterial Wolbachia symbionts of Drosophila melanogaster and congeners. These bacteria are capable of inducing a wide range of phenotypes in their hosts, from reproductive manipulations such as cytoplasmic incompatibility that bias offspring production towards infected females, to viral suppression that reduces titer and transmission from vector species to human populations. These phenotypes are currently employed to control host populations. However, little is known about the cellular, molecular, and evolutionary mechanisms underpinning these phenotypes or the transmission mechanisms that link Wolbachia between host generations. Over the next five years, we aim to reveal the microevolutionary processes driving host and symbiont evolution, the developmental processes that enable symbionts to induce their own host cell types, and the molecular mechanisms that link host and symbiont phenotypes using our in vivo and in vitro Wolbachia-infected Drosophila model system. To capture the rare and ephemeral horizontal transmission events that have occurred across Wolbachia’s evolutionary history, we will study diverse mixed strain infections in Drosophila cell lines and track their genome evolution. We will study the genomic basis of symbiont domestication after a host-switching event by leveraging historical stocks collected immediately after a Wolbachia strain entered and swept to fixation. We will study the downstream impacts of coevolutionary interactions by interrogating host and symbiont gene expression to identify novel symbiont-induced host developmental programs and molecular mechanisms. High-throughput genomic assays such as these have revealed promising candidate genes underlying Wolbachia’s ability to transmit to host offspring through the germline and infect uninfected cells efficiently. Using our in vivo and in vitro system, we will study the functional implications of these factors on the localization patterns of Wolbachia within and among host cells, Wolbachia’s ability to establish stable infections, and the fertility reinforcement phenotypes demonstrated by some strains. The overall vision for our research program is to develop a robust understanding of Wolbachia functional and evolutionary genetics in Drosophila to enable informed biological control applications. It is vital that we know the evolutionary genomic outcomes of divergent strains infecting novel hosts, as this is a possible outcome of releasing Wolbachia-infected mosquitoes into natural ecosystems. Our studies on the novel genetic regulatory and functional implications of host-symbiont molecular coevolution will reveal new biological pathways and mechanisms that could be leveraged for biomedical applications. Lastly, our approaches for functional genetic testing of candidate genes in Wolbachia-infected Drosophila cells and flies will enable us to validate host-symbiont interaction hypotheses and ascribe direct functions to Wolbachia genes.

NSF Awards · FY 2025 · 2025-01

Ocean ecosystems are reliant on tiny, microscopic phytoplankton that form the base of the marine food web, yet vast regions of sunlit open ocean waters also have chronically low concentrations of dissolved nitrogen (N), a nutrient that limits photosynthesis and growth. These ecosystems are highly regenerative, meaning that the organisms are adapted to low concentrations of nitrogen and recycle it efficiently. While this nitrogen recycling sustains growth, addition from other sources (or “new” nitrogen) is important for fueling new growth and is ultimately linked to the ocean’s ability to remove carbon dioxide from the atmosphere and store it in the deep ocean. New nitrogen is introduced into the surface ocean through the movement of deep waters with high nitrogen concentrations to the surface or through the process of biological nitrogen fixation -- the microbial conversion of nitrogen gas into a biologically available form. Diatoms are one of the most important phytoplankton groups in modern oceans and form the base of the food web in the most productive ocean ecosystems. In the low nutrient open ocean they can sometimes form dense aggregates (or “mats”) that can descend into deeper waters to obtain the nitrogen needed for their growth using buoyancy regulation. The investigators recently observed and sampled mats of multiple Rhizosolenia diatom species in the North Pacific Subtropical Gyre (NPSG) and showed that their microbiome contained a diverse array of microbes capable of nitrogen fixation. This discovery calls into question where Rhizosolenia mats acquire nitrogen and suggests that they may obtain it from living in symbiosis with nitrogen-fixing microbes. Importantly, fragile Rhizosolenia mats are not well sampled using traditional oceanographic techniques, as such we know very little about these microbial ecosystems and their contribution to oceanic productivity. This project is characterizing Rhizosolenia mat ecosystems, determining whether they are growing on nitrogen from nitrogen-fixers, and assessing their contribution to the nitrogen cycle in the NPSG. The investigators are combining traditional microscopy techniques, as well as modern multi-omics, imaging, and stable isotope tracer techniques. They are using deployable optical instrumentation and satellite data to track the location of mats during a research cruise to the NPSG, and using blue-water diving to sample and incubate the fragile mats. This project is having an impact beyond advancing discovery by providing professional development opportunities for early career ocean researchers, including exposure to a broad array of transferable skills, from scientific diving to molecular techniques. The investigators are also developing a hands-on educational module about marine phytoplankton, symbioses, and ocean nutrient cycles to be featured at the Moss Landing Marine Labs Open House, a free public outreach event held annually each spring. Diazotrophy, the microbial fixation of dinitrogen gas into ammonia, supports a significant amount of primary production in