Oregon State University

NSF Awards · FY 2024 · 2024-10

This project develops FaaSr, a new software that will facilitate the programming and deployment of scientific computing applications written in the R language in Function-as-a-Service (FaaS) cloud computing infrastructures. The FaaS model of cloud computing supports dynamic, on-demand execution of computing functions in servers that are automatically provisioned and managed, in a way that is both cost-effective and scalable: users do not need to manage cloud servers (including on-demand scaling) nor pay for idle time of unutilized servers. The FaaS model thus has much potential for reducing the complexity and cost of performing scientific computing in cloud infrastructures. To date, however, FaaS platforms have been primarily designed to support Web-based applications, resulting in a major gap between existing FaaS platforms and the scientific community. This gap is particularly evident in the environmental sciences, where R is the focal programming language. This is because: 1) there is no native support for the R language in FaaS platforms, and 2) each FaaS platform has a unique interface to deploy and manage workflows consisting of multiple functions, thereby creating barriers for users to develop and deploy applications on one or more FaaS platforms. This project bridges this gap by developing open-source software to accelerate the adoption of event-driven FaaS workflows for scientific applications. The FaaSr software will be distributed as an easy-to-install R package and will provide simple interfaces to programmers, while supporting multiple open-source and commercial cloud computing infrastructures. The software will support a wide range of scientific computing applications, in particular those that require dynamic event-driven processing (such as forecasting and continuous data quality) in environmental science subfields (including ecology and biodiversity). Ultimately, the project aims to develop scalable, generalizable, and robust workflows that will advance the capacity, practice, and training opportunities for ecological forecasting, an active area of scientific research poised to significantly increase predictive capacity for effective environmental decision-making and management. The FaaSr software developed in this project will greatly expand the adoption of FaaS cloud computing infrastructure. Currently, there are significant challenges to be overcome before scientific applications written in the R language can fully realize the potential of FaaS platforms, because R is not supported natively, and because different platforms have different, incompatible programming interfaces. Furthermore, scientific applications require workflows consisting of multiple functions that are executed dynamically and communicate by exchanging data as files in cloud storage. Different FaaS platforms have different programming interfaces to accomplish these capabilities, leading to increased complexity for developers and users. This collaborative, interdisciplinary project overcomes these challenges by integrating expertise in distributed systems, ecology, and forecasting together to design and implement software that: is driven by scientific computing use cases; creates easy-to-use interfaces; and builds on state-of-the-art distributed computing techniques and frameworks. Specifically, the FaaSr software will make multiple novel technical contributions, including: 1) it will allow end users to program a workflow at a high abstraction level and with the R language; 2) it will include a unified, easy-to-use interface for handling event invocation and argument parsing that hides the complexity of programming for multiple FaaS interfaces from developers, while supporting multiple FaaS frameworks, including GitHub Actions, OpenWhisk, IBM Cloud Functions, and Amazon Web Services Lambda; 3) it will include an easy-to-use interface for handling cloud data storage and access that hides low-level details (e.g., access endpoints and credentials) using de-facto standard interfaces and file formats; and 4) it will implement a unified approach to compose directed acyclic graph workflows that can be automatically mapped to programming interfaces supported by different FaaS platforms. Experiences with the design, implementation, and deployment of FaaSr will contribute new techniques and technologies in distributed/cloud computing, with lakes and reservoirs studied as part of this project providing a realistic testbed for assessing performance, extensibility, and availability of the software. Furthermore, the team will build on and expand its existing program for cross-disciplinary research exchanges of undergraduate and graduate students that provide novel training at the intersection of computer science, freshwater science, and ecosystem modeling. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the NSF 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.

NSF Awards · FY 2024 · 2024-10

This project seeks to support and study the 2024 Advancing Research Translation (ART) awardee institutions and their corresponding mentor institutions, including how they change to better value and support faculty innovation & entrepreneurship (I&E), including in in promotion & tenure (P&T) processes and structures. This work will support our Nation’s economy and higher education’s evolution to respond to modern societal needs, through accelerating research translation. As well, the project will support a greater diversity of faculty work, as solutions for our most pressing socio-scientific problems. Specific examples of these impacts include: (a) the increased level of engagement on university campuses around societally focused work and related faculty advancement, (b) the training of the next generation of innovators, entrepreneurs, intrapreneurs and researchers, (c) the societal impact realized from university discoveries through translation to the market, and (d) original research exploring these phenomena and relative efficacy of associated strategies and tactics. In addition, this program could serve as a model for other efforts to reform the academy’s reward structure on university campuses to be more responsive to the needs of society, via necessary, but complex and challenging, cultural change. The project activities cover multiple aspects: (a) site visits to ART awardee institutions for immediate on-the-ground support, (b) cohort-based model for ongoing development and support of institutional leaders, (c) process change pilots through institutional case studies, and (d) longitudinal study of ART institutions and the greater higher education landscape with respect to ART goals. All programming is targeted specifically for the ART awardee institutions (and their mentor institutions) to enable building of scalable models for ongoing support of current and future ART cohorts. This work will significantly advance the understanding of how to accelerate transition outputs on ART awardee institution campuses, including through addressing misalignments with (inter)organizational cultures, functioning and composition, and the reward structures and the processes at play at and between institutions of higher education and its sub-organizations. Case studies will inform national strategies, as well as colleges and universities hoping to advance ART goals and related culture change, including future ART program applicants - thereby increasing the likelihood for successful outcomes of reform efforts broadly. 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

Drinking water safety is threatened globally by increasing phytoplankton blooms in lakes and reservoirs, which pose major threats to water quality via harmful toxins, scums, and changes in taste and odor. To improve drinking water management in the face of global change, this project proposes to develop the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty in water quality predictions. If managers had forecasts of phytoplankton blooms, they could preemptively act to mitigate water quality impairment, such as by adapting water treatment, thereby decreasing costs and improving drinking water safety. The project team plans to integrate cutting-edge lake ecosystem and statistical modeling with new computing capacity to deliver 1 to 35 day-ahead forecasts of phytoplankton blooms to water managers daily for several U.S. lakes. Researchers intend to work with water managers on the forecasting system to generate valuable knowledge about how best to effectively communicate forecasts for improved water resource decision-making. The project team also plans to develop teaching modules on forecasting and freshwater ecosystems for high school students and community college students in water management/wastewater certificate programs, thereby improving both water quality and water worker training in central Appalachia. The teaching modules will be made available to colleges and universities across the U.S. as part of an existing educational program that has reached over 100,000 students to date. Phytoplankton blooms in lakes are a type of emergent behavior that can have ecosystem-scale, societally important consequences by degrading water quality, yet are challenging to predict. A fundamental Rule of Life governs this behavior: ecosystem-scale emergence is a function of environmental dynamics operating on individual organisms (e.g., temperature and light effects on phytoplankton growth rates), mediated by population and community processes (e.g., multi-species interactions that promote increased phytoplankton biomass). This project will apply a Rules of Life approach to solve a major societal problem by implementing emergent phytoplankton behavior into predictive models to generate real-time lake water quality forecasts with cloud and edge computing tools. This research is uniquely enabled by a transdisciplinary team with expertise that spans the biological sciences, social and decision sciences, physical sciences, computer and data sciences, and statistics, as well as long-term partnerships with managers, educators, and community members. Advances from this convergent, use-inspired research approach will include: 1) improved understanding of how a Rule of Life can be used to predict emergent, ecosystem-scale phenomena; 2) new cyberinfrastructure for transferring data from environmental sensors to the cloud; 3) generation of novel, computationally-tractable statistical methods for real-time forecasting with individual-based models; 4) greater understanding of how water management and ecosystem dynamics interact to control phytoplankton; 5) creation of new tools that effectively communicate forecast uncertainty; and 6) capacity-building by providing innovative training for researchers, managers, and students that broadens STEM participation across central Appalachia. Through novel, cross-disciplinary integration, this project aims to develop a forecasting system that will become a model for drinking water systems in communities 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 2024 · 2024-10

