Washington State University

NSF Awards · FY 2026 · 2026-09

Viral infections grow and spread through processes that occur at many connected levels, from events inside infected cells, to the body’s defense against infection, and to the spread of disease across communities. However, these processes are often studied separately, making it difficult to understand how changes at one level affect outcomes at another. This project will develop new mathematical and computational tools to connect these levels in one framework. The work will help researchers better understand how viruses grow, how the body responds to infection, how treatments and prevention measures work, and how infections spread in populations. By linking these processes together, the project may provide useful guidance for evaluating strategies to reduce the impact of viral diseases. Educational and outreach activities will provide interdisciplinary training opportunities and introduce students to the application of mathematics in biomedical research and public health. This project will develop and analyze a multiscale mathematical modeling framework for viral infections by integrating intracellular and extracellular dynamics within the human body with disease transmission between host populations. At the intracellular level, the models will describe viral entry, replication, assembly, and release. At the extracellular level, the models will describe viral kinetics and immune responses, including antibody and cellular immune responses. At the population level, the models will incorporate multiple transmission routes, symptomatic and asymptomatic infection, vaccination, waning immunity, and variant emergence. These components will be coupled into unified multiscale systems to study how molecular mechanisms, host immune responses, and transmission processes interact across scales. The project will combine dynamical systems analysis, bifurcation theory, stochastic modeling, data fitting, sensitivity analysis, and numerical simulation. The resulting models will provide a mathematical basis for studying treatment effects, vaccine impacts, variant dynamics, and epidemic outcomes across multiple biological scales. 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 2026 · 2026-09

Groundwater models are computer models that scientists and engineers use to predict the flow of water in subsurface environments. They can also be used to predict the transport and fate of contaminants in aquifers. The models usually require some knowledge about the subsurface structure and other hydrological parameters. However, this knowledge is often incomplete, leading to predictions that may not be accurate. This project will develop new computer simulation tools that account for uncertainties in aquifer parameters to make predictions that also report uncertainties in the results in plain terms. The mathematics underlying the new tools is borrowed from quantum mechanics. The tools use quantum theory to represent possible states, such as multiple possible transport pathways, at the same time, which is novel for groundwater models. The team will test the methods on controlled benchmarks and on well-known field datasets and will compare results to industry standard workflows. All codes and test cases will be shared openly. An AI-chatbot support tool will help users understand concepts, run examples, and interpret outputs. The outcomes of the project will have the potential to transform the way model results are reported, which can influence the management of environmental challenges. The project will develop a “Quantum-Enhanced Hydrology (QEH)” through three complementary, quantum-inspired amplitude-phase frameworks for groundwater flow and transport that embed uncertainty directly in the evolving model state while guaranteeing recovery of the classical advection dispersion equation (ADE) in the defined limit cases. Framework 1 will implement a Hydro-Madelung style amplitude-phase formulation in which amplitudes represent probabilistic occupancy across modes conditioned on facies structure and phases act as velocity potentials, enabling low dimensional calibration of effective parameters linked to conductivity structure and multiple possible transport speeds. Framework 2 will implement a facies-aware, Markovian transport evolution of a complex amplitude field with a dephasing operator tied to facies correlation length scales to represent sub-grid mixing and pre-asymptotic behavior on practical grids, while collapsing to ADE behavior in limiting cases. Framework 3 will represent transport with a density matrix evolution whose diagonal corresponds to concentration and whose localized off-diagonals encode short-range correlation structure, providing a compact “mixed-state” closure for unresolved heterogeneity and mixing. Validation will proceed from synthetic impulse/step plume benchmarks to heterogeneous ensembles and then to the MADE (Macrodispersion Experiment) site datasets, using withheld-data experiments to quantify robustness under limited information and to define when added model complexity is warranted. Each framework will be benchmarked against established tools (e.g., MODFLOW/MT3DMS with PEST++) using identical grids and calibration targets, with predictive performance assessed using distribution shape and decision-relevant metrics and computational cost reported transparently (outer-loop evaluations, CPU-hours, and time-to-target error), and all three frameworks have strong potential for acceleration on quantum computers. Deliverables include three open QEH prototypes with ADE-consistency tests, a documented comparison of accuracy-cost tradeoffs across regimes relevant to remediation risk assessment, complete ready-to-run code for all examples, and an AI-assisted, documentation-grounded guide that helps practitioners reproduce benchmark results and deploy uncertainty-centric predictions to real-world problems in their own workflows. 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 2026 · 2026-08

The dryness of the Earth's atmosphere (vapor pressure deficit, or VPD) is increasing. Its effects on many plant processes – from leaf photosynthesis, to tree growth and mortality, to forest production and water use – remain poorly understood. How does rising VPD affect different tree species growing in different locations? How do the effects of rising VPD on plants vary depending on the amount of moisture in the soil? How much and how quickly can plants cope with changes in VPD? And how do VPD effects operating on short-term, small-scale leaf processes translate to longer-term and larger-scale impacts on whole forests? Not knowing answers to these questions challenges researchers' ability to predict and manage forest responses to rapid changes in the environment. This project, termed SCALE-UP, is a partnership between institutions funded by the U.S. NSF and the NSF of Switzerland (SNSF) that enables a collaboration between U.S. and Swiss plant scientists and ecologists. This international team will examine the effects of rising VPD across vast distances and time scales using a cutting-edge combination of experiments, observations, and computer models. Not only will the project answer important scientific questions and advance fundamental knowledge about limited forest resources, it will also improve the ability of the U.S. and Switzerland to model, predict, and manage those resources. The scientists will utilize artificial intelligence (AI) approaches in their computer models, and will contribute to the U.S. AI national priority area by training the next generation of researchers in these techniques. By partnering internationally, U.S. researchers will gain access to unique resources, including a forest experiment in Switzerland that manipulates atmosphere and soil moisture, providing benefits to science and society in both countries. SCALE-UP will leverage state-of-the-art controlled seedling experiments, the first in-vivo VPD and soil drought manipulation in a mature forest, globally distributed tree growth datasets across dozens of species at various time resolutions, and mechanistic and scalable models ideally suited for understanding VPD impacts and underlying processes. The approach will thus link experimental insights, large data analyses, and mechanistic model simulations across scales — from minutes to decades, from individual leaves to entire ecosystems, and from seedlings to mature trees. By combining these cutting-edge and cross-scale approaches, SCALE-UP will unravel (1) the mechanisms underlying the diversity of VPD impacts across species on gas exchange, growth, mortality, and ecosystem-scale carbon and water fluxes, (2) the interaction of these responses with soil drought, (3) the potential for acclimation to rising VPD and its role in mitigating impacts of extreme events, and (4) the development of mechanistic insights and predictive models to understand and project VPD effects on forests at multiple scales. The project will therefore enable establishment of a robust empirical and theoretical basis for predictive understanding of VPD impacts on forests. This comprehensive analysis will offer unprecedented insights into the constraints to tree growth, drivers of tree mortality, the diversity of responses across species, and the role of acclimation of plant functional traits in response to exposure to a gradual rise vs. extreme VPD, enabling robust projections of environmental change on water and carbon cycles, and on forest dynamics. Integrated with the experiments and analyses will be training opportunities for five graduate students, and three postdoctoral researchers, and outreach to grade school students and land managers. 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 · 2026-06

