Colorado State University

NIH Research Projects · FY 2024 · 2024-09

Project Summary Outdoor fine particulate air pollution (PM2.5, particles with aerodynamic diameter < 2.5 𝜇m) is a leading cause of global morbidity and mortality, contributing to millions of premature deaths each year. Climate change will worsen the burden of disease from PM2.5 in the coming decades, with severe effects felt by those of low socioeconomic status and from underrepresented racial and ethnic groups. Little is known about the extent to which different US populations may experience “different kinds of particles” with respect to PM2.5 composition and overall toxicity. Recent research suggests that the combined transition metal and sulfur content of PM2.5 may influence the respiratory and cardiovascular health risks that result from acute and chronic exposure. Oxidative stress is an important mechanism linked to the cardiorespiratory health effects of PM2.5 and several recent studies have incorporated measures of PM2.5 oxidative potential (a measure of the ability of particles to promote oxidative stress in cells and tissues) as a complementary metric to PM2.5 mass. Importantly, several of these studies have noted stronger associations between outdoor PM2.5 mass concentrations and both acute and chronic health outcomes when the PM2.5 oxidative potential was elevated. We hypothesize that particle acidity increases the bioavailability of PM metals, allowing them to participate in redox reactions that contribute to oxidative stress and potential adverse cardiovascular and respiratory health outcomes. The objective of this research is to determine how spatial and temporal variations in PM2.5 composition and oxidative potential may modify the strength of associations between PM2.5 mass concentrations and cardiorespiratory morbidity/mortality. We will deploy a low-cost measurement network to quantify PM2.5 oxidative potential on a national scale. Collected samples will be analyzed for trace elements and oxidative potential (Aim 1). With these data we will conduct a national-scale time-stratified case-crossover study of daily variations in outdoor PM2.5 mass concentrations and acute cardiorespiratory morbidity among Medicare enrollees (Aim 2). Specifically, this analysis will evaluate how monthly variations in PM2.5 components and oxidative potential across the US modify the strength of associations between day-to-day changes PM2.5 mass concentrations and acute health outcomes (acute myocardial infarction, ischemic heart disease, congestive heart failure, chronic obstructive pulmonary disease, and asthma) among potentially sensitive subsets of the US population. Finally, we will conduct a cohort study in the Medicare cohort (Aim 3) to evaluate how spatial variations in annual average estimates of PM2.5 components and oxidative potential across the US modify the strength of associations between yearly changes PM2.5 mass concentrations and chronic health outcomes among potentially sensitive subsets of the US population. We will also estimate the shapes of concentration- response relationships within strata defined by different PM2.5 composition and oxidative potential.