This project aims to serve the national interest by exploring community-based participatory research and relevant theories from disability studies and organizational change to implement inclusive practices for developmentally disabled employees, interns, and volunteers in informal STEM education settings such as zoos and aquariums. The investigator will implement a mentorship and professional learning plan specifically focused on three key areas: 1) developing deeper understanding of theories and methods from disability studies, 2) applying community-based participatory research with the developmentally disabled community, and 3) exploring theories of organizational change. The project will advance knowledge about inclusive hiring and employment practices in informal STEM education settings, specifically in zoos and aquariums, for developmentally disabled employees, interns, and volunteers and deepen understanding of organizational characteristics that advance or limit the implementation of these practices. This project explores the overarching research question: What is the experience of developmentally disabled individuals working as employees, interns, and volunteers at zoos/aquariums as the organizations change to implement inclusive employment practices? The study will be conducted in partnership with developmentally disabled researchers and advisors using community-based participatory methods to explore the research question. The study will apply qualitative case study methods, specifically using a sociological descriptive and multiple case study approach, with developmentally disabled zoo and aquarium employees, interns, and volunteers as case study participants. Data collection activities will include listening sessions, interviews, observations, and photo journaling with developmentally disabled employees, interns, and volunteers at participating zoo/aquarium sites and interviews with decision-makers at these sites. The data will be coded and analyzed using qualitative methods, including thematic and cross-case analysis, to describe the experience of developmentally disabled individuals at case study sites and how case study sites implement inclusive workplace practices. The research findings will be informative for zoos, aquariums, and potentially other informal science education settings that share major characteristics with zoos/aquariums as they engage in work to implement inclusive workplace practices, with the ultimate goal of creating equitable zoo/aquarium workplaces for employees who identify as having a developmental disability. This project is supported by the Mid-Career Advancement program that offers opportunities for scientists and engineers to substantively enhance and advance their research program through synergistic and mutually beneficial partnerships. This project is also supported by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments, and the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce 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 2024 · 2024-10

Oceanic hotspot tracks form when plumes of hot rock upwell from the deep mantle and melt. Approximately 60 million years ago the Ontong Java Plateau (OJP) moved over the Samoan and Rarotonga plumes. The OJP is the largest volcanic structure on Earth. This project will sample seamounts on top of the OJP to study the interactions between plumes and the overlying large and thick OJP. Early-career scientists, graduate students, and undergraduate students will take part in the cruise. In port the science team will lead ship tours for local public schools and media. While at sea the PIs will make a training video that outlines how to prepare for and carry out a successful cruise. Recent work offers tantalizing hints that the currently active Samoan hotspot may be a long-lived mantle melting anomaly, or hotspot, that has been active since the Cretaceous. Many of the 87 to 106 Ma volcanoes in the Magellan Seamount Chain, located north of the OJP, have Samoan hotspot geochemical signatures. Critically, their ages and locations match predictions for the Samoan hotspot made using absolute plate motion reconstructions. The implications for identifying a Cretaceous segment (87-106 Ma) of the Samoan hotspot are profound. First, using the same plate motion models, reconstructions of the Samoan hotspot show that the OJP passed over the Samoan plume at ~60-30 Ma, and over the Rarotonga plume—a second, possibly-long-lived hotspot—at ~60-50 Ma. Second, passage of the extraordinarily thick OJP lithosphere over the Samoan and Rarotonga plumes would have resulted in lower degree plume melts with stronger enriched mantle (EM) signatures than anywhere else along the Samoan and Rarotonga hotspot tracks. The hypothesized relationship between thicker lithosphere and stronger EM signatures is supported by the Cretaceous Samoa-related Magellan Seamounts, which exhibit a robust relationship between older (and therefore thicker) lithosphere and stronger EM signatures. A 43-day seagoing dredging expedition to sample the seamounts along the modeled traces of these two hotspot tracks on top of the OJP will test whether the OJP passed over the Samoan and Rarotonga plumes, and whether this resulted in generation of extreme EM melts. A combination of geochemical analyses and 40Ar/39Ar ages will uniquely identify contributions from these two hotspots. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This award supports international research experiences for U.S. undergraduate students from the University of Texas at El Paso (UTEP) at the University of Victoria (UVic) in Canada in interdisciplinary research representing the fields of bioengineering and neuroscience. The project engages primarily Hispanic engineering students at UTEP, a majority Hispanic 4-year, public institution. Reciprocal visits to UTEP by UVic students will be supported with Canadian funds. UTEP students will be afforded professional development and educational opportunities in cutting-edge research. Specifically, the project engages the synergism of stem cell biology with advanced 3-dimensional biomaterial technology to manufacture tissue from stem cells. Generating patient-specific "artificial" tissue allows the researchers to control the rate of growth and differentiation of stem cells. This research is significant in paving the way for modeling disease and for future drug development. Working together, the interdisciplinary team aims to identify unique approaches of targeting differentiation of human induced pluripotent stem cells [iPSCs] into neural phenotypes by utilizing advanced materials and processing. Specifically, the UTEP team has expertise in human induced pluripotent stem cell (iPSC) culture and 3D bioprinting, and UVic has expertise in culturing neuronal differentiation of human iPSC on fibrin-based scaffolds. The research focus is the application of microfluidic-based 3D bioprinting for coprinting of human iPSCs together with biomaterial scaffolds to generate bio-composites of the desired architecture with high fidelity. Bioprinting iPSCs with advanced biomaterials and scaffolds bears the promise to develop 3D tissues, ideally including encapsulated cells and facilitating their proliferation, and targeted differentiation. The project enables addressing critical questions in the field: 1) How can advanced materials and their unique designs facilitate stem cell culture and promote their differentiation? 2) How can 3D bioprinting be applied as an advanced manufacturing technique to mimic the complex environment for regulating growth and differentiation of stem cells? 3) Does the differentiated progeny of cells cultured atop micro-textured scaffolds exhibit enhanced functionality and phenotype, compared with controls? Thus, the scientific goal is to better understand the role of growth factors and micro-environmental niche and cues released from biomaterial scaffolds in the regulation of adult human iPSC differentiation into neural phenotypes. It is widely known that distractive biomaterial scaffolds that incorporate patterned structures and specific compartments of growth factors can more intricately guide stem cell development. Achieving this goal enables the ability to address fundamental neurobiological questions about neuronal growth, differentiation, which is essential for designing treatments for nervous system disorders or traumatic brain injury. 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