the chronically nitrogen-limited oligotrophic ocean. However, the relative importance of different diazotrophs to primary production is not clear, and ongoing discoveries of novel diazotrophs highlight our incomplete understanding of marine nitrogen-fixers. Phytoplankton vertical migration is an additional source of new nitrogen to surface waters in oligotrophic systems, and multispecies, migrating Rhizosolenia aggregates (or “mats”) have been reported to contribute significantly to both primary production and carbon export fluxes due to their ability to transport deep nitrogen into surface waters. The investigators encountered Rhizosolenia mats on a research cruise in the North Pacific Subtropical Gyre (NPSG) in 2022, which led to the discovery that they contain a varied assemblage of diazotrophs, but not the heterocyst-forming Richelia known to form associations with some Rhizosolenia sp. These findings, along with a historical observation of dinitrogen gas fixation in Rhizosolenia mats, suggest that these mats may acquire some of their needed nitrogen from diazotrophy, and mat-associated dinitrogen gas fixation constitutes an unrecognized important source of nitrogen to the NPSG. This project is assessing the composition, activity, and symbiotic nature of Rhizosolenia mat communities, as well as determining their significance to the nitrogen inventory in the NPSG. The investigators are providing the first detailed characterization of mat-forming Rhizosolenia and their associated diazotroph communities by using a combination of traditional microscopy techniques (light microscopy, Scanning Electron Microscopy, Transmission Electron Microscop), molecular and ‘omics tools (metagenome-assembled genomes, Rhizosolenia barcoding using voucher isolate strains, fluorescence-based visualization, amplicon High Throughput Sequencing) and stable isotope-based approaches at both whole mat and sub-mat scales (using nanoscale secondary ion mass spectrometry). Demonstrating that Rhizosolenia mats obtain diazotroph-derived nitrogen would transform the current paradigm about the role of these mats in nitrogen and carbon biogeochemical cycles and identify a novel diazotroph niche that is missed with conventional sampling. This project is also opening avenues to explore fundamental questions of diatom evolution and characterization of diatom strategies for metabolic adaptation to low nutrient environments through the isolation of mat-forming diatoms and generation of metagenome-assembled genomes. Additionally, morphological and molecular characterization of these fragile and cryptic Rhizosolenia mats is significantly contributing to illuminating the unseen pelagic microbiome. 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 · 2024-12

Cloud service providers offer Field Programmable Gate Arrays (FPGAs) as a time-shared service for efficiently accelerating high-value workloads such as machine learning, genome sequencing, databases, encryption, and other applications with strict security requirements. While the hardware is time-shared between multiple tenants, there is generally believed to be no information leakage between subsequent users since the FPGA bitstream and memories are digitally erased after each tenant’s use. The project studies “FPGA pentimenti” data that leaks between subsequent users through analog effects. The project’s broader significance and importance are developing techniques for securing cloud infrastructure and promoting education and research in FPGA security. This project studies, characterizes, and develops mitigations for FPGA pentimenti. Specifically, this project investigates how data from previous users is leaked via an analog side channel due to bias temperature instability effects. This project establishes bounds of data-recovery capabilities within the cloud FPGA model and identifies effective defenses for all stakeholders. This project also characterizes techniques for extracting FPGA pentimenti. With this knowledge, this project develops mitigations to reduce or eliminate these analog side-channel attacks from the perspective of the manufacturer, cloud provider, and end-user. 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 · 2024-12

Researchers are proposing an innovative project that combines cutting-edge artificial intelligence (AI) with vast ocean datasets to gain new insights into ocean dynamics and create datasets for the wider community. The team plans to develop a sophisticated AI technique to analyze satellite measurements and high-resolution ocean model outputs. This system will detect and measure ocean fronts—boundaries between water masses with different properties—using multiple types of satellite data. It will also estimate the heat content of the ocean's upper mixed layer by examining fronts and other remotely observable quantities over time. A key aspect of their approach is designing the AI system to be interpretable, allowing scientists to understand how it reaches its conclusions. The researchers will also develop methods to quantify the uncertainty in the AI's predictions, which is crucial for scientific applications. By applying this AI system to approximately 15 years of satellite data, the team hopes to track changes in ocean fronts and mixed-layer heat content over time. This could provide valuable insights into how various physical processes, from large ocean currents to smaller-scale phenomena, influence the ocean's heat storage. We will use output from COAS, a state-of-the-art global, high-resolution (4-km) coupled ocean-atmosphere model, to train nested physics-informed vision transformer (ViT) algorithms to (I) diagnose the incidence and strength of density fronts from “static” multi-field scenes of remote sensing measurements (e.g., wind, sea surface temperature and height); and (II) infer the heat inventory of the mixed layer from a time-series of the density fronts and remote sensing data. A key novelty is to design the ViT for interpretability and quantification of uncertainty, through a physics-guided