PROJECT SUMMARY Uganda has experienced seven outbreaks of the highly pathogenic orthoebolavirus, Sudan virus (SUDV) since 2000. SUDV has reemerged in Uganda twice since September 2022 - first with an outbreak with 142 confirmed cases and 55 known deaths between September-December 2022, and again in January 2025, with 9 confirmed cases and 1 death as of mid-February 2025. Beyond the high mortality associated with orthoebolavirus infection, survival from infection is marked by long-term health problems in a subset of survivors including persistence of viral RNA in immune-privileged sites for months to years after recovery, and development of long- term disease sequelae symptoms that can be debilitating. These long-term impacts on survivors have been primarily studied in the context of Ebola virus (EBOV), and thus data are lacking for SUDV. We have begun to fill in those gaps through the analysis of SUDV survivors of the 2022 outbreak in Uganda together with the Ugandan Ministry of Health and researchers from the Uganda Virus Research Institute and Makerere University. Shortly after the end of the outbreak, we enrolled 86 of the 87 SUDV survivors into a study to enable long-term analysis of viral persistence, sequelae, and host immunity. We have found that 20 individuals of the 42 individuals who were eligible testing of viral RNA in secretions from immune-privileged sites (e.g. semen and breastmilk) had evidence of viral persistence beyond acute infection into convalescence. In addition, we have found that over half of the survivors report post-SUDV sequelae symptoms at 3 months after infection, with 39% of survivors still reporting symptoms 24 months after infection. As viral persistence and sequelae in the context of EBOV have been shown in survivors past 5 years, here we aim to leverage our approved research protocol and existing cohort of the survivors of the 2022 SUDV outbreak in Uganda to maintain serial sampling through 5 years post-infection. To date, we have already collected samples and sequelae data at 7 timepoints starting from 3 months post-infection through 2 years post- infection. We propose to continue sample and data collection every 6 months on these survivors through 5 years- post infection to capture clinical sequelae data, viral persistence, and immune dynamics for a total of 5 years after acute infection. The samples and data collected through this proposal will be used in subsequent studies to investigate the relationship between humoral immune responses, virus persistence, and development of chronic sequelae in SUDV. If successful, the samples and clinical data collected will represent among the most complete, continuous, and comprehensive analysis of sequelae and viral persistence of any human Ebolavirus infection to date.

NSF Awards · FY 2026 · 2026-06

Foundation models are large artificial intelligence (AI) systems that learn from vast data and now play a central role in science and technology. Yet the complexity of how these models process information makes it difficult to recognize when their outputs are unreliable. This is especially challenging in safety-critical settings, such as health monitoring and smart homes. As hardware technologies evolve, new forms of data emerge from new sensors, and often do not align with past data used to train existing models. How these models encode information might also be disconnected from established scientific knowledge, such as human physiology. Current methods lack a systematic way to incorporate such information or assess when model outputs are unreliable. This research addresses this gap by reshaping how large AI models transmit information so that it becomes easier to assess their output reliably and easier to align with new data and scientific knowledge. The project’s novelties are this new representation of information flow and a unified framework for integrating diverse data with scientific knowledge. The project’s broader significance and importance are enabling trustworthy decision support in large AI systems and educating a workforce adept at leveraging these technologies. This approach treats the internal stages of a foundation model as snapshots of an information flow that captures how the statistical geometry of the model's internal features changes as information passes through it. Learning this flow makes it possible to track uncertainty in a more structured and lower-dimensional space, enabling scalable and accurate reliability estimates that previously did not extend to large-scale models. It supports adaptation to new modalities, such as signals from emerging sensors, and incorporation of scientific laws describing the processes that generate the data. These advances enable trustworthy foundation models that are adaptable and grounded in scientific laws, with applications spanning health monitoring, scientific discovery, and engineering 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.

NIH Research Projects · FY 2026 · 2026-05

Abstract Arthropod-borne disease continues to be a significant source of morbidity and mortality worldwide. The ability of an arthropod to harbor and transmit pathogens is termed “vector competency”. Many factors influence vector competency, including how the arthropod immune system responds to the microbe. The intricacies of insect immunity have been well-studied owing to the model organism, Drosophila. In contrast, comparatively little is known about tick immunity, representing a fundamental knowledge gap in vector biology. Arthropod immune processes are now increasingly recognized as being divergent across species. We previously reported a noncanonical Immune Deficiency (IMD) signaling network in ticks that differs from what has been described in insects and restricts colonization and survival of the Lyme disease- causing spirochete Borrelia burgdorferi. The tick genome has orthologues to core signaling components of the IMD network, including the NF-κB-like transcription factor Relish, but lacks pathway-specific antimicrobial peptides (AMPs). How the tick IMD pathway exerts antimicrobial activity and what gene targets Relish transcriptionally upregulates remain unknown. To identify genes putatively regulated by Relish, we used a program that predicts transcriptional factor binding sites in arthropod genomes, ArthroQuest. We identified one putative Relish-regulated gene, tachylectin-5, with predicted antimicrobial and agglutinating functions. We found that Tachylectin-5 was also induced in tick cells infected with B. burgdorferi. Based on these findings, our central hypothesis is that Ixodes Relish upregulates the expression of Tachylectin- 5 and other antimicrobial effector molecules that restrict the Lyme disease spirochete B. burgdorferi. Aim 1 of this proposal will examine whether tachylectin-5 expression is driven by Relish. Aim 2 will investigate if B. burgdorferi is functionally restricted by Tachylectin-5. Aim 3 will seek to define the Relish-regulated transcriptional network in Ixodes ticks. The outcome of this proposal will provide insight into the molecular mechanisms that govern vector competence and immunity in ticks.