PROJECT SUMMARY Chronic heavy alcohol consumption contributes to high rates of low trauma fractures in middle-aged and elderly populations, especially men. However, low trauma fracture rates in chronic alcohol abusers exceed levels predicted by age, sex, and bone mineral density, suggesting that alcohol negatively impacts bone quality. Without understanding the precise effects of alcohol on bone quality, it will be difficult to appropriately target interventions. Rhesus macaques are a valuable animal model for alcohol studies because of their similar physiology to humans and because they exhibit drinking behavior that mimics the full range observed in humans. We have shown that male rhesus macaques (Macaca mulatta) offered free access to alcohol for 12 months had lower cancellous bone turnover in lumbar vertebra, where a negative turnover balance led to bone loss. The alcohol consuming monkeys also had lower initiation of intracortical bone remodeling (tibia), a process critical for repair of fatigue-induced microdamage. These findings suggest that excessive alcohol consumption could lower cancellous bone mass due to a negative turnover balance and decrease bone quality in cortical bone by suppressing repair of microdamage. These intriguing effects of alcohol in monkeys require verification by comparison to humans. The objective of this R03 application is to assess bone response (quantity and quality) to alcohol in male and female rhesus macaques at a skeletal site (iliac crest) typically biopsied in humans. We propose to test the hypothesis that chronic alcohol consumption will result in dose, bone compartment (cancellous versus cortical), and sex-specific changes in bone architecture and remodeling balance, and that the changes will be similar to those in humans. Specifically, alcohol will reduce cancellous bone mass and lower cortical porosity in both species. The rationale for this project is that the results will increase understanding of the actions of alcohol on the skeleton and will rigorously assess translatability of the nonhuman primate model to humans. To accomplish our objective, we propose two Specific Aims. Specific Aim 1: Assess bone mass, density, microarchitecture, and turnover in iliac crest biopsies from male and female rhesus macaques given free access to alcohol for 7 or 15 months. Specific Aim 2: Assess bone mass, density, and microarchitecture in iliac crest biopsies obtained from sudden death human males and females who chronically abused alcohol for the previous 5 years. The proposed studies are important because 1) they will rigorously test our hypothesis that chronic heavy alcohol consumption alters bone architecture and turnover balance independent of comorbidities, and 2) validate the translatability of the animal model to humans. Additionally, the results have the potential to guide treatment to prevent or reverse the detrimental skeletal effects of alcohol abuse. 1 2/14/2024

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Sexual concerns are among the most common, distressing, and persistent aspects of life after cancer. For at least half of breast and gynecological cancer survivors, cancer treatment adversely affects physical, social, or psychological aspects of sexual health. Examples include vaginal pain, loss of sensation, poor body image, disrupted intimate relationships, and loss of sexual interest. Despite this, sexual concerns are often unaddressed, with one major reason being lack of access to evidence-based interventions. In short, to improve sexual health in cancer survivorship, there is an urgent need to test and implement accessible, evidence-based interventions that can effectively reach cancer survivors in need. Mindfulness-based interventions have demonstrated efficacy in addressing a range of sexual concerns and improving psychosexual health outcomes. Our preliminary research demonstrated support for the acceptability of a virtually delivered mindfulness-based intervention adapted specifically for breast and gynecological cancer survivors experiencing sexual concerns. However, a more rigorous evaluation of this promising intervention, called Mindful After Cancer (MAC), is needed to support wide implementation and dissemination. A pragmatic trial would be ideal because it would facilitate the evaluation of MAC in the context of routine practice, which leads to more generalizable results and shortens the gap from research to implementation outside of the trial setting, thus more quickly increasing access for cancer survivors in need. The Specific Aims of the proposed R34 are 1) To conduct a pilot trial in preparation for a future multisite pragmatic trial of the virtual MAC intervention and 2) To identify contextual factors that may shape equitable reach and future implementation of the intervention. Our feasibility objectives are to develop protocols for 1) recruitment, 2) retention, and 3) training and supervision for adequate intervention fidelity for the planned future trial. Results of the proposed pilot trial will provide a robust scientific foundation and preparation for a rigorously designed multisite pragmatic trial. Evaluation of intervention access barriers and strategies to improve equitable recruitment and reach in both academic- and community-based care settings will inform future implementation and dissemination of mindfulness-based interventions such as MAC. Overall, results of the proposed project will readily translate to the protocol for rigorous, well-designed multisite pragmatic trial to evaluate the effectiveness of the virtual MAC intervention in real world conditions where we expect to implement and disseminate the intervention.

NSF Awards · FY 2024 · 2024-09

The design of new, more active, and selective catalysts is vital for creating cleaner, more sustainable chemical processes with wide-ranging applications, including renewable energy and chemical syntheses. A typical catalytic process includes multiple elementary steps involving many surface-bound intermediates and transition states. The energetics of these intermediates and transition states are crucial, as they determine the rate and selectivity of the catalysts, yet accurate energies are only available for a few key intermediates on metals. The project will use Single Crystal Adsorption Calorimetry (SCAC) to directly measure the heat of adsorption and co-adsorption with solvents on clean single-crystal surfaces in an ultrahigh vacuum. Graduate students and postdocs involved in this project will benefit from the learning and professional environment at PNNL. Microcalorimetry will also be integrated into a mini research project for high school students attending Summer Experience in Science and Engineering for Youth at Oregon State University. SCAC provides the only way to measure the heat of adsorption for irreversible events like dissociative adsorption, which are crucial for producing adsorbed molecular fragments ubiquitous in catalytic mechanisms (e.g., -OH, -CH3, -OCH3, -OOCH) and part of the project. These measurements will also expand to include metal oxides (e.g., FeO/Pt(111). The adsorbate energies gained from these studies will provide fundamental information about the effects of solvents on the binding of reaction intermediates. They will further serve as benchmarks for more accurate theoretical predictions of adsorption energies and effects of solvent on their values, improving predictability and enabling computational catalysis design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The deep sea is an epicenter of biogeochemical cycling that is globally important but poorly understood. Big data generated by emergent gene sequencing technology provides a new avenue to link genes with biological processes. In the deep sea, the vast majority of genes are unknown. This project will focus on methane seep systems. New microbial samples will be collected from methane seeps off the coast of Oregon and Washington. This research will employ a novel natural language processing artificial intelligence approach to predict what these unknown genes do. This will be a critical step toward quantifying oceanic ecosystem function based on genomics. The artificial intelligence models developed using these samples will be broadly applicable. They can provide a foundation to answer many questions across scientific fields ranging from ecology to human health. A tutorial for the models developed will be written and workshop run to explain the techniques. Further, artists will be involved in the research and a documentary will be produced to spread the results of the research. This research will build two new artificial intelligence models to use gene sequence data to understand ecosystem processes, and apply them to methane seep habitats. A new model incorporating genes and ribosomal amplicon co-occurrence will code genes and classify them into pathways. In parallel, generative models with text and sequence protein representation will be developed. Models will identify putative genes responsible for each of the cycles identified, or dl-genes. These two models will be applied to new samples collected from methane seeps offshore Oregon and Washington. Methane seep habitats are areas where methane is consumed by microbial activity and are also areas with strong redox gradients leading to diverse methane and nitrogen over a small spatial area. Both artificial intelligence models will be applied to these habitats, and the results used to empirically validate the dl-genes by testing if the dl-genes are transcribed when the associated geochemical process is observed. The main outcome will be a scalable approach with artificial intelligence that will advance key questions in earth system science. To broaden the use of the methods developed in this project to solve similar problems, a tutorial and workshop will help others learn and use the models. Further, the results of this work will include exhibits by artists involved in the research as well as producing a documentary about how artificial intelligence can harness big data to help advance the understanding of earth systems. 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