NSF Awards · FY 2024 · 2024-09

Brain-computer interface (BCI) research explores avenues of controlling devices directly from brain signals. Thus, BCI technology is a powerful control option for neuro-prosthetic limbs, as well as a potential communication option for people with severe motor disabilities or disorders such as amyotrophic lateral sclerosis (ALS), brain stem stroke, cerebral palsy, and spinal cord injury, who may have little or even no muscle control and therefore no means of communication with the external world. The International Brain-Computer Interface (IBCI) meeting is the flagship conference for the field, and the 11th in the series will be held June 2-5, 2025, at the Banff Centre for Arts and Creativity in Alberta, Canada. Effective BCI research requires interdisciplinary interactions involving neuroscience, psychology, engineering, mathematics, computer science, and clinical rehabilitation, and the IBCI meetings serve as critical catalysts for technology dissemination, new collaborations, and educational opportunities for students. Sponsored in the early years primarily by NIH, the IBCI conferences are now under the auspices of the BCI Society, and the 2025 International BCI meeting will focus on emerging applications and techniques to foster research leading to technologies that enable people to interact with the world through brain signals. NSF funding will enable an additional 18 students, including undergraduate and graduate students and postdoctoral fellows, all from United States institutions, to attend and participate in the conference by supporting their travel and registration as well as the cost of student-only events. Student participation in previous IBCI meetings has been very fruitful; many of those students have now graduated and are prominent researchers in the BCI field. The organizers are actively working to recruit student attendees from traditionally underrepresented groups. More information about the conference may be found online at https://bcisociety.org/bci-meeting/. Reflecting the growth of the field of BCI research, 450 or more participants are expected to attend this year's meeting, including investigators from at least 200 BCI research groups. the program will be similar to that of the successful 2023 meeting, preserving new sessions related to neuroethics and recognition of an exceptional early career researcher. All attendees commit to the entire meeting, from the opening reception and dinner on the evening of Monday, June 2 through the closing session on Thursday afternoon, June 5. A main objective of the conference is to give students a significant educational and professional experience in the BCI field, and to provide opportunities for them to gain depth in their specific interest areas. To these ends, and guided by feedback from a survey of 2018 IBCI attendees, the conference will include interactive events such as workshops and poster sessions along with 7 plenary keynote talks which will be complemented by research sessions and master classes, a BCI users forum as well as BCI didactics sessions, and a Women in BCI social. With all participants housed on site and all meals for all attendees taken together on site, there will be ample opportunity for informal discussions. This creates a unique opportunity for students and trainees to mingle with and learn from established researchers. 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 will fund research that strives to enable swimming robots with novel capabilities customized to a specified range of objectives and environments. Fish, shaped by hundreds of millions of years of evolution, display a diversity of body structures and neural circuits in response to ecological pressures. This project will build upon modular representations of these evolutionary solutions to implement a robot design process emulating natural selection. The project envisions design features grouped into the following three categories: (i) external flow sensing, decision-making, and power management, (ii) body and fin actuation, shape and internal state sensing, and buoyancy control, and (iii) body and fin shape and compliance control. Automated printing and packaging will allow rapid prototyping of candidate robots from this design space. Each robot will undergo physical tank trials using reinforcement learning to develop control policies for a set of characteristic movements, including speed and acceleration of turning, forward, backward, and sideways motion, and energy efficiency during sustained forward motion, which will then be evaluated by physical flow testing subject to an anticipated range of operating conditions. Candidates will compete against each other to accomplish movement-based tasks in relevant flow conditions, with high-scoring designs selected as the starting point for the next round of testing, and low-scoring designs eliminated from further consideration. After multiple such rounds, the winning configurations will be equipped with fluid flow sensors, gyros, and accelerometers, and will learn decision-making and feedback strategies for choosing and blending individual motion primitives to effectively achieve higher-level guidance and navigation objectives. This work will accelerate the application of intelligent underwater robots to address national needs and grand challenges, including search and rescue, disaster recovery, pollution and ecological monitoring, and infrastructure inspection. Associated outreach and STEM education efforts include developing a plug-and-play robot kit and a science class at the Harvard Museum of Natural History. This research will create modular robotic swimmers capable of artificial evolution, to enable novel swimming capabilities such as stable swimming in turbulent flows, navigation towards wakes of underwater objects, performing stable rheotaxis, and dynamic energy savings via real-time adjustment of robot body and caudal fin stiffness and shape. The project will first modularize fish-inspired robotics to create a Modular, Mutational, Morphing Underwater Robot (M3UBot) design space. Next, asynchronous evolution will be performed directly in the physical M3UBot design space for evolving body morphologies and learning motor control programs for modular swimming behaviors (e.g., rapid turning, acceleration, steering, forward or backward swimming). The large-scale robot evolution in physical space and the “plug-and-play” robot assembly will be enabled by innovating 3D-Printing and Electronic packaging (3DPE) for rapid design and automatic fabrication of M3UBot modules. Finally, selected prototypes from the evolved M3UBot population will be equipped with hydrodynamic pressure sensors; they will then undergo reinforcement learning for feedback control and decision-making that combines modular behaviors to navigate in challenging hydrodynamic conditions. Together, this project will transform the fundamentals and applications of underwater robotics, culminating in next-generation intelligent robotic swimmers capable of hydrodynamic perception, active shape morphing and stiffness tuning, and versatile motor skills in challenging hydrodynamic conditions. 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

Many natural systems, including those in the solid earth, are typically described by time series data. Seismic data are known to be summations of many concurrent sources with complex frequency patterns, rendering attempts at automated identification of behaviors difficult. In many cases, rapid identification of transient signals is critical to early warning systems and escalations in hazards, such as with volcanic systems. The advent of machine learning methods has opened the door to unsupervised learning efforts, where a neural network can be tailored specifically to distinguish between different physical behaviors. This project designs an algorithm to learn the various forms of seismic behaviors that exist in a volcanic environment, such as icequakes, lava lake eruptions, ash vent signals, iceberg tremor, teleseismic activity and other phenomena. The project will focus on Erebus volcano, Antarctica, where there are enough multi-scale arrays and where long-term well-understood data exist. The approach will map any time series segment to a position in a different space where similar seismic behavior types cluster closely and are separated from other families of events. Once validated at Erebus, the method will be tested at other glaciated volcanoes featuring long term seismic arrays, such as the Cascades in the Pacific Northwest and the Aleutian volcanoes in Alaska. This project also aims to accelerate exposure of machine learning methods to underserved student populations through the hosting of a cross-disciplinary workshop. Unsupervised time series clustering is a long-standing objective that spans multiple fields including seismology. Whereas classic approaches to clustering involve seismic segments that are tested against human-assigned features such as distributional skew, standard deviation, and others, recent machine learning methods have allowed for the flexible expansion of these efforts through unsupervised non-orthogonal basis pursuit methods. Here, a multimodal variational autoencoder approach is proposed to process array-based continuous seismic data at Erebus volcano with the objective of blindly resolving families of different events contained within. These include icequakes, lava lake eruptions, ash vent signals, iceberg tremor, teleseismic activity, and different manifestations of ambient noise. Once trained, transfer learning will be tested at other glaciated volcanoes in the Aleutian Islands and the Cascades, with the objective of generating a community Python package that will allow researchers to process any data set for preliminary behavioral analysis. A workshop will be hosted at University of Texas El Paso in year 3 to promote educational exposure and internship opportunities in machine learning and data science and will be aimed at underserved graduate and postdoc populations seeking to expand their skillsets. 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