pre-training procedure. With the ViT, we will assess changes in front incidence and intensity and the heat inventory in the mixed layer over the past ~15 years where sufficient satellite coverage is available, as assessed through ViT uncertainty estimates. A central scientific question we target is a better understanding of the interactions of various physical drivers of the mixed layer heat inventory, with contributions spanning mesoscale currents to sub-mesoscale processes. We aim to both predict the mixed layer heat inventory and occurrence and type of density-driven fronts but also to understand their physical drivers; this will require innovation within AI. We will focus on ViTs for ocean applications which offer unique challenges to the state-of-the-art, but transferable solutions. Challenges include uncertainty quantification to guide the scientific discovery and verification of the trustworthiness of ViT predictions. We will achieve this through a physics-guided pre-training based on latent-space manifold identification and a physics-guided approach to semantic segmentation and understanding. By assessing the sources of ViT predictive skill, we endeavor to verify existing theories for heat inventory and density-driven front variability determined using sparse and costly in-situ observations, and also if entirely new physical insight can be found using the ViT. From analysis of remote sensing datasets, we will estimate how identified drivers have changed from past to present, and assess the likelihood of future change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Gaps in equity and inclusion within STEM graduate education in the U.S. persist. At stake is the loss of contributions toward scientific innovation and excellence from more racially and economically diverse scientists (e.g., low-income, first-generation, students of color). Indeed, these students bring various cultural strengths from their home communities that can transform learning spaces, known as community cultural wealth. However, the transformative power of their cultural wealth is only possible with institutional commitment to recognize its value, including the implementation of educational practices that take concrete steps to leverage these diverse strengths with a critical lens. This National Science Foundation Innovations of Graduate Education (IGE) Track 2 award to the University of California, Santa Cruz will conduct a multi-stage and multi-program intervention aimed at investigating how programs build structural opportunities and support for mobilizing marginalized doctoral students’ cultural strengths and ways of knowing. Using a multiple case study design to understand the mobilization process, this project will illuminate pathways for diversifying, strengthening, and transforming STEM graduate education to better represent and serve new generations of talented scientists. The project will take inventory of a fellowship support program (Cota Robles Fellows program), an interdisciplinary research training program (New Gen Learning Consortium), and a mentoring training program (Equity-Minded Mentoring Certificate program). The goal is to redesign program elements to better mobilize marginalized students’ strengths for learning and make crucial connections to home departments to scale culture changes in STEM graduate education at the institutional level. In Stage 1, the project will connect to home departments and gather baseline data to examine the strengths and gaps of the three focal programs. Stage 2 will focus on relationship-building between programs and home departments, including learning about the specific cultures of support for graduate students and identifying potential target areas for collaboration. This step includes presenting the mobilization framework and findings from Stage 1 to develop re-design plans. Stage 3 will focus on implementing the re-design plans. The project will use a multiple case study design to examine the implementation process through focus groups with program staff and department contacts to examine their perspectives, challenges, and questions about the implementation as it unfolds and as it relates to the mobilization process, paying keen attention to concrete steps taken and resources used toward mobilization. In Stage 4, the project will study the impacts of the implementation on the culture, practices, and support structures of the programs and department spaces in the longer term, including a final focus group to have program staff and department contacts reflect on the implementation process, again paying keen attention to questions related to the mobilization process. Findings will be disseminated through conference presentations, brief reports, and publications on lessons learned for universities, researchers, and practitioners to scale up the impacts of the project. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Spectroscopy is a fundamental tool astronomers use to understand the physical Universe, allowing scientists to measure, for example, the physical state and motions of intergalactic gas and the demographics of stellar populations in distant galaxies. State-of-the-art instrumentation continually improves the ability to collect these data; however, equally important are sophisticated software packages that perform the careful processing of raw data into calibrated spectra ready for scientific analysis. The PypeIt (pronounced "pipe it") software package has emerged as a comprehensive data-processing product that leverages modern, industry-driven support infrastructure for open-source code development. This NSF award enables PypeIt to expand into a full-fledged open-source ecosystem (OSE) that will support nearly all medium- and large-class telescopes owned and operated by institutions in the United States. The wide reach of this effort reduces barriers to processing data from both new and existing spectrographs, improving the scientific productivity of both experienced astronomers and those relatively new to processing