NSF Awards · FY 2026 · 2026-04

The Plant Cell Dynamics Meeting is an annual conference run since 2012 that has served as the only North American forum dedicated to advancing fundamental understanding of plant cell function across biological scales, species, and environmental conditions. The 2026 meeting will be held at Washington State University in Pullman, WA, from June 2–5. The program will include keynote lectures, research talks, poster presentations, workshops, and structured discussion sessions. It is carefully designed to address recent advances across diverse areas of plant cell biology and emerging technologies. Special emphasis is placed on professional development of early-career scientists, with opportunities for students, postdoctoral researchers, and recently appointed faculty to present their work and receive critical feedback. Participants will gain training in scientific communication and critical thinking, as well as build the network of professional contacts. These activities will strengthen preparation of workforce with deep expertise in biotechnology and bioinnovations for careers paths in academia, industry, and government. The meeting will also contribute to leveraging fundamental knowledge for the development of technologies that address societal challenges. Discussions on the application of artificial intelligence in cell biology will contribute to aligning the U.S. research community with activities directed at creating an AI-driven economy. The Plant Cell Dynamics 2026 meeting will advance mechanistic understanding of the molecular and cellular processes that govern plant cell architecture, polarity, division, and intracellular trafficking. The central objective is to accelerate the integration of molecular genetics, high-resolution imaging, biophysical approaches, and computational modeling to address fundamental questions in plant cell biology across spatial and temporal scales. The program includes two focused workshops: (i) AI-driven image analysis and quantitative modeling of cellular dynamics, and (ii) advanced genome editing strategies for functional dissection of cellular processes. Together, these components promote rigorous coupling of experimental and computational frameworks, aligning with the core priorities of the NSF. A defining feature of the meeting is the prioritization of early-career researchers as primary speakers and poster presenters, facilitating rapid dissemination of emerging methods and critical evaluation of new conceptual frameworks. Senior investigators will provide integrative perspectives that define key knowledge gaps, theoretical challenges, and opportunities for methodological innovation. Emphasis on AI-enabled quantitative imaging directly addresses current bottlenecks in high-throughput analysis and predictive modeling of cellular behavior. In parallel, discussions of genome editing and targeted perturbation approaches will strengthen experimental rigor and expand the toolkit for functional analysis. Collectively, the meeting will catalyze advances that connect subcellular dynamics to emergent physiological properties and developmental regulatory networks at the whole-plant level. This award is funded by the Cells, Development, and Physiology Section of the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract Chemical separations are foundational to modern chemistry, yet traditional liquid chromatography-mass spectrometry workflows often fail to detect many isomeric and conformational features of biomolecules critical to metabolism, disease, and drug efficacy. To address these limitations, the Clowers Research Group (CRG) at Washington State University is advancing ion mobility spectrometry and mass spectrometry techniques, leveraging gas-phase ion chemistry and high-resolution separations enabled by Structures for Lossless Ion Manipulations (SLIM). SLIM technology’s extended cyclic separations and controlled gas environments allow for the resolution of isomers and intermediates that conventional methods frequently miss. To quantitatively capture isomeric heterogeneity in complex biological systems, the CRG is implementing tailored multiplexed ion strategies such as Phased Ion Mobility Spectrometry. This technique aligns SLIM separations with ultra-performance liquid chromatography timescales, reducing spectral ambiguity and improving the resolution of co-eluting isomers in diverse ‘omics applications. Complementing these strategies, the group is integrating gas-phase labeling techniques, including hydrogen-deuterium exchange (gHDX) and ozone-induced dissociation. These methods provide dynamic insights into molecular interactions by capturing solvent- accessible regions and chemically selective modifications, enhancing the characterization of isomeric and conformational states. The integration of tandem collision-induced unfolding with SLIM and gHDX adds another layer of analytical depth, enabling the isolation and detailed examination of intermediate protein structures. This approach reveals conformational transitions and solvent-accessible regions during unfolding, helping to quantify molecular stability and refine structural models of biopolymers. By combining SLIM-based separations, multiplexing strategies, and gas-phase labeling workflows, the CRG’s research program is redefining bioanalytical approaches. These innovations aim to enhance the detection of isomeric heterogeneity, reduce analytical errors, and provide new insights into metabolomics, lipidomics, and structural biology in support of public health outcomes. 1

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Light profoundly influences the physiology and behavioral patterns of a wide range of organisms, from bacteria to mammals. Intrinsically photosensitive retinal ganglion cells (ipRGCs) are melanopsin – a photopigment – expressing retinal ganglion cells that are known to provide non-image-forming photic signals to regulate light- dependent physiology and behaviors, such as sleep, mood, memory, and learning. Adenosine is a retinal neuromodulator that rises during exposure to prolonged darkness and at night; its level oscillates in anti-phase to that of dopamine, another well-known retinal neuromodulator. Preliminary results demonstrate that application of adenosine regulates intrinsic light responses in M1 ipRGCs in ways that would not be expected based on the known signaling pathways. Furthermore, single-cell RNA sequence data analysis reveals that a subset of M1 ipRGCs co-express mRNA encoding the adenosine A1 receptor (A1R; Adora1) and the D1 receptor (D1R; Drd1). These findings suggest the possibility of A1R-D1R heterodimer formation and, thus, novel signaling pathways mediated by adenosine. The exact mechanisms behind these interactions, however, remain unclear. We hypothesize that A1R-D1R interactions enable novel signaling mechanisms for adenosine. To understand the effects of neuromodulators and their receptors on the ipRGC signaling pathways, we propose three Specific Aims: 1) Determine if the D1 receptor alters signaling by adenosine in M1 ipRGCs via whole-cell patch clamp electrophysiology and RNAscope; 2) Identify putative G-protein signaling partners for A1Rs, D1Rs, and potential A1R-D1R heterodimers using Bioluminescence Resonant Energy Transfer and cAMP Biosensors.; 3) To examine the behavioral effects of a potential A1R-D1R interaction on light-dependent physiology via a “jet-lag” paradigm. Overall, our experiments aim to contribute to knowledge of the biochemical and molecular mechanisms through which light impacts human health and performance by governing circadian rhythms, sleep, mood, memory, and learning.