A major goal of Earth science research is to understand interactions among tectonic and surface processes that control topographic evolution in regions of active mountain building, particularly in zones of terrane-continent collision at convergent plate boundaries. Early Eocene sedimentary rocks in SW Oregon offer an ideal opportunity to test models for the surface response to collision of the oceanic Siletz terrane with western North America, and regional post-collision adjustments, that affected the PNW region between ~ 55 and 40 million years ago. The events associated with collision of Siletzia are central to understanding the origins of the Coast Ranges in western Oregon and Washington, as well as the regional crustal structure that governs processes active in the modern Cascadia subduction zone. This research applies a novel combination of field and analytical techniques such as isotopic dating of sand grains that are well suited for testing hypotheses in this setting, and the results will be useful for understanding this and other terrane-continent collisional margins around the world. The study provides a unique opportunity for undergraduate students to conduct place-based research by investigating the deep-time geologic history of Oregon. Student researchers will be recruited from Lane Community College and University of Oregon (UO) to work under the mentorship of PIs and graduate students. This project benefits local communities through cooperation and data sharing among the UO, Oregon State University, and the Oregon Department of Geology and Mineral Industries (DOGAMI). The study also contributes to the broader impacts of an ongoing NSF-RUI funded study of bedrock evolution in the Klamath Mountains through cross-project fieldwork, conference presentations, and student-focused workshops. Eocene sedimentary rocks in southwest Oregon preserve an unparalleled archive of the surface response to collisional mountain building and post-collision tectonic reorganization. The syn-collision Umpqua Group (~ 54–49 Ma) rests on basalts of the Siletz terrane and filled a syn-orogenic basin on the north margin of the Klamath Mountains orogen. The post-collision Tyee Group (~ 47–45 Ma) is a thick succession of fluvial, deltaic, and marine turbidite deposits that record rapid progradation of an integrated fluvial-delta-shelf-slope-basin clinoform system during initiation of the modern Cascadia subduction zone. Hypothesis 1 postulates that the Tyee paleoriver originated in western Idaho, traversed a large low-gradient continent-interior drainage, and flowed through the former collisional orogen to a prograding fluvial-deltaic to offshore marine turbidite system. In Hypothesis 2, the Tyee paleoriver was sourced in the Klamath Mountains, and changes in sand composition record bedrock exhumation, recycling of older sediments, and/or catchment growth. Because paleocurrent data show unequivocally that the Tyee paleoriver flowed directly out of – not around – the Klamath Mountains, evidence of a large continent-interior catchment for the Tyee paleoriver would imply major post-collision reorganization of the drainage system by headward erosion and stream capture and/or extensional collapse of the former collisional orogen. These hypotheses will be tested through integration of modern provenance tools, detrital thermochronology, and detailed field observations. Methods include U-Pb dating and Lu-Hf analyses of detrital zircon, 40Ar/39Ar dating of detrital micas, basin subsidence analysis, and detrital thermochronology, all tied to detailed geologic mapping and stratigraphic analysis. The results will generate new insights into the regional surface response to collision-related mountain building and post-collision reorganization associated with accretion of Siletzia to 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.

NIH Research Projects · FY 2024 · 2024-09

ABSTRACT Climate change is the largest global health challenge of the 21st century. Although direct and indirect effects of climate change threaten health worldwide, the impacts will be particularly severe in low and middle-income countries (LMICs). There is an alarming absence of research on climate-driven exposures and human health in LMICs, and how to adapt and build resilience to these climate hazards. We will address these research gaps by leveraging the infrastructure and ongoing data collection in the Prospective Urban and Rural Epidemiology (PURE) study, a prospective cohort of 200,000 adults across 998 urban/rural communities in 27 low, middle, and high-income countries. We will leverage this existing data and infrastructure to establish PURE-Climate, a global study of climate change and human health. Aim 1 will determine the direct impacts of acute climate- driven exposures (e.g. extreme temperatures, floods, storms) on diverse health outcomes (e.g. hospitalizations, CVD incidence, mortality) and determine vulnerability and resilience factors. We will quantify associations between acute climate-driven hazards and diverse health outcomes using a case-crossover design and over a decade of prospective data collected for PURE individuals. Acute hazards will be assessed using state-of-the-art geospatial methods for each PURE community. Aim 2 will evaluate the indirect impacts of chronic climate-driven exposures on mortality and NCDs, determine pathways of influence, and assess vulnerability and resilience factors. We will apply long-term meteorological data and global climate re-analysis datasets to examine the indirect impacts of climate exposures on mortality and NCD incidence using prospective data collected in PURE. To further understand indirect pathways, adaptation, and vulnerably and resilience factors we will implement a climate-health survey to individuals living in PURE communities experiencing diverse climate-driven changes. Aim 3 will identify community-level opportunities and barriers for increasing climate resilience by conducting focus groups with 12 PURE communities and develop a Building Resilience Against Climate-Disaster Effects (BRADE) framework for low-resource settings. The PURE-Climate grant will provide quantitative and qualitative information on how climate change influences human health, especially in LMICs, and demonstrate how these impacts can be reduced through adaptation and local climate resilience. Finally, the PURE-Climate study will build capacity for future climate change and health research within LMICs.

NSF Awards · FY 2024 · 2024-09

This project proposes the study of novel data visualization techniques based on tensor fields, which are a mathematical approach to represent relationships between different data in a model. Tensor fields have a wide range of applications in science, engineering, and medicine. For instance, enabling the three dimensional visualization of the wind speed of tornadoes. Despite these applications, traditional visualization of tensor fields is limited to static conditions; that is, the visualization of data at a fixed point in time. Thus, the visualization of the wind in a tornado would be at a fixed point in time not over the course of tornado. In contrast, this team will study the visualization based on tensor fields as the model is changing over time. For this purpose, the project proposes creating mathematical models of two dimensions for visualization of a dynamic tensor field. The team will also investigate efficient techniques to enable domain scientists, engineers, and data stakeholders to gain critical insight into their data and the underlying physics of the movement. Such insight can be beneficial to tasks such as natural disaster modeling and management, structural stability in the nation’s critical infrastructure, and medicine. The developed visualization can also be used in classroom teaching of advanced mathematical concepts to undergraduate and graduate students in both computer science and other fields. The core activities of the research include the investigation of the following: (1) the set of all atomic bifurcations in 3D time-varying symmetric tensor fields; (2) a holistic view of all the structures in the tensor fields in the form of a bifurcation graph; (3) robust and efficient algorithms to extract bifurcations from such tensor fields; and, (4) a fast and effective visualization of both the tensor fields and their bifurcation graphs. The research leverages on existing research in topology-driven scalar and vector field visualization as well as two-dimensional tensor field analysis. Moreover, modern mathematical machinery such as abstract algebra, differential geometry, and algebraic topology is used in the enumeration of all atomic bifurcations in the fields and their robust and efficient extraction. The research can help not only push the envelope of tensor field visualization in a significant way but also benefit related visualization topics such as scalar and vector field visualization. The set of atomic bifurcations in tensor fields can serve as a dictionary to physicists and engineers who can benefit from fundamental understanding of Physics using tensor fields. 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