spectroscopic data. The OSE also introduces PypeIt support for commercially available spectrographs used by amateur astronomers, enabling them to quickly produce calibrated, science-ready spectra from their observations. Although initially developed by a small team at the University of California Observatories for instruments at the Lick and W. M. Keck Observatories, PypeIt now supports processing data from more than 50 spectrographs at 18 observatories worldwide. Its success can be attributed to two of its guiding principles: (1) focus low-level algorithms on the commonalities of spectroscopic data process such that they can be cleanly separated from the higher-level specifications of each spectrograph; and (2) employ state-of-the-art algorithms that ensure systematic errors are negligible compared to the fundamental noise limit set by Poisson counting statistics. Building from these core principles, this NSF award enables PypeIt to reach its full potential as a community-wide OSE, meeting a critical need outlined by the 2020 Decadal Survey of Astronomy and Astrophysics. This is achieved by four primary activities: (1) strategically broadening the current PypeIt developer base to include key staff at seven US observatories (Lick, WMKO, Palomar, MMT, LBT, Lowell, and APO) by directly funding them to enable PypeIt to support processing data from their instruments; (2) building partnerships with additional observatories both, in the U.S. and abroad, by supporting open-source development by the worldwide astronomical community and by hosting bi-annual workshops that introduce new users to PypeIt and provide real-time collaboration between users and developers; (3) implementing a new community-driven governance structure for the project that improves prioritization of the development activities and is more broadly inclusive of all stakeholders (developers, observatory staff, and users); and (4) extending existing, but observatory-specific, tools that facilitate real-time decision making for observers at the telescope (key to improving observing efficiency) and that streamline long-term data archiving (a key priority of the astronomy community) to new observatories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The Adélie penguin (Pygoscelis adeliae) is the most abundant penguin in Antarctica, though its populations are currently facing threats from climate change, loss of sea ice habitat and food supplies. In the Ross Sea region, the cold, dry environment has allowed preservation of Adélie penguin bones, feathers, eggshell and even mummified remains, at active and abandoned colonies that date from before the Last Glacial Maximum (more than 45,000 years ago) to the present. A warming period at 4,000-2,000 years ago, known as the penguin ‘optimum’, reduced sea ice extent and allowed this species to access and reproduce in the southern Ross Sea. This coastline likely will be reoccupied in the future as marine conditions change with current warming trends. This project will investigate ecological responses in diet and foraging behavior of the Adélie penguin using well-preserved bones and other tissues that date from before, during and after the penguin ‘optimum’. The Principal investigators will collect and analyze bones, feathers and eggshells from colonies in the Ross Sea to determine changes in population size and feeding locations over millennia. Most of these colonies are associated with highly productive areas of open water surrounded by sea ice. Current warming trends are causing relatively rapid ecological responses by this species and some of the largest colonies in the Ross Sea are likely to be abandoned in the next 50 years from rising sea level. The recently established Ross Sea Marine Protected Area aims to protect Adélie penguins and their foraging grounds in this region from human impacts and knowledge on how this species has responded to climate change in the past will support this goal. This project benefits NSF’s mission to expand fundamental knowledge of Antarctic systems, biota, and processes. In association with their research program, the Principal Investigators will create undergraduate opportunities for research-driven coursework, will design K-12 curriculum and assess the effectiveness of these activities. Two graduate students will be supported by this project to update and refine the curricula working with K-12 teachers. There is also training and partial support included for one doctorate, two master and eight undergraduate students. General public will be reached through social media and YouTube channel productions. A suite of three stable isotopes (carbon, nitrogen, and sulfur) will be analyzed in Adelie penguin bones and feathers from active and abandoned colonies to assess ecological shifts through time. Stable isotope analyses of carbon and nitrogen (δ13C and δ15N) are commonly used to investigate animal migration, foraging locations and diet, especially in marine species that can travel over great distances. Sulfur (δ34S) is not as commonly used but is increasingly being applied to refine and corroborate data obtained from carbon and nitrogen analyses. Collagen is one of the best tissues for these analyses as it is abundant in bone, preserves well, and can be easily extracted for analysis. Using these three isotopes from collagen, ancient and modern penguin colonies will be investigated in the southern, central and northern Ross Sea to determine changes in populations and foraging locations over millennia. Most of these colonies are associated with one of three polynyas in the Ross Sea. This study will be the first of its kind to apply multiple stable isotope analyses to investigate a living species of seabird over millennia in a region where it still exists today. Results from this project will also inform management on best practices for Adelie penguin conservation affected by climate change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Seagrass suffers from several threats, including wasting disease, that could impair