NSF Awards · FY 2026 · 2026-03

The Cascade Topology Seminar (CTS) will meet for its 69th edition on May 30-31, 2026, at Portland State University, Portland, Oregon. The CTS is a semi-annual meeting of researchers and students with interests in topology in the greater Cascades region. Apart from bringing together the regional topology community in a welcoming and intimate setting, the CTS also connects them with selected invited speakers from outside the region. The scientific focus of CTS is being expanded from low dimensional and algebraic topology now to add the emerging subfield of applied topology. This will engender new developments in all three areas. The direct impact of CTS is in supporting the regional community to learn the latest developments in topology and to interact with leading researchers from within and outside the region. The broader scientific community as well as the general public will learn about CTS through its web page as well as the short professional interaction videos that will be shared widely. The CTS will provide junior researchers (graduate students and postdoctoral scholars in particular) with opportunities to share recent work and seek feedback from experts in a welcoming setting. With the expansion of its scope to include applied topology, such interactions will lead to the discovery of new results that are, or are combinations, of theoretical, computational, or applied flavors. The widespread sharing and access of the CTS web page will help popularize the focus areas. Furthermore, the early career participants will create short (2–3 minutes long) videos of informal interactions with the senior researchers at CTS discussing their work or other topics of professional interest. Apart from popularizing CTS, these videos will have a significant impact on developing a strong and supportive research community. CTS web page: https://sites.google.com/view/cascadetopologyseminar/. 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 · 2026-02

PROJECT SUMMARY Tumor necrosis factor (TNF)-α is a major inflammatory cytokine involved in the pathogenesis of several autoimmune and inflammatory diseases, including rheumatoid arthritis (RA). Current approaches aim to curb TNF-α-induced inflammation and tissue damage by treating patients with TNF-α inhibitors (specific antibodies or soluble receptors). The ineffectiveness of TNF inhibitors in >40% of patients partly indicates that we do not yet fully understand the underlying signaling mechanisms to effectively target TNF-α. While the use of TNF inhibitors has provided new insights into human immune and inflammatory systems and the mechanisms involved in disease processes, adverse events, and the re-emergence of the disease upon cessation of therapy suggest that other pathways might be involved in re-establishing the disease. Previous studies from our lab using RA synovial fibroblasts (RASFs) and preclinical models of RA not only shed light on the mechanisms by which TNF- α utilizes cell surface or cellular proteins to cause progressive inflammation and tissue destruction but also provided novel pharmacological approaches to suppress TNF-α’s function in RA. In this proposal, our novel preliminary data show that TNF-α utilizes Fn-14 (fibroblast growth factor-inducible 14), a receptor originally characterized for mediating TWEAK cytokine signaling. Knockdown of Fn-14 significantly reduced TNF-α- induced RANTES, MCP-1 and MMP-1 production, and cellular expression of podoplanin and cadherin-11 in human RASFs. In Fn-14-overexpressing cells, even low TNF-α concentrations synergistically induced inflammation, suggesting a potential undescribed mechanism exploited by TNF-α to propagate inflammation. RNA-sequencing analysis revealed >200 differentially expressed genes (DEG) affected by Fn-14 knockdown in TNF-α stimulated RASFs. Gene set enrichment analysis (GSEA) on the RNA-seq data revealed that IFN-α and IFN-γ pathway responses were significantly altered. Intraperitoneal administration of Fn14 antagonist (L524- 0366; 10 mg/kg; daily from the disease onset) inhibited collagen antibody-induced arthritis in DBA1/J mice. Based on these novel observations, we hypothesize that TNF-α utilizes the Fn-14 receptor as a non-canonical signaling pathway to induce inflammation. Therefore, in specific aim 1, we plan to determine the molecular mechanisms through which TNF-α utilizes Fn-14 in the synthesis of inflammatory and tissue-destructive mediators in human RASFs and other cells relevant to TNF-α-driven diseases such as IBD and psoriasis. In specific aim 2, we plan to decipher the role of Fn-14 in TNF-α induced function and phenotypic changes in RASFs by using complex multicellular ex-vivo systems, including synovium-on-a-chip model and RA synovial tissue explants. Finally, in specific aim 3, we plan to validate the in vivo efficacy and inhibitory potential of Fn-14- targeted approaches in acute and chronic models of TNF-α-inflammation. The success of these studies will elucidate the role of Fn-14 as a non-canonical signaling receptor of the TNF-α signaling pathway and validate Fn-14 blockade as a potential therapeutic approach for the treatment of RA.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY Cannabis is the most used illicit drug among pregnant mothers. As the number of states with legal cannabis continues to increase, there has been a concomitant rise in the rate of maternal cannabis use. This is particularly troubling given that prenatal cannabis exposure could interfere with neurodevelopment and increase the risk for cognitive dysfunction later in life. Preclinical animal models are advantageous in that they provide fine control over potentially confounding variables. However, current models of maternal cannabis use have been plagued by methodological concerns that limit the translatability of these data to human populations. Our laboratory has generated important new data using a novel, translationally relevant model of cannabis vapor exposure in pregnant rat dams. This new method of administration uses custom-designed equipment to deliver discrete ‘puffs’ of vaporized, plant-derived cannabis extracts in a response-contingent manner. Using this approach, we have shown that prenatal cannabis exposure produces marked deficits in cognitive flexibility in offspring by impairing acquisition and maintenance of newly updated strategies. This ability is known to depend on corticostriatal neurons, namely glutamatergic projections from the prelimbic (PL) region of the medial prefrontal cortex to the nucleus accumbens (NAc). Dynamic release of glutamate and dopamine (DA) in the NAc coordinates flexible decision making and is constrained by local endocannabinoid (eCB) signaling. Our data also show sex-dependent alterations in spontaneous glutamate release onto NAc-projecting PL neurons in cannabis- exposed offspring from dams that self-administered cannabis. However, we still do not know 1) whether this corticostriatal circuit and local DA and eCB transmission are differentially recruited during flexible decision making in cannabis-exposed vs. control offspring, 2) whether long-term functional alterations occur within this pathway, and 3) whether early intervention strategies can prevent cannabis-related impairment. To address these gaps, we will use complementary projection-specific approaches to identify long-term alterations that give rise to cognitive flexibility deficits in cannabis-exposed offspring and test novel pharmacologicala and circuit- based treatment strategies for restoring behavioral and synaptic function. In Aim 1, we will conduct in vivo fiber photometry recordings of 1) Ca2+ transients in PLNAc neurons and 2) glutamate, DA, and eCB dynamics in the NAc using novel biosensors. In Aim 2, we will employ viral labeling to examine intrinsic excitability and optically evoked transmission from PLNAc neurons, as well as endocannabinoid-dependent plasticity at PLNAc synapses. In Aim 3, we will test whether in vivo depotentiation of synaptic activity at PLNAc synapses or juvenile administration of the FDA-approved steroid precursor pregnenolone can restore cognitive flexibility and recalibrate corticostriatal synaptic transmission in cannabis-exposed offspring. Completion of this work will have a broad, sustained impact by delineating cognitive effects of maternal cannabis use in exposed offspring and their neurobiological underpinnings, which is very timely given the current wave of cannabis legalization.