The carbon cycle plays a critical role in regulating the earth’s climate. Carbon in different forms can be found everywhere on the planet and there is a large and dynamic pool of carbon in the ocean. The fate of carbon in the ocean influences other parts of the global carbon cycle such as the atmosphere and is regulated by a range of biological and physical processes. This study examines the role of marine microorganisms in producing and consuming highly abundant small carbon-containing molecules called osmolytes. Many of these molecules have never been measured in the ocean or have only been measured in limited instances. Microorganisms may change how they cycle and store osmolytes in response to environmental fluctuations in temperature and nutrient availability. This study will culture marine microorganisms under nutrient-limiting conditions and measure osmolytes across a gradient of nutrient limitation in the ocean. This new information will show how environmental conditions impact the abundances of these molecules and the rates at which they are cycled. These challenging measurements will contribute to constraining one aspect of the carbon cycle and will enable better predictions of how cycling of these molecules will change under future climate conditions. The project will be led by three early career female scientists and will provide training opportunities for several undergraduate students, a PhD student and a postdoc. In addition, members of the research team will provide training on mass spectrometry data analysis at an annual summer school in Africa. In a changing ocean it is critical to quantify labile carbon flux and predict its role in future climates. Each year, half of all marine primary production (25 Pg C) is remineralized by microorganisms. Organic osmolytes, which regulate osmotic pressure and thus may accumulate at millimolar concentrations inside cells, likely comprise a significant, but unconstrained, component of this carbon flux. Given their high intracellular concentrations and small size, these labile compounds are valuable carbon substrates as well as sources of nitrogen and sulfur. This study will produce mechanistic insights into this component of the labile carbon flux by direct quantification of the cycling of organic osmolytes with implications for nitrogen and sulfur cycling as well. Objective 1 will identify differential production of osmolytes by different taxa responding to nitrogen stress by coupling osmolyte measurements and transcriptomics. Objective 2 will measure rates of osmolyte assimilation and the fate of different osmolytes within metabolism. Objective 3 will quantify osmolyte cycling across a coastal to oligotrophic transect in the Mid-Atlantic that encompasses a gradient of natural communities and nitrogen availability. Standing stocks, assimilation rates, and metabolic fate of osmolytes in a natural community will be measured using mass spectrometry and linked to microbial activity via metatranscriptomics. The resulting budget of osmolyte cycling will constrain the contributions of osmolytes to the labile dissolved organic matter flux and inform future efforts to quantify microbial rates of individual labile molecule production and uptake. This project will provide student and postdoc training opportunities in advanced laboratory and analytical skills. As part of a broader effort to build capacity in oceanography globally, mass spectrometry data generated for this project will be used as a training tool at the Coastal Ocean Environment Summer School in Nigeria and Ghana. This project is funded by the Chemical Oceanography and Biological Oceanography Programs in the Division of Ocean Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The ability to modify crop genetics is critical to agricultural systems, and a foundation of the high productivity of agriculture that supports both developing and advanced economies. Modern biotechnologies, based on the ability to insert and precisely modify genes, are a new wave of innovation that is beginning to deliver major advancements to traits such as stress resistance and product quality. However, for numerous crops, including most woody plants, the ability to carry out modern biotechnology is limited by a very low rate of gene insertion and regeneration of modified plant tissues. The goal of this research is to study natural variation in the capacity of the natural plant genetic engineer Agrobacterium to insert genes and promote the regeneration of modified plant tissues—a process which is called “transformation.” The investigators will scan approximately 100 novel Agrobacterium strains for their capacity to transform the economically important and scientific “lab-rat” woody species poplar, study the reasons for variation among strains and poplar genotypes in efficiency, analyze the Agrobacterium genes responsible for high transformation capacity, and using the best strains and genes engineer new and more powerful strains. In addition, the investigators will produce a variety of video products that seek to advance critical thinking about the value of modern biotechnologies for helping agriculture to cope with climate emergency. Students will lead on innovation in video production, guided by biological science and social science faculty and videographers. These will also be used to create curricula for use by middle and high school teachers. Genetic transformation and regeneration continue to be major bottlenecks for functional genomics and biotechnology in the majority of crop species. Using Populus as a model woody plant system, we propose to identify new Agrobacterium strains, and new genes encoded in their T-DNAs, that can improve the success rate of transformation. The investigators will study the extensive natural variation among approximately 100 sequenced Agrobacterium strains, with a focus on strains and methods capable of inducing transgenic shoots. For selected high performing strains they will also analyze the mechanisms by which strains interact with specific plant genotypes and study the roles of specific genes on their T-DNAs. They will also use these genes to improve domesticated strains and domesticate high performing wild strains that can impart high rates of transformation and regeneration. Broader impacts activities will seek to improve literacy and critical thinking about crop biotechnologies by K-12 students and the public. The investigators will engage undergraduate and graduate students in the production of a series of short videos designed to stimulate thinking and discussion of these topics, sharing them widely and tracking views and the extent of “virality” on social media platforms. In addition, with input from science teachers and students the investigators will create education-oriented videos and linked teacher guides to promote video use in high school classrooms of underserved student populations. 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

Agriculture is a critical social-ecological system producing much of the world’s food, supporting millions of livelihoods, and interacting with local ecosystems. Recent documents by the United Nations, World Bank, and Intergovernmental Panel on Climate Change highlight both the vulnerability of our agricultural system to climate change as well as its ability to mitigate and adapt to climate change. In 2022, the U.S.’s Inflation Reduction Act dedicated $19.5 billion to support farmers in “climate-smart” practices that sequester carbon into agricultural soils and increase soil water holding capacity and infiltration, thereby contributing to both climate change mitigation and adaptation. As the U.S. and other governments invest in supporting farmers’ ability to respond to climate change, it becomes necessary to assess (1) the regional variation of climate risks for farmers in the U.S.; (2) the quantifiable impacts “climate-smart” practices have on soil composition, farmer livelihoods, and climate resilience at multiple scales, and (3) the equitability of resources and outcomes for “climate-smart” agriculture across the country. This study will provide an integrated analysis across the ecological and social dimensions of climate adaptive agricultural practices to support more effective, responsive, and equitable applications of climate-adaptive agriculture in the U.S., while improving scientific knowledge of the role agriculture plays in climate-change mitigation and adaptation. Agricultural systems are priority human-environment systems for climate change intervention. Recent international documents, such as the UNFCCC Conference of the Parties (COP-27), the IPCC’s Sixth Assessment Report, and the World Bank’s Climate Change Action Plan 2021-2025.demonstrate a growing recognition that agriculture is a complex human-environment system, which both contributes to and is highly vulnerable to global climate change. Agricultural policies and incentives based on the priorities set forth in these documents, support practices like low tilling, cover cropping, and residue management, which have been deemed "climate smart" due to their ability to sequester carbon into agricultural soils and increase water retention and infiltration. However, there is still minimal knowledge of the quantifiable impacts these practices have on soil composition and climate resilience at multiple scales, and the complexity of factors that influence decisions by farmers to adopt these practices, even when presented with financial incentives. Through the application of mixed methods including Earth-system modeling, soil testing, farmer surveys, interviews, and focus groups, this project will (1) identify regionally specific climate risks for farmers in three agricultural regions of the U.S., (2) quantify, the impacts of climate-adaptive farming practices on soil composition, climate, and crop yield at multiple scales, and (3) assess the role of these climate-adaptive practices in producing overall climate resilience. This study will illuminate the equitability of climate-resilience outcomes and build new capabilities within the Community Earth System Model (CESM) to represent climate adaptive processes under global-change scenarios. 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