Mathematics brings a wide variety of tools that will be adapted and tuned for the development of new algorithms capable of addressing this variability in threat data observations. The outcome of this research will be new capabilities that will aid in the detection of potential threats in data. Graduate students will be trained in the application of theoretical mathematics to support algorithmic innovations related to an area of national need. This project integrates disparate areas at the interface of geometry, topology, geometric topology, optimization, machine learning and high-performance computing, all connected by the common thread of predictive analytics. A broad geometric framework will be considered that includes the interplay between Grassmannians, flags and Schubert varieties. Further, this foundational geometric framework will be integrated into the machine learning paradigm for building models from data. This approach exploits geometric structure in data taking the manifold learning model to the realm of manifolds built from matrix manifolds. 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

Research in the 1960s revealed that Earth’s outer shell is broken into a dozen or so relatively rigid plates that represent the top of a convecting system in Earth’s deep interior. Motions between and within these tectonic plates create mountain ranges, volcanoes, sedimentary basins, and other major geologic surface features. These features represent vertical relief that, under the force of gravity, is then subject to erosion, landsliding, and other forms of downslope movement of mass. Earth’s topography is thus controlled by the balance between tectonic processes that build relief, and erosional processes that remove and redistribute relief. Conversely, the evolution of topography affects the forces within tectonic plates, influencing subsequent faulting and volcanic activity, and leading to feedbacks over a range of spatial and temporal scales. On million-year timescales, sedimentary basins create natural resource deposits (such as oil and gas reservoirs), and chemical reactions associated with erosion can remove carbon dioxide from the atmosphere, directly influencing Earth’s climate and habitability. On human timescales, the creation of vertical relief promotes landsliding and far-reaching sediment distribution, which is often associated with interacting geohazards including earthquakes, tsunamis, and volcanic eruptions. Building on prior, previously independent work modeling Earth’s interior and surface processes separately, this project develops new computational methods to simulate and advance our knowledge of the dynamic interplay between Earth’s surface and interior and makes these methods available to the scientific community. The computational methods derived through this project have direct societal relevance to studying geohazards and resource exploration. All software developed through this award follows established software engineering practices, is openly available to the public, and is fully documented. Community training activities are used to engage other scientists and promote the adoption of the new methods developed by this project. A major research challenge in the geosciences is understanding how the Earth’s surface and its interior interact to shape one another. Because much of the relevant interactions are inaccessible due to their space or time extents (or both), computer simulations serve as an essential tool for studying interactions in coupled geologic systems. Yet, numerical models have traditionally treated the Earth’s surface and its interior as independent domains. None of the widely used, open-source software packages for simulating mantle convection, long-term tectonics, or short-term tectonics have incorporated surface processes until very recently. Similarly, software for the simulation of surface processes has generally been driven by prescribing vertical uplift rates, even though it is clear that these uplift rates depend on, and thus must be coupled to, erosion rates. This project couples two widely used community codes: (i) ASPECT, a package originally intended for the simulation of mantle dynamics but more recently also used extensively for modeling of long-term processes in tectonic plates, with active development towards incorporating physics (such as compressible elasticity) necessary to capture shorter term processes; and (ii) Landlab, an environment that includes and facilitates the description of surface processes. Since their inception, these codes have transformed the level of complexity of simulations in their respective domains and have gained large user bases. Both codes are backed by large NSF-funded centers: the Computational Infrastructure for Geodynamics (CIG) in the case of ASPECT, and the Community Surface Dynamics Modeling System (CSDMS) in the case of Landlab. The software and workflows developed through this project enable scientific communities that are typically siloed, studying either Earth’s surface or its interior, to initiate new studies of coupled processes with direct societal relevance, including geohazards and resource exploration. Model use cases implemented by the project demonstrate the coupling on different spatial and temporal scales, which can be used by domain scientists to initiate independent research projects. Project training materials are incorporated into long-standing training programs associated with ASPECT (e.g., annual hackathons) and Landlab (e.g., CSDMS clinics), as well as online videos, interactive web visualizations, and at various community meetings and workshops. Finally, a major part of the development effort is parallelizing Landlab, which improves its performance over a wide range of applications, including modeling short time-scale processes such as volcanic eruption cycles, landslides and flooding. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions. 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