NSF Awards · FY 2026 · 2026-01

Streams and associated riparian areas supply water for a multitude of downstream uses, reduce wildfire risk, and provide habitat for many species. Streams are increasingly threatened by drought, flood, and changing patterns of land and water use. Accordingly, billions of dollars have been spent to restore these ecosystems so that they can continue to provide vital services for people and support valuable fish and wildlife resources. Stream restoration increasingly focuses on beavers, which were once common throughout North America but were harvested nearly to extinction in the 18th and 19th centuries. Beaver-based restoration, which includes reintroducing beaver to historically occupied habitat and building structures that mimic beaver dams, has the potential for far-reaching beneficial impacts on stream and riparian ecosystems. However, it is not clear how these benefits vary across different environments and with different restoration practices. This proposal will provide critical data on how beaver-based restoration improves stream and riparian health. Such information is critical for promoting responsible use of restoration funding and effective stewardship of natural resources. This proposal will investigate the effects of beaver-based restoration on stream and riparian habitats and associated fish and wildlife species across divergent precipitation regimes using a large-scale, five-year field experiment and a complementary set of artificial stream experiments. Specifically, investigators will evaluate: 1) the effects of beaver reintroduction and the construction of beaver dam analogs on fish, amphibians, birds, bats, and mammals in wet vs. dry climates, 2) the specific mechanisms underlying the effects of beaver-based restoration on key habitat features under different streamflow conditions, and 3) the efficacy of different beaver-based restoration practices for achieving specific habitat and biodiversity outcomes. The field experiment will feature four types of sites: 1) beaver reintroduction (the translocation of beavers into currently unoccupied sites), 2) beaver-mimicry (the construction of beaver dam analogs), 3) unrestored controls, and 4) beaver-occupied reference sites. These site types will be replicated on the wet west side and the drier east side of the Cascades Range in the Pacific Northwest. The artificial stream experiments will provide detailed mechanistic insight into how different types of beaver-related structures, and different spatial layouts of such structures, influence stream and riparian habitat variables under varying streamflow conditions. This project is jointly funded by the Divisions 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.

NSF Awards · FY 2025 · 2025-11

Water shortages occur in many places in the world due to increasing water demand and changing water availability. Navigating changes in water resources, therefore, requires a global workforce with the intellectual depth and breadth of expertise to respond to water-related challenges. The Partnerships Along the Headwaters of the Americas for Young Scientists Program (Pathways Program) develops such a workforce with training to work in interdisciplinary, international, and interorganizational (I3) settings throughout the Americas. Specifically, the Pathways Program provides educational and research experiences at research sites in the Andean Mountain Range (Ecuador, Peru, Chile, and Argentina). Within the I3 context, the Pathways Program provides U.S. students with experiences in problem-based learning and teamwork-oriented collaboration, equipping them first to cross disciplinary borders, then organizational and international borders. Students acquire the skills necessary to excel in a wide range of water-related careers. Shifting climate patterns, changes in land use, escalating water demand, and rigid water policies have amplified the risk of enduring ecological damage and political conflict in river basins worldwide. Addressing and resolving water conflicts requires a robust understanding of various disciplines within varied interdisciplinary contexts. For instance, students investigating the human dimensions of water resources often require a solid understanding of the hydrological landscape and the physical barriers and opportunities that may influence water policy and norms. Conversely, hydrologists and ecologists are more likely to produce and communicate actionable science if they can interpret the social and political factors exacerbating water management issues. The Pathways Program facilitates collaborations between U.S. students and international researchers, who will provide research expertise, regional knowledge, and access to data and field sites. This will enable the exploration of water resource system connections across the Americas and provide opportunities for research collaborations and the development of intellectual capacity through educational activities. Objectives of the Pathways Program are to: (1) Build, deliver, and disseminate a novel model for graduate-level international research experiences including research-related professional development; (2) Use and expand an international research network to advance fundamental knowledge of socio-ecological systems; and (3) Enhance competency in I3 with community engagement to evaluate socio-ecological patterns and climate adaptation. Student research results improve society's ability to build adaptive capacity to changes in climate by expanding an interdisciplinary network of researchers. The training plan prepares U.S. students for I3 research to develop a set of elite water resources professionals, with scientific and cultural sophistication to work effectively in international settings. Through the multi-disciplinary Pathways program and its focus on internationalizing research and professional development this project grows international research capacity for U.S. graduate students and early career faculty. 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 · 2025-10