Water plays a crucial role in supporting life on Earth, not only in rivers and lakes, but also within the soil and atmosphere. The way water moves and is stored can affect everything from plant growth to water quality. When it rains, water doesn’t just sit on top of the land; it gets absorbed into the soil to support plants and animals, seeps down to become groundwater, evaporates back into the air, or runs off into streams and oceans. The way water is distributed on the planet is changing due to human activities and global warming, yet it is not fully understood how such changes impact water stored in the ground, plant growth, or how water availability regulates where and how much carbon is stored belowground. To solve this puzzle, this project will measure the movement and amount of water and carbon in a Kansas grassland where the climate is getting wetter, and the land cover is changing where shrubs are encroaching and replacing grasses. The project will train undergraduate and graduate students, as well as a postdoctoral researcher, to collect water, soil, and plant data and bring these data together to understand how the water and carbon cycle are changing. This project will use the Konza Prairie (Kansas, USA) and its long-term research platform of watershed-scale manipulations (e.g., fire and grazing) in a mesic grassland to answer the question: How does concurrent climate and land cover disturbance alter below-ground water and carbon cycle processes, and their interactions? Using an interdisciplinary approach that incorporates new data collection, data harvesting, and numerical modeling the project team will: 1) quantify the degree to which woody encroachment alters soil macropore abundance, preferential flow occurrence, and vertical water fluxes; 2) measure the impact of woody encroachment on groundwater residence times and the fractional sources of water (e.g., interflow, shallow groundwater, deep groundwater) that support stream flow; 3) quantify changes in carbon subsurface processes (e.g., respiration rates, decomposition, weathering) and fluxes; and 4) model the impact of synchronous changes in climate (i.e., precipitation and evapotranspiration) on the capacity for carbon sequestration as woody encroachment progresses in grasslands. By addressing the overarching question in grasslands with intermittent streams underlain by carbonate bedrock, the research will provide transferable knowledge on the ecohydrologic function in systems particularly vulnerable to climate and land cover disturbance. This project is co-funded by the Division of Earth Sciences Hydrologic Sciences program and the Division of Environmental Biology Ecosystem Science 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 2024 · 2024-09

Autonomous robots hold the potential to revolutionize society in areas such as healthcare, transportation, and manufacturing. These systems frequently employ learning-enabled components in their perception, planning, and control modules, necessitating complex design choices to ensure safe operation. However, design decisions that initially appear sound may lead to unexpected problems during testing or, even worse, post-deployment. For example, an autonomous vehicle once exhibited erratic swerving to localize itself for lane-keeping, a failure mode unforeseen by the system's designers and developers. Such surprises indicate that the agent's norms—what it considers permissible and obligatory—are inappropriate in certain situations. As learning-enabled systems become more complex, operate in open environments, and interact with humans and other robots, these challenges are likely to be exacerbated. This project focuses on safety failures of reinforcement learning (RL) agents, stemming from two primary sources: the misalignment between design intent and the agent’s perceived norms, and the gap between the agent’s required knowledge for safe operation and its actual perception capabilities. The goal is to equip researchers and practitioners with tools to design provably safe autonomous systems, encompassing all major stages of design, verification, and deployment. The project develops a process to iteratively align an RL agent's norms with those of its designers and formally verify the resulting behavior. Key activities include: (1) developing inverse reinforcement learning algorithms to learn a reward function from demonstrations, constrained by deontic logic; (2) systematically exploring the trained agent’s norms to uncover unknowns by generating norms that would surprise the engineer; (3) querying the agent to explain its reward function when it produces undesired behavior; (4) defining a new class of obligations related to knowledge and corresponding formal specification logic; (5) designing run-time monitors to predict action and knowledge safety violations during operation; (6) implementing online metareasoning, coupled with introspective perception modules, to restore safe behavior; (7) iteratively improving system alignment by updating the agent's learning process using verification and run-time monitoring results. The project's outcomes are validated using an industrial simulator of a real-world bipedal robot, scaled-down autonomous race cars, and a campus-wide fleet of delivery robots. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to computationally design, then fabricate and test, an electronic nose for classifying complex gas mixtures. Broadly, the gas sensing technology developed in this project could enable the design and fabrication of electronic noses for indoor and outdoor air quality monitoring, crop monitoring in agriculture, food quality assessment, and health assessment via breath composition analysis. Specifically, the hardware of the electronic nose will constitute an array of porous materials, metal-organic polyhedra (MOPs), coated as thin films on microbalances. Each sensor in the array functions by virtue of gas adsorbing into the film of material, which is registered by the microbalance. The software of our electronic nose will constitute a machine learning algorithm that parses the response pattern of the sensor array and makes a prediction about the gas composition that produced the response. A key advantage of MOPs is their tunability: the pore size and shape and surface chemistry of the MOPs can be tuned to arrive at a highly diverse set of MOPs that interact differently with each species in the gas phase. As a consequence, the response pattern of the MOP-based sensor array will provide a lot of information about the composition of the gas. While first tested for its ability to discriminate between pure analytes, the electronic nose in this project will be tailored and tested for classification of plant oils and, as a lofty goal, grades of olive oil, to counter fraud. For outreach, the project includes development of YouTube videos to educate the public about MOPs and gas sensors and hands-on learning modules with a gas sensor and an Arduino microcontroller. This project will design, fabricate, and test an electronic nose, consisting of (1) a gas sensor array employing diverse metal-organic polyhedra (MOPs) as gravimetric sensing elements paired with (2) a supervised machine learning model, to discriminate complex mixtures (eg. plant oils). As nanoporous, stable, recyclable, and tunable materials, MOPs may serve as sensitive and selective sensing elements for the next generation of gas sensors. Coating a thin film of MOP on a quartz crystal microbalance (QCM) gives a gravimetric sensor whose response is the mass of gas adsorbed in the MOP film. Mimicking olfactory systems in living organisms, the response pattern of an array of multiple QCM-MOPs—chemically and structurally diverse MOPs—will be interpreted by a supervised machine learning model to discriminate complex mixtures. The computational design of the QCM-MOP sensor array constitutes: (i) construct a database of candidate MOP structural models, (ii) conduct molecular simulations of adsorption of a portfolio of volatile organic compounds in each MOP, (iii) employ dimension reduction algorithms to embed the MOPs into a latent space wherein MOPs with similar adsorption properties congregate, then (iv) use a diversity selection algorithm to curate the most diverse set of MOPs for the array. Next, the investigators will synthesize and characterize the computationally-curated MOPs and employ surface deposition techniques to attach them to QCMs. To test the efficacy of different strategies to inject diversity into MOPs, the investigators will construct three generations of QCM-MOP arrays, wherein the MOPs differ by: (1) metal only, (2) functional group only, and (3) topology, metal, bridging ligand, and functional group. The reversibility, cyclability, and stability of the MOPs will be tested. Finally, the QCM-MOP sensor array will be tested for discrimination of (1) pure compounds, (2) plant oils, and (3) grades of olive oil. Dimension reduction algorithms will aid in exploring the discriminatory capability of each QCM-MOP array, then supervised machine learning algorithms will map its response pattern to a predicted compound/mixture. The design of the 3rd-generation QCM-MOP array will be evolved by replacing the least-informative MOP, flagged by interpreting the machine learning model, with the next MOP in the computational design queue. Finally, to make for a robust QCM-MOP array, the temperature and humidity will be varied for a context-sensitive classifier. 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