ecosystems, and accurate and up-to-date mapping of seagrass is critical for understanding estuary health and resilience to environmental stressors. This project advances fundamental research using Unmanned Aerial Vehicles (UAVs) to map seagrass across Northern California to detect disease and analyze temporal trends in seagrass health and extent. A significant focus of this project is creating a STEM network between minority-serving institutions and community colleges to foster collaborations and build capacity, training and mentoring 24 students in 4-year university and 2-year community colleges annually, providing them with interdisciplinary skills such as UAV piloting, GIS, coastal science, and scientific communication. Students also develop professional networks and experience study, promoting long-term engagement in STEM. The goal is to extend this training program to community organizations, citizen scientists, and practitioners, aiming to contribute to coastal management and seagrass conservation. Efficient, comprehensive monitoring of the seascape-scale impact of seagrass disease is essential to predict future ecological impacts and implement early interventions. However, seagrass conditions are rarely quantified in spatial and temporal domains, and traditional mapping methods are labor-intensive, expensive, or have a coarse resolution. UAV imaging combined with GIS are emerging technologies for monitoring coastal seagrass ecosystems because of their spatial high-resolution, temporal flexibility, and cost-efficiency. Our project utilizes multi-platform UAV systems with visible, multispectral, and LiDAR sensors to provide more accurate data for better delineation and classification of seagrass and predicting disease more effectively. Combined with ground data and in situ sampling, we develop a UAV and GIS-based platform for comprehensive monitoring of seagrass ecosystems. The platform aid in the detection of diseases at an individual leaf scale in prominent seagrass habitats. Our research includes multi-platform UAV mapping, spatial modeling of the seagrass disease, temporal analysis of seagrass bed extent changes, development of AI-based image analysis for species classification and disease detection, creation of a cloud data hub for real-time data exchange, and provision of research training and professional development opportunities for students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Trained and optimized for fluent speech, speech AI performs poorly for people who stutter (PWS). Even when automatic speech recognition (ASR) systems do manage to transcribe stuttered speech, the resultant transcriptions often remove disfluencies like filler words, inevitably stigmatizing stuttering and denying the option for PWS to have their disfluencies preserved and normalized in transcripts. This project will partner with StammerTalk – a grassroots community of PWS – to destigmatize disfluencies in speech Artificial Intelligence (AI) by: (a) developing metrics, tools, and techniques to measure, understand, and address fluency biases in existing ASR models, and (b) studying StammerTalk itself as a case study for grassroots AI development that not only produces more equitable and fair AI models but also fosters technical capacity and collectivity within the community. By empowering grassroots, marginalized communities to engage and drive AI initiatives, this project seeks to challenge the existing concentration of AI power by opening up a paradigm for community-led, decentralized AI data collection and development that prioritizes equity, inclusion, and autonomy. A cross-sector team of academic and community researchers will carry out three strands of activities: 1) technical work to support the StammerTalk community to develop stuttering- friendly speech AI; 2) empirical work to document, analyze, and understand their working model for community-driven, grassroots AI; and 3) co-design work to develop design concepts that integrate more fair and inclusive ASR models in products. Lastly, all three strands will be synthesized to produce a playbook for grassroots AI outlining the steps to community-led data collection, model evaluation, model development, and product co-design as a capacity builder for marginalized, low-resourced communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Table lookups serve as fundamental functions and design blocks of numerous computer network protocols and algorithms. With the emergence of new network application scenarios such as cloud and edge computing paradigms, the Internet of Things (IoT), smart communities, self-driving vehicles, and distributed machine learning, modern networks have been using in-network lookups beyond classic Internet Protocol (IP) or Medium Access Control (MAC) address forwarding. The project plans to investigate a new paradigm using machine learning models to replace traditional hash functions in lookup engines and disaggregating lookup functions for heterogeneous devices. The project aims to develop LEarned and Disaggregated In-network Lookup Engines (LEDILE), a next-generation network framework that provides cost efficiency, high performance, effectiveness in handling failures, scalability to large networks, and compatibility with emerging network features. This project seeks to fundamentally change the design and deployment of classic in-network lookup engines by replacing a functional stage with a learned model and disaggregating lookup functions onto heterogeneous devices. The proposed research will include the following: 1) Develop new in-network lookups with perfect hashing and learned models; 2) Disaggregate in-network lookups in two dimensions: stages and shards; 3) Develop the LEDILE framework and its applications; 4) Evaluate the proposed algorithms, protocols, and software framework on multiple platforms. If successful, the research outcomes of this project will be transformative as they will provide critical networking functions and services in emerging networks including cloud, IoT, edge computing, and their applications. The PI plans to integrate the research being conducted under this project into the undergraduate and graduate curriculum. The algorithms, protocols, software, and experimental tools developed in this project will be made available to the public to enable other researchers to work in this area. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Solid-state batteries (SSBs) have emerged as strong contenders for next generation LIB technologies as they are expected to achieve higher energy and power densities and increased safety through replacement of liquid electrolyte formulations with solid electrolytes. This recent global and commercial interest in SSBs has helped illuminate the key challenges for the realization of this technology, such as increased understanding and control of the solid-state composite cathodes and the impact of the active material/solid electrolyte interfaces. Supported by the Division of Chemistry at NSF and DFG, Profession Guo from University of California-Santa Cruz in US, and Dr. Adelhelm at Humboldt University and Dr. Bär at HZB in Germany will work together focus on studying new cathode chemistries for SSBs, including materials development and multi-modal characterization of their properties and interfaces with solid electrolytes. The collaboration also provides students the opportunities to be exposed to international research activities and different culture. An understanding of the processes at the interfaces and in bulk that occur in batteries requires obtaining qualitative and quantitative atom-specific information under realistic operating conditions at relevant time scales. X-ray spectroscopies are critical tools to achieve this understanding. State-of-the art high-brilliance synchrotron sources provide a powerful means to use photons for probing buried interfaces. The ability to study buried interfaces is of great importance for energy storage devices, such as batteries. The international team supported by this grant will develop a mechanistic understanding of the redox and decomposition processes occurring in sulfide-based electrode/electrolyte composites. Achieving this goal is dependent on the development of a new multi-modal characterization platform that enables experiments with high energy resolution on different length scales. This new platform will enable the study of SSBs under in-situ and operando conditions by XAS and RIXS for an atom-specific understanding of electrochemical phenomena and associated degradation processes at electrode/electrolyte. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Across species, multisensory integration (MSI) is used to accurately and rapidly localize objects in the environment. This process is presumed to rely on the correct organization of sensory inputs into topographically aligned maps of egocentric space. However this idea has not been rigorously tested. MSI and spatial orienting behaviors are compromised in many developmental disorders including Autism, Schizophrenia and ADHD, highlighting the significance of understanding the developmental mechanisms that establish effective MSI-dependent behaviors. The overall objective of this application is to determine the mechanisms used to integrate visual and auditory maps of location in the mouse superior colliculus (SC) and identify the behavioral consequences to animals deficient in these mechanisms. The central hypothesis is that sensory experience is used to align auditory and visual maps of space in the SC; furthermore, accurate alignment of the two maps is required for effective MSI and robust natural spatial orienting behavior. Aim 1 seeks to determine both the developmental window in which MSI forms and the critical period of sensory influence. We will present a wake mice with spatially restricted visual and auditory stimuli while recording neuronal responses in the SC using high-density probes. Analyzing this data will determine the spatial receptive fields (RFs) of visual, auditory and visual/auditory multimodal neurons. This will be done at key developmental stages plus or minus visual or auditory deprivation. The goal of Aim 2 is to quantify how developmental changes in multisensory experience alter natural stimulus localization behaviors. Mice innately hunt insects and their ability to capture them is most efficient when they have access to multisensory cues. Of note, orienting behavior during prey capture is disrupted in mouse models of ASD. Prey capture behaviors such as time to cricket detection, spatial accuracy and precision of pursuit, and time to capture success will be measured in mice at different ages and in those deprived of early vision or audition. This aim will reveal the role that sensory experience plays in generating natural localization behavior. Experiments in Aim 3 will test the longstanding hypothesis that the alignment and integration of visual and auditory inputs in the SC rely on the visual map as a template. We will record the auditory and visual response properties as in Aim 1 from two populations of mice: those with scrambled or duplicated visual map topography via perturbations in EphA/ephrin-A signaling or shifted via prism goggles. We can then determine if the auditory map rearranges to align and integrate with the duplicated, scrambled or shifted visual map and, if so, how these changes lead to altered orienting behavior during prey capture.