Wheat is the world’s most traded crop and plays a central role in feeding the world, providing nearly 20% of all calories consumed by humans and serving as the primary source of plant-based protein for billions. Securing a stable wheat supply is essential, especially as extreme weather events like drought continually threaten food security and economic stability. This project harnesses artificial intelligence to transform wheat breeding, enabling faster development of drought-tolerant varieties. By advancing these innovations, this research directly contributes to securing food supplies, benefiting farmers to adapt to environmental challenges. This project addresses the challenge of improving wheat breeding by overcoming limitations in traditional methods, which struggle with the complex interactions between wheat genetics and diverse field environments, especially when analyzing the vast datasets now available from genome sequencing, weather stations, and high-throughput phenotyping technologies such as drone imaging. The project leverages feature-embedded neural networks to overcome these limitations, integrating genetic markers, environmental factors, and plant growth measurements to predict wheat performance. A core set of 400 wheat lines are being genotyped and evaluated over three years in two locations, under both normal and drought conditions. Environmental data is collected in parallel to create environmental indices and response parameters. The trained neural networks incorporate sequence variants and environmental indices in their first layer, embedding features representing quantitative trait loci identified via genome wide association studies in a second layer. The resulting tools will be made available as open-source software, enabling scientists worldwide to apply these innovations in their own breeding programs. In addition, the project includes training initiatives designed to equip the next generation of agricultural scientists with the skills needed to develop improved wheat varieties using cutting-edge technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

1 Project Summary: Veterinary teaching hospitals are important tertiary care centers for high- 2 quality, cutting-edge care of animals. As veterinary medicine has evolved to resemble human 3 medicine in complexity, it has become apparent that complications historically observed in 4 human medicine are now of consequence in veterinary medicine and efforts are needed at the 5 hospital level to reduce harm. Antimicrobial resistance (AMR) is a critical human health 6 challenge and is reported more frequently in veterinary medicine. Not only do resistant bacterial 7 infections impact animal health and welfare, but they also have potential to cause negative 8 human health consequences through transmission of resistant bacteria or resistance genes 9 through animal contact or the shared environment. Little information is available to assess 10 impact of AMR bacterial infections in veterinary medicine, but presumably there is a significant 11 cost both in morbidity and mortality, economic output for treatment, lost productivity in animal 12 agriculture and emotional distress of animal owners and caregivers. In order to understand and 13 mitigate infections from antimicrobial resistant bacteria in a veterinary teaching hospital, the 14 collection of data related to patient outcomes, antimicrobial use and bacterial characterization, 15 including whole genome sequencing, must be performed. While obtaining data is a primary step, 16 data management, visualization and analysis is must be built to ensure data can be useful for 17 stakeholders in teaching hospitals, including faculty clinicians, trainees, students and staff. Data 18 dashboards provide this tool to tailor information that is accessible for users with varying skill in 19 data analytics. Dashboards can be configured to provide pertinent information on individual 20 providers, service and hospital wide antimicrobial use, rates of surgical site infections, 21 antimicrobial susceptibility test patterns, rates of resistant bacteria and results of environmental 22 surveillance of resistant bacteria. The purpose of this proposal is to create user-friendly data 23 dashboards for multiple metrics for the Washington State University College of Veterinary 24 Medicine Teaching Hospital for use in antimicrobial stewardship and infection prevention and 25 control efforts and education. Additionally, these dashboards could be shared with other 26 Veterinary Teaching Hospitals. 27 28