The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in their efforts to significantly increase the numbers of students matriculating into and successfully completing high quality degree programs in science, technology, engineering and mathematics (STEM) disciplines to diversify the STEM workforce. Particular emphasis is placed on transforming undergraduate STEM education through innovative, evidence-based recruitment and retention strategies, and relevant educational experiences in support of populations underrepresented in STEM disciplines: African Americans, Hispanic Americans, American Indians, Alaska Natives, Native Hawaiians, and Native Pacific Islanders. These strategies facilitate the production of well-prepared students highly qualified and motivated to pursue graduate education or careers in STEM. For the United States to remain globally competitive, it is vital that it taps into the talent of all its citizens and provides exceptional educational preparedness in STEM areas that underpin the knowledge-based economy. The Pacific Northwest LSAMP STEM Pathways & Research Alliance has formed an alliance in response to the need for a more diverse and skilled technical workforce. That need still exists and is particularly acute in the Pacific Northwest (PNW). The institutions that make up the Alliance are diverse and include Oregon State University (the lead institution), Boise State University, Portland State University, the University of Washington, Washington State University, the College of Western Idaho, Linn-Benton Community College, Seattle Central College, Yakima Valley College, and Portland Community College. Through this project, the Alliance will implement activities to increase both the number of underrepresented minority students graduating with STEM degrees, and the percentage of student participants who are prepared to pursue a STEM career pathway in industry or graduate school. In addition, the Alliance will expand its focus on STEM career pathway planning. This increased focus on STEM career pathways will include professional development, faculty mentorship, opportunities to participate in project operations and peer mentoring to other STEM students. PNW LSAMP will also conduct a multi-year mixed methods research study to examine the landscape of STEM-focused diversity, equity, and inclusion efforts at universities that are part of the Alliance. The research will contribute to a deeper knowledge of the mechanisms that drive successful diversity, equity, and inclusion initiatives, including how funding allocation and institutional support play crucial roles in shaping and sustaining these efforts. The PNW LSAMP team is also committed to disseminating lessons learned through practice and research to audiences within the region and nationwide, supporting the National Science Foundation's strategic goal to "empower STEM talent to fully participate in science and engineering" by improving conditions for minoritized students in STEM and helping build a diverse and excellent 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 2024 · 2024-09

The upper ocean boundary layer (OSBL) controls the vertical transfer of heat, mass, and momentum from the atmosphere to ocean interior, affecting water properties and influencing climate. Turbulent OSBL flows, unresolved by ocean circulation models, must be parameterized. Over recent decades, significant developments in OSBL parameterization schemes (i.e., boundary layer models), have occurred primarily through comparisons with Large Eddy Simulations (LES). Nevertheless, multiple competing schemes with different physical assumptions are still in common use. Recent systematic comparison of predictions finds differences averaging 15% in mixed layer depth, larger under stabilizing, wind-wave forced conditions. It is unclear which of these schemes is best since LES may not represent the ocean accurately and comparisons with oceanic data are limited. This project will try correct both of these deficiencies by developing a new model for the OSBL which includes both local and non-local transports and is tuned to both observational and LES results. A database of OSBL turbulence properties from existing Lagrangian float data will also be compiled both to tune and validate this model and to serve as a reference for other future efforts. This development has potential to significantly improve physical and biogeochemical model predictions on a wide range of space and time scales directly through our modelling efforts, and by providing a database of unique and directly relevant measurements and analysis of the turbulence dynamics and covariances responsible for upper ocean vertical fluxes to the model development community. The project will support the multi-institutional collaboration of an early-career PI, a graduate student at UW, and the individual outreach efforts of the PIs in local public schools. Parameterization of OSBL has been hampered by two current obstacles. First, recent measurements and LES comparisons suggest there are significant fundamental deficiencies in the physical assumptions of many schemes. Specifically, observations suggest that (1) there are strong deviations from standard surface layer “Monin- Obukhov” similarity scaling due to surface wave impacts; (2) overturning scales near the entrainment zone of mixed layers are much smaller than can be accurately represented in LES; and (3) vertical transport of turbulence has substantial non-local components not well represented by a diffusive cascade across discrete OSBL depths. Second, recent decades of detailed OSBL observations have had little impact on model formulation, presumably because the data and the dynamic scaling behavior it supports have not been presented to the modeling community in useful ways. This project has two main task to address both of these pressing issues. Task 1: Develop a new model for the OSBL which includes both local and non-local transports and is tuned to both observational and LES results. This will build on an existing local second moment closure (SMC; from APL/UW) that includes surface wave effects, and a newly developed non-local, plume-based, assumed distribution higher order closure (ADC; from OSU and collaborators) that vertically exchanges turbulence properties (e.g. Reynolds covariances) across the boundary layer, evolving in reference to production length scales. The investigtors will develop a nonlocal SMC comparable in computational expense to traditional two-equation ‘single-point’ local SMCs by combining a bottom-up approach of adding nonlocal closures based on Lagrangian float measurements to an SMC’s Algebraic Reynolds Stress Model (ARSM), and a top-down approach reducing the large number of dynamic equations for Reynolds covariances in the higher order ADC model to nonlocal linearized ARSM closure expressions. Task 2: Develop a database of OSBL turbulence properties based on ~25 years of Lagrangian float data to tune and validate this model and provide reference ground truth to guide development and validation of other OSBL mixing schemes. A recent APL/UW Ph.D. computed profiles of vertical velocity variance and skewness for both wind/wave forced and convective cases. This project will expand this to include dissipation rate by an inertial subrange method, turbulence length scales computed directly from Lagrangian trajectories and indirectly from dissipation and kinetic energy. Air-sea fluxes and mean profiles of velocity and density will be available for all cases. The turbulent quantities will be scaled on the forcing, further complemented by dimensional scalings generated from Task 1. This database will be distributed within the framework of the widely used community-driven General Ocean Turbulence Model (GOTM) effort. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This three-year REU Site: Engineering for Bouncing Back Better—Integrating Resiliency and Engineering Disciplines Through Systems Thinking is hosted by Oregon State University. Natural and anthropogenic stresses and shocks, such as the devastating wildfires in California, the heightened frequency and intensity of hurricanes, exemplified by Hurricanes Harvey and Irma, severe drought conditions in Western states, the protracted water contamination crisis in Flint, Michigan, and economic adversities experienced in various urban centers (notably Detroit, Michigan) have triggered communities across the U.S. to consider resiliency in their preparedness and planning activities. There is a critical need for research and education that advances a systems view of resilience – specifically, one that incorporates response, recovery, and mitigation via a multitude of solutions that span infrastructure systems, social institutions, emerging technologies, and stakeholder engagement and capacity-building in communities. This REU Site integrates researchers and ten undergraduate students each year from Chemical, Biological, and Environmental Engineering, and Civil and Construction Engineering on the topic of resilience. The project offers exciting interdisciplinary research opportunities to undergraduates and professional development including graduate school and career explorations. Technical research projects coupled with a suite of REU activities will be offered to advance the science on interdisciplinary methods, tools, and technologies used toward improving the (a) resiliency of infrastructure systems, (b) resiliency of ecosystems and natural resources, (c) capacity for community resilience, and (d) capability for risk and resilience assessment in communities. Participants will employ Systems Thinking skills to solve complex resiliency research problems and increase their motivation to pursue interdisciplinary graduate research. Each undergraduate student will be part of a research team comprised of a primary faculty mentor, faculty collaborators (from multiple socio-technical disciplines), and graduate students. Cohort-building activities include field trips to communities facing unique resiliency challenges, and a movie night focused on community resilience stories. Professional development will be comprised of skill-building workshops and panels, a 10-page project report, and the final REU symposium. 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