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Psychedelics are chemicals best known for their ability to induce profound changes in the human conscious experience. After a several-decade hiatus, research on psychedelics is undergoing a renaissance, driven by their potential to treat psychiatric disorders with rapid onset and enduring effect. Recent research reveals that psychedelics can rapidly promote the structural and functional plasticity of synapses, leading to the conjecture that neural plasticity underlies their long-term therapeutic values. However, the neurobiological mechanisms remain largely elusive. Our overarching goal is to understand the cellular and circuit mechanisms underlying psychedelics’ long-lasting therapeutic effects. In this proposal, we focus on the classical serotonergic psychedelic psilocybin and investigate how it rescues the deleterious effects of stress, a major risk factor for many neuropsychiatric disorders. Our central hypothesis is that psilocybin affects the brain at multiple levels, from synaptic plasticity to the functional network; although psilocybin only transiently enhances synapse formation, it permanently alters the synaptic circuit in an experience-dependent manner; the incorporation of new synapses into the neural circuit is essential for psilocybin’s long-lasting rescue of stress-induced functional and behavioral deficits. Specifically, in Aim 1, we will determine the acute and enduring effects of psilocybin on the stressed brain, particularly on the structural reorganization of synaptic circuits, on cortical functional networks, and on the representation of behavioral variables by cortical neuronal ensembles. Aim 2 determines how the environmental and behavioral contexts in which psilocybin is administered impact its rescuing effects on the stressed brain. Aim 3 determines the contribution of psilocybin-induced neuroplasticity, particularly the stabilization of newly formed dendritic spines, to its rescuing effects on the stressed brain. Overall, these studies will provide an integrated, mechanistic understanding of psilocybin’s rescuing effects across the organizational hierarchy of the brain, from molecules and synapses to circuits and functional networks, and lay the foundation for its clinical application in treating stress-related psychiatric disorders.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY Psychedelics are chemicals best known for their ability to induce profound changes in the human conscious experience. After a several-decade hiatus, research on psychedelics is undergoing a renaissance, driven by their potential to treat psychiatric disorders with rapid onset and enduring effect. Recent research reveals that psychedelics can rapidly promote the structural and functional plasticity of synapses, leading to the conjecture that neural plasticity underlies their long-term therapeutic values. However, the neurobiological mechanisms remain largely elusive. Our overarching goal is to understand the cellular and circuit mechanisms underlying psychedelics’ long-lasting therapeutic effects. In this proposal, we focus on the classical serotonergic psychedelic psilocybin and investigate how it rescues the deleterious effects of stress, a major risk factor for many neuropsychiatric disorders. Our central hypothesis is that psilocybin affects the brain at multiple levels, from synaptic plasticity to the functional network; although psilocybin only transiently enhances synapse formation, it permanently alters the synaptic circuit in an experience-dependent manner; the incorporation of new synapses into the neural circuit is essential for psilocybin’s long-lasting rescue of stress-induced functional and behavioral deficits. Specifically, in Aim 1, we will determine the acute and enduring effects of psilocybin on the stressed brain, particularly on the structural reorganization of synaptic circuits, on cortical functional networks, and on the representation of behavioral variables by cortical neuronal ensembles. Aim 2 determines how the environmental and behavioral contexts in which psilocybin is administered impact its rescuing effects on the stressed brain. Aim 3 determines the contribution of psilocybin-induced neuroplasticity, particularly the stabilization of newly formed dendritic spines, to its rescuing effects on the stressed brain. Overall, these studies will provide an integrated, mechanistic understanding of psilocybin’s rescuing effects across the organizational hierarchy of the brain, from molecules and synapses to circuits and functional networks, and lay the foundation for its clinical application in treating stress-related psychiatric disorders.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY / ABSTRACT This grant proposal outlines a comprehensive plan to develop novel computational methods and software tools for analyzing pangenomic data, with a focus on improving the accuracy and efficiency of variant calling and genotyping, particularly for complex structural variants (SVs). The proposal is divided into five specific aims: Aim 1: Create a pangenome mapper supporting long-reads, which will enable accurate and efficient mapping of long-range sequencing data to pangenome references. Aim 2: Develop personalized pangenomes, which involves rapid and efficient construction of a subset of a larger graph based on an input sample's k-mers. This approach will tailor the pangenome for specific analysis and so lead to improved performance in downstream analysis. Aim 3: Create a pangenome variant calling and imputation method for unified genome inference, which will combine imputation with read-based genotyping using machine learning to infer a more complete representation of variation, including both small variants and SVs. Aim 4: Genotyping complex SVs involving protein-coding genes, which will involve identifying long segmental duplications, grouping haplotypes, and developing targeted genotyping methods for long and short reads. Aim 5: Develop mature rGFA based variant calling for reporting both SV and small variants within polymorphic sequence, which will expand the current definition of reportable variation and provide pipelines that can report tens of thousands of additional variations per sample. The proposal highlights the need for better computational tools for pangenome analysis, especially for complex SVs, and outlines a comprehensive plan to address these challenges. The proposed software tools and methods will enable researchers to analyze pangenomic data more effectively and efficiently, leading to new insights into genetic variation and its role in disease and other biological processes.