NSF Awards · FY 2025 · 2025-09

Spillover of infectious diseases from wildlife to humans and livestock is a pervasive risk to the health and welfare of human populations around the world. Effective management of this risk is facilitated by early detection of changes in the frequency of spillover events. This research will develop new mathematical models and statistical methods that allow changes in the rate of spillover to be detected from the fossil record of past infection that remains imprinted on human and animal immune systems. The general methodology developed by this project will be rigorously tested using simulated data and applied to Rift Valley fever virus, a pathogen that poses a high risk of global expansion with potentially devastating consequences for human health and agriculture. Work on this project will train students in cutting edge mathematical and statistical methods and support an international workshop where software developed by the project will be introduced and instruction on its use provided. Predicting how zoonotic infectious diseases change over time is a fundamentally important challenge with few general mathematical solutions. Central to addressing this problem is disentangling historical changes in the rate or “force” of spillover from background biological processes, such as age-specific infection and wanning immunity, which can cloak or mimic the signal of temporal change. Existing statistical methods to infer historical changes in the force of spillover for zoonotic pathogens rely on piecemeal solutions tailored to specific scenarios, ignore interacting background processes, use only single immunological markers, and have failed to rigorously evaluate parameter identifiability. To fill this gap, this project will develop a general mathematical framework describing the probability that an individual is in a specific multivariate immune state as a function of age and time using a coupled system of partial differential equations (PDEs). Approximate and numerical solutions to this system of PDEs will enable a Bayesian statistical framework for inferring recent historical changes in the force of spillover in the presence of alternative biological processes. Testing this statistical framework using extensive, biologically realistic simulated datasets will allow the identifiability of historical change in force of spillover to be evaluated. Application of this methodology to Rift Valley fever virus, a pathogen with significant pandemic potential, will determine whether increasing case counts in East Africa result from fundamental shifts in disease epidemiology or from increased disease surveillance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY / ABSTRACT Alcohol use disorder (AUD) is a heterogeneous and relapsing illness. Poor treatment outcomes for AUD highlight the need to investigate the mechanisms that increase risk for harmful alcohol use. Two factors that have been independently associated with harmful alcohol use are stress and executive function. However, the use of objective biomarkers and longitudinal assessments to assess these variables in the real-world settings is limited. Further, these variables are predominantly investigated in treatment-seeking individuals, potentially limiting the generalizability of findings to the larger AUD population. Thus, this K01 will investigate the relationship between objective measures of stress, executive function, and alcohol use in laboratory and real-world settings in non-treatment seeking individuals with AUD. Aim 1 will characterize the relationships between laboratory and real-world stress biomarker measures. Aim 2 will examine the associations between stress and alcohol biomarkers in real-world settings. Aim 3 will determine if the relationship between stress and alcohol biomarkers is moderated by executive function in real-world settings. Findings from this study have the potential to improve assessment accuracy and the field’s understanding of mechanisms underlying the addiction cycle, as well as inform interventions for AUD. Situated in a supportive environment within the Department of Community and Behavioral Health at Washington State Univeristy Health Sciences Spokane, I have access to well-funded mentors, space, equipment, and protected time to conduct my research. To gain the expertise needed to complete the proposed aims, a comprehensive mentoring training plan has been developed for me to: 1) gain expertise in the identification and assessment of mechanisms that drive drinking behavior in order to inform interventions; 2) gain expertise in collecting, managing, and analyzing intensive longitudinal data (i.e., ecological momentary assessment); and 3) acquire proficiency in utilizing biomarkers and innovative technologies to assess stress, cognitive function, and alcohol use in laboratory and real-world settings. The proposed training plan integrates mentorship from experts in basic and applied human research (McPherson), mechanisms of behavioral change and the ecological validity of remote neuropsychosocial assessments (Chaytor), intensive longitudinal assessments and ecological momentary assessment methods (Cleveland), mobile health technologies (Ghasemzadeh), stress and the addiction cycle (Sinha), and biomarker analyses (Hill-Kapturczak). This Mentored Research Scientist Development Award will provide vital protected time, resources, and mentorship that will build on my previous training and support my long-term career goal of becoming a successful independent investigator with a research program that utilizes basic and applied research methods in real-world settings to investigate mechanisms that underlie addiction and inform interventions for substance use disorders.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Brian H. Clowers of Washington State University leads a team of researchers developing a tractable, high-performance analytical platform to explore chemical structure and behavior in the gas phase. Built using economical printed circuit board (PCB) technologies, these systems are expected to enable scientists to separate and analyze molecules based on subtle differences in shape, charge, and chemical reactivity—capabilities essential to fields ranging from medical diagnostics to materials chemistry. The ability to isolate and study different molecular forms in the gas phase will provide critical insight into chemical processes that are not accessible by traditional solution-based methods. This project will emphasize workforce development through direct student engagement in instrument design, data analysis, and experimental planning. The open-source dissemination of hardware and software components aims to ensure broad impact across academic, industrial, and educational communities, contributing to national capacity in measurement science. Technically, the project will apply modular, PCB-based ion manipulation systems to advance gas-phase separation methods and ion chemistry through three main objectives: (I) characterizing structural heterogeneity of fragment ions formed after ion activation; (II) examining site-specific solvent interactions and isotope exchange kinetics to study the initial stages of solvation; and (III) conducting ion-ion reactions to evaluate chemical reactivity under conditions not accessible with conventional instruments. These systems will support extended ion residence times and controlled gas-phase reaction environments following multidimensional separations. Project outcomes will inform the design of new analytical workflows that enhance measurement sensitivity and selectivity, supporting the characterization of complex mixtures in fields such as structural biology, synthetic chemistry, and molecular analysis. In carrying out these research activities, participants will engage advanced chemical instrumentation, experimental design, and data interpretation—experiences that are expected to strengthen technical expertise and support future contributions to the STEM workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Physical systems at nanoscopic scales or ultracold temperatures often exhibit pronounced quantum mechanical behavior. The laws of quantum mechanics are markedly different from classical mechanics, which opens up new possibilities for advanced applications in sensing, secure communication, computation and more. To harness this potential, fundamental studies are required that probe and characterize the quantum regime. To this end, the research team will employ an ultracold-atomic-gas platform as a well-controlled model system. Using laser-cooling and related techniques, a cloud of atoms will be cooled until a Bose-Einstein condensate (BEC) forms as a state of quantum mechanical matter. Additional specially tailored laser fields will be used to create a variety of quantum phases, and to study dynamics near critical points where phase transitions occur. Near these points small changes of system parameters can lead to strong changes in the properties of the system, making those points particularly promising for future sensing applications. The resulting data will provide important benchmarks for accompanying theoretical research. The experiments will be conducted at Washington State University, where they will play a key role in the education of students in the lab and in the classroom, contributing to the development of a workforce that is ready to meet the needs of the rapidly growing quantum industry. The research builds on extensive experience in the PI’s lab with spin-orbit coupled BECs. These systems consist of a BEC held in an optical trap, onto which additional Raman lasers are shone that couple different hyperfine states. An additional optical lattice is used to create state-independent couplings. The system is dynamically very rich, hosting a variety of quantum phases and phase transitions. A sequence of experiments will study quantum phase transitions induced by rapid quenches of system parameters. Topics of particular focus include Josephson physics, quantum fluctuations, chaos assisted tunneling, and Kibble-Zurek physics. A hydrodynamic approach based on large dark-bright soliton trains will also be studied as a complementary approach providing a new viewpoint on the underlying physics. The acquired data will contribute to the development of a comprehensive theoretical understanding of quantum dynamics, paving the way for future applications in quantum sensing or quantum information processing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project advances the theory and methods underlying the development of computationally lightweight control algorithms that ensure the safe and reliable operation of autonomous systems in safety-critical applications. Such methods can benefit a broad range of industrial uses, including autonomous drone delivery, robotics, manufacturing, aerial and ground transportation, autonomous driving, and precision agriculture. To fully realize their benefits, autonomous systems must be capable of making reliable decisions in real time while operating in complex