PROJECT SUMMARY (See instructions): To optimize drug dosing, clinicians may order laboratory testing of drug levels in patient blood samples, but the clinical benefit of such testing is limited because testing is slow and infrequent. While field-use measurement systems are a partial solution, a major problem is that a person's drug pharmacokinetics (PK) can vary over time and from person to person. Through innovative use of new tools from Al and data science, we propose to develop a personalized therapeutic drug monitoring (pTDM) system incorporating (1) frequent noninvasive field measurements of drug levels and (2) a platform that delivers in real-time a profile of the circulating drug levels and patient-reported clinical information. We will assess the system's accuracy in the context of measuring salivary levels of anti-seizure medications for people with epilepsy. In our four research aims, we will develop a personalized drug titration PK model; electrochemistry assays for two anti-seizure drugs in saliva; electronic and electrochemical components and computational tools needed for a field-use platform; and an integrated and validated field-use measurement platform. In our two broader impacts aims, we will train students in transdisciplinary science and engage with caregivers and patients to obtain feedback and ensure equitable benefits from pTDM. Successful completion of our project's research aims will address critical knowledge gaps in how to (i) account for varying pharmacokinetics in saliva-based personalized therapeutic drug monitoring; (ii) address the challenges of electrochemical drug measurement in saliva given sample-to-sample variation in background matrix; and (iii) address the challenges of integrated field-use pTDM system development with low-power microelectronics. Further, the integrated system that we develop will demonstrate the benefits of using machine-learning to overcome the challenges of variable PK and saliva background matrix in the context of personalized therapeutic drug monitoring for chronic conditions and would be transformative for the field. As such, our project is in excellent alignment with the NIBIB's mission to transform, through garnering knowledge and technology development, disease treatment.

NSF Awards · FY 2024 · 2024-08

The NSF CAREER program seeks to nurture PIs with innovative programs in teaching and research who are leaders in their field. The Division of Integrative Organismal Systems (IOS) CAREER awardee conference will provide its CAREER awardees with continued professional development support as they seek to strengthen their existing programs and navigate new challenges and opportunities in mid-career. This two-day conference, to be held near NSF Headquarters at the Westin Old Town Alexandria, Virginia on November 21-22, 2024, will create formal and informal opportunities for interaction among two cohorts of current IOS CAREER awardees. This meeting will create opportunities for participants to share successes and challenges of ongoing CAREER projects and to stimulate discussion of new ideas for research and education, including synergistic research across IOS core programs. The overall goal of the conference is to foster the exchange of ideas. Specific goals include: (1) developing opportunities for collaboration, peer support, and learning among CAREER awardees; (2) discussing new strategies for building and managing a portfolio of NSF projects; (3) increasing awareness of NSF programs and new initiatives; (4) sharing new tools and techniques for research and teaching; and (5) discussing research priorities and nucleating new, cross- disciplinary collaborations. Through these activities, IOS CAREER awardees will benefit from the guidance provided by their peers that will help them maintain and strengthen their research and teaching programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Large volcanic eruptions are rare but when they occur, they can devastate areas close to the volcano and have severe impacts on areas hundreds of miles away. But large eruptions do not occur without warning: they are typically preceded by earthquakes, gas emissions and smaller eruptions. Under these circumstances, it can be difficult to determine when, and how large, the main eruption will be. A good example of this behavior is the eruption that formed Crater Lake, Oregon, about 7700 years ago. Here explosive eruptions occurred over the decades before the big eruption, with several eruptions during the months leading up to the final event. The research team will study deposits from these eruptions to determine the changes in the system that produced the catastrophic event. This work will improve our ability to forecast volcanic activity. This project will not only train two PhD students but will also (1) engage students at a local community college in running a hazard assessment exercise simulating the buildup to the eruption, and (2) engage professionals in related fields to examine the long-term (decades to centuries) impact of the eruption on the vegetation, rivers and human occupants of the Pacific Northwest. This proposal to conduct a multidisciplinary re-examination of the precursory activity an archetype M7 eruption, Mount Mazama, Oregon will track, in P-T-X-t space, the products of explosive and effusive activity that both preceded (by ≤ ~200 years) and include the climactic Mazama eruption that formed Crater Lake, OR. The team's goal is to identify changes within the magma storage region that led to reservoir evacuation and caldera collapse, and, by doing so, to define key observables in eruptive products that could provide early warning of a major explosive eruption. Detailed studies of precursor eruptions are limited, because these smaller eruptions are over-shadowed by the climactic event and early deposits are often covered and/or destroyed by later activity. In this respect, deposits from precursor Mazama eruptions may be unique, both in their preservation and in the number and complexity of the precursor eruptive sequences that they record. The proposed approach is comprehensive in linking petrology to physical volcanology through detailed analysis of ash, pumice and lava samples. Ash samples, in particular, may be enriched in components that are not well-represented in larger clast sizes typically used for petrologic studies and that record conduit processes controlling eruptive transitions, including onset of a paroxysmal phase. The approach is innovative in using deposits from precursor eruptions to track changes in both the chemistry and physical properties of the larger magmatic system in space and time. The approach is unusual in combining textural analysis and diffusion chronometry to monitor P-T changes within eruptive sequences and link them to larger-scale processes operating within an evolving magmatic system. This project will not only train two PhD students but will also (1) engage students at a local community college in running a hazard assessment exercise simulating the buildup to the eruption, and (2) engage professionals in related fields to examine the long-term (decades to centuries) impact of the eruption on the vegetation, rivers and human occupants of the Pacific Northwest. 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.