NIH Research Projects · FY 2025 · 2024-09

We propose to develop flexible Bayesian statistical methods to gain a comprehensive understanding of microbial community dynamics using high-throughput sequencing data. The emergence of large-scale microbiome studies provides new opportunities for understanding how various microbial communities function and relate to their environment. However, the analytical methodology required to model complex microbiome data is still lacking. One of the key objectives is to develop a general method for inferring microbial community dynamics that vary with host and environmental factors. We also aim to extend this method to complex scenarios, such as longitudinal microbiome studies, which investigate the evolution of microbial communities, and multi-omics microbiome studies that integrate various types of omics data. Our proposed methods rigorously address the unique challenges of microbiome data analysis and achieve more accurate inferences about the underlying biological processes with honest uncertainty quantification. The proposed methods will provide an opportunity to attain a deeper understanding of the microbiome’s role, potentially paving the way for intervention strategies that enhance health and disease management. The proposed research involves synthesizing innovative concepts to tackle statistical challenges in microbiome data analysis within complex study settings, with a particular focus on multivariate count data presenting unique statistical complexities. The research agenda is broad and widely applicable, consisting of methodological development and theoretical examination of model properties, along with a challenging computational component aimed at achieving computational feasibility for big data. Our semiparametric methods offer significantly improved accuracy compared to existing methods. Our innovative approach to imposing a joint sparsity structure on the covariance matrix enhances the ability to infer microbial interactions. This approach improves robustness against large signals and reduces noise in complex high- dimensional data. These models are developed in close collaboration with biologists at UC Los Angeles and UC Santa Cruz, incorporating domain-specific biological knowledge from microbiome research, and consequently, they yield biologically interpretable inferences. Our findings, integrated into microbiome research through collaboration, will advance our understanding of how microbes are functionally related to the host, the environment, and other microbes. This understanding can ultimately lead to improvements in human health or the environment through microbiome monitoring or manipulation. Another key aspect of the project involves disseminating the proposed methods through user-friendly software for public use.

NSF Awards · FY 2024 · 2024-09

Black disabled students encounter systemic challenges in K-12 education such as being overrepresented in special education categories of behavioral and intellectual disabilities while facing harsher disciplinary consequences compared to other students. These challenges impact their opportunities for meaningful STEM learning. A key avenue to counter these disparities is through high school mathematics teacher coaching encompassing knowledge of the interactional nature of racism and ableism in teaching and decision making. Therefore, this project aims to develop and test a theoretical coaching framework that addresses challenges while advancing conceptual mathematics learning and high school mathematics instructional practices. Using qualitative participatory methodology, this project will involve establishing and sustaining an authentic partnership with a cohort of Black disabled high school students. Their voices, knowledge, and experiences will be central in informing the development of this project’s coaching theoretical framework. The research team will support students’ learning, developing, and enacting ways to counter racism and ableism, advance conceptually oriented mathematics instructional practices, and impact instruction to improve students’ experiences and learning opportunities. Students will have opportunities to convene to share their experiences, and mathematics teachers will participate in professional development opportunities to support working with students as well as piloting and developing the coaching model. This project will contribute to both theory and practice in mathematics education as well as produce positive impact to the lives of Black disabled students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Brown bears (Ursus arctos) have undergone rapid population declines over the last 150 years in the lower 48 states. This project will use DNA sequencing technologies to investigate the effects of this rapid population decline as well as the effects of previous conservation management actions. The researchers will investigate the utility of these genetic technologies for population monitoring and management. New genetic tools will be developed to rapidly sequence and identify individual brown bears in the lower 48 states using non-invasive samples. Samples from both historical (museum) and contemporary populations will be used to better understand the impact of population decline and conservation management efforts on the health of brown bear populations. The project will yield new insights into how small populations of animals can persist and will include a database with applications for general population monitoring and human-wildlife conflict scenarios. This project will also establish a brown bear genetic database and provide training opportunities in genetic and genomic technologies to conservation managers. Genomics is poised to be a potentially useful and cost-effective tool for population monitoring and management, however, the limitations of population genetic estimates for conservation purposes are not well understood. This project will use an extensive set of historic and modern brown bear (Ursus arctos) samples to characterize genomic diversity over the last 200 years, how it has changed over time and whether management decisions (e.g., translocations) have impacted the genomic landscape of the species. Brown bears in the lower 48 have been extensively monitored since approximately 1975. The life history data collected by conservation partners over the past several decades, paired with newly collected genomic data, will be used to analyze the impact of past translocations and population bottlenecks in the lower 48. Relating population genetic statistics to life history traits, such as fecundity, lifespan, and independent population size estimates, will help to better implement recommendations to maintain genetic health for species of conservation concern. This project is jointly funded by the Division of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice 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.