environments with rapidly changing constraints. This is especially challenging because modern autonomous systems are often designed to reduce cost, weight, and energy consumption, which limits their onboard computing capabilities. To address these challenges, this project pursues a systematic and theoretically justified framework, grounded in extensions to Control Barrier Function (CBF) methods, that enables the design of control algorithms ensuring the satisfaction of safety constraints even when computational resources are severely limited. Control Barrier Functions (CBFs) hold significant promise for addressing constrained control challenges in nonlinear systems and for providing computationally lightweight solutions that ensure the safety and reliable operation of autonomous systems. At the same time, systematic design procedures for CBFs are currently limited to specific classes of systems and constraints. To provide CBF-based solutions for systems operating in environments where operating conditions and constraints can change rapidly, this research will expand the applicability of CBFs through the development of a novel class of CBFs parameterized by the reference command. The project will establish a rigorous theoretical foundation for such parametric CBFs and their onboard implementation. Methods for enhancing onboard computations to ensure real-time computational feasibility will be developed. The advances will be pursued by integrating techniques from control theory, set invariance, machine learning based on neural networks, and computational optimization algorithms grounded in robust-to-early-termination optimization. The outcomes of this research will include methods, algorithms, and theoretical guarantees that support their application. The proposed methodologies will be validated through both simulations and real-world case studies, such as drone delivery applications, to demonstrate their practical effectiveness and potential for real-world technological impact, ultimately benefiting the U.S. economy and society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The research will provide new insights into how lipids in the plant thylakoid membranes, the site of photosynthesis, control and fine-tune harvesting of sunlight by highly specialized light-harvesting complex II proteins (LHCII) through quantum photochemistry. The work will identify the optimal lipid membrane composition for light harvesting, balancing the competing demands of photoprotection vs. photo-efficiency. This information is crucial for optimizing photosynthetic energy conversion in crops to meet the challenges of the 21st century. Educational activities that include student training and public outreach to encourage young children to explore how science and engineering can help improve our world will contribute to the development of a STEM workforce and general scientific literacy. The precise influence of the physical properties of membrane lipid bilayers, such as lipid chain length and the volume they occupy, on the structure and function of LHCII and other photosynthetic proteins is unknown. This research aims to utilize a recently developed LHCII proteoliposome pipeline, combined with single-molecule spectroscopy and molecular dynamics simulations, to elucidate the role of physical lipid properties in the fine-tuning of light harvesting by individual LHCII proteins. Proteoliposomes are small spherical vesicles comprising a lipid bilayer with incorporated membrane proteins such as LHCII. The experiments aim to validate our hypothesis that a specific lipid length and morphology (volume) optimize sunlight collection by LHCII, enabling efficient photochemistry by tuning protein structure to optimize excitation transfer at the quantum level. To achieve this, a distinctive real-time, feedback-driven single-particle tracking system will be employed for the comprehensive characterization of lipid-protein interactions at the level of individual LHCII molecules within well-defined proteoliposomes. These experimental results will be simulated through molecular dynamics simulations, offering profound mechanistic insights into how the physical properties of lipids influence the structural and functional attributes of LHCII. The integrated methodology constitutes an innovative research platform that facilitates the systematic analysis of lipid-protein interactions with sub-molecular resolution on individual LHCII proteins, adaptable to other systems. The anticipated outcomes are expected to enhance our broader understanding of physicochemical dynamics within membranes and their role in the sunlight-harvesting process in plants. This contribution is of considerable significance, providing unprecedented mechanistic insight into how lipids govern the functionality of a crucial photosynthetic membrane protein. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Increasing energy demands coupled with dependence on fossil fuels have promoted research into efficient renewable energy sources such as solar, wind, geothermal and wave energy. These intermittent energy sources must be used in conjunction with devices capable of storing energy. Owing to their high energy density, affordability, scalability, and environmental safety, water-based (aqueous) metal-ion batteries have been proposed as a viable solution for large-scale or grid-scale energy storage. Zinc, a naturally abundant and non-toxic metal has emerged as a front-runner in the development of aqueous-based batteries. Unfortunately, current limitations of aqueous batteries drastically reduce their efficiency and durability. As a result they currently cannot outperform Lithium-ion batteries to meet national energy demands. The goal of this project is to overcome the performance-inhibiting limitations of aqueous zinc-based batteries through the use of weak to moderate magnetic fields and realize safer and sustainable energy storage solutions. Permanent magnets can be integrated into battery housings to induce phenomena that benefit performance, such as fluidic rotation and spatial control over reactive chemical species. They offer an elegant and simple approach towards the realization of high-performing aqueous zinc-based batteries. This project focuses on advancing the utility and understanding of the role of magnetic fields in electrochemical systems with an emphasis on electrode materials and electrochemical processes related to aqueous Zn-based batteries. The project will address the critical need in correlating systematic experimental data with computational modeling to help understand effects of magnetic fields on i) the structure of the solid/electrolyte interface under various experimental conditions, ii) regional and global ion-transportation, and iii) the performance of aqueous Zn-based batteries. The central hypothesis of this project is that incorporating magnetic fields into reactions, processes, and device operation will enable efficient and tunable control over critical surface processes and fluid flow, resulting in enhanced device performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Through the process of domestication, humans intensified their use of animal populations and fundamentally altered their respective interactions with the environment. Studying faunal domestication through the archaeological record thus provides critical insights into past subsistence systems and human-environment relationships. Through expanded domestication research, archaeologists are better positioned to develop more sophisticated models comparing the process of animal management and domestication across regions. This research contributes to advancing research and refining methodological approaches in many fields including evolutionary biology, animal science, and archaeology. The biotechnological and analytical techniques used in the project, including genetic, isotopic, and morphometric analyses, are continually developed and refined in archaeological research. Finally, the project provides opportunities for hands-on laboratory training in archaeological chemistry for student researchers, and the research results are presented widely to reach both academic and public audiences. The researchers analyze archaeological faunal remains from an area located between the two currently recognized origin centers of ancient domestication. Through combined genetic, isotopic, and morphometric analyses, the investigators reconstruct past husbandry practices, and determine whether the region’s domestic fauna were adopted from other regions, or whether they represent a novel domestic lineage derived from local wild animal populations. The proposed work thus contributes to a more holistic understanding of faunal domestication and diffusion, and to more detailed reconstructions of trade, economy, and both ritual and subsistence use throughout ancient North America. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Cyber-physical systems, such as those found in autonomous vehicles, drones, medical devices, and power plants, require real-time guarantees to ensure predictable and safe operations. However, this predictability can introduce a hidden security risk: covert timing channels. These are unintended communication channels that allow malicious actors to secretly exchange information between software tasks by manipulating the execution timing. Such vulnerabilities can enable attackers to disrupt normal operations or launch targeted attacks by knowing when critical applications are scheduled to run, posing serious threats to safety and reliability. This CAREER project investigates the existence of covert channels in real-time schedulers and develops strategies to detect, measure, and mitigate them. The success of this project will enhance real-time cyber-physical applications in terms of their security, safety, and resilience. The project's long-term goal is to understand how deterministic behaviors in real-time systems contribute to covert timing vulnerabilities and to develop improved schedulers that can prevent information leaks. By systematically analyzing how timing behaviors in real-time schedulers can be exploited, this study develops new algorithms to detect and mitigate these covert channels. In parallel, the project will devise metrics to quantify information leakage and evaluate the effectiveness of defense strategies. The proposed techniques will be integrated with existing real-time operating systems, and their efficacy will be validated using off-the-shelf cyber-physical testbeds (e.g., multi-terrain rovers and drone swarm systems). In addition to advancing foundational knowledge at the intersection of cybersecurity and real-time cyber-physical systems, this project encompasses comprehensive educational and outreach efforts designed to promote equitable participation. These include publishing a free online textbook, offering international research opportunities for graduate students, and actively involving undergraduate and K-12 students in research and outreach activities. 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.