Per- and polyfluoroalkyl substances (PFAS) are a group of manmade chemicals that are used in many consumer products and industrial processes due to their unique chemical properties. However, their persistence in the environment poses a significant threat to the drinking water supply of roughly one in three people in the U.S. One promising method for PFAS removal from contaminated water is nanofiltration (NF), a technique that removes nanoscale particles from a liquid using membranes as a filter. Yet, several critical challenges must be addressed to make NF a viable part of PFAS cleanup efforts. First, the effectiveness of NF in removing the wide variety of PFAS types, especially (ultra)short-chain PFASs and those found in complex mixtures, remains unknown. Second, a better understanding of how various forms of PFAS interact with NF membranes at a molecular level is needed. Third, the lack of predictive models to identify key factors that affect PFAS passage through NF membranes hinders rational membrane design and selection. This research aims to address these knowledge gaps by combining experiments and computer simulations, integrated with specialized modeling techniques such as machine learning, to investigate how NF removes PFAS from contaminated water resources. The fundamental knowledge gained through this work will advance membrane-based technologies for remediating PFAS-contaminated water. In addition, this project will include public engagement and educational activities such as developing a new educational module, training students from underserved groups, and hosting outreach activities for PreK-12 students to increase PFAS scientific literacy and awareness. The overarching goal of this research is to use an innovative integration of experimental and computational studies to elucidate the performance and mechanisms of (ultra)short-chain PFAS removal by NF. To achieve this goal, the NF removal performance for (ultra)short-chain PFAS of varied structural features will be evaluated, and the structure-property-performance relationship of PFAS removal in NF treatment will be established using machine learning techniques. The investigators will use non-targeted chemical analyses to further assess the NF performance in removing diverse PFAS from complex aqueous film-forming foam-impacted water. The interactions and transport of PFAS at the water-membrane interface and within polyamide NF membranes will be probed theoretically using molecular dynamics simulations to gain mechanistic insights into the experimental results. The findings of this research will generate fundamental knowledge to inform rational design strategies for developing more effective NF membranes tailored to remediating PFAS-contaminated water. 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

For this project, the PI and collaborators will perform high-precision, quantum-enabled laser spectroscopy experiments on trapped, highly charged ions for the purpose of testing fundamental physics at low energy. Highly charged ions (atoms in which several electrons have been removed) are among the most sensitive systems to a possible time-variation of the fundamental physical constants. This high sensitivity, combined with laser-accessible transitions, makes them a unique platform for investigating predicted extensions to the standard model of particle physics. This project will focus on the development of an optical atomic clock based on trapped highly charged metal ions. The experiment will combine techniques that have been developed for ion trap-based quantum computing and optical frequency standards with a compact source of highly charged ions. This project will also provide training for undergraduate and graduate students in the fields of experimental atomic, molecular and optical physics, optical frequency metrology, and precision measurements. Several optical transitions in highly charged ions provide both an enhanced sensitivity to possible time-variation of the fine-structure constant (alpha) and favorable systematics as optical clocks when compared to singly charged ions and neutral atoms. In particular, these systems are immune to frequency shifts due to the presence of blackbody radiation (BBR). Using quantum-enabled spectroscopy techniques, the research team aims to develop an optical atomic clock based on narrow linewidth transitions in highly charged praseodymium ions (PrXI). A highly charged ion optical clock with a fractional systematic uncertainty at the level of one part in ten to the eighteen, when compared to an optical clock based on singly ionized ytterbium (YbII), could lead to a factor of one hundred improvement in the current laboratory limit on time-variation of alpha. Either an improved limit on the constancy of alpha, or a non-zero signal of the time-variation of alpha could be used to constrain physics beyond the standard model of particle physics. Results from this experimental work will be analyzed in the context of theoretical extensions to the standard model that propose new dark matter candidates and couplings that would lead to the observation of a non-zero value of the time-variation of alpha. 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

Hail is the most consistently damaging hazard of severe thunderstorms, producing losses in the U.S. alone exceeding $10 billion per year over the past 14 years with impacts on homeowners, business owners, aviation, agriculture, transportation, and renewable energy producers. The In-situ Collaborative Experiment for Collection of Hail In the Plains field campaign, or ICECHIP, will improve radar detection and monitoring of hail, provide critical ground-truth information for materials science, and improve the nation’s capabilities to predict hailstorms and their impacts. Current hail forecasting methods struggle to forecast more extreme hail events, and it is difficult to connect radar-observed storm characteristics with specific hail characteristics such as concentration or extreme sizes. Very few observations of hail characteristics other than maximum dimension are even available, nor knowledge of how those characteristics could change during a storm. ICECHIP, the first U.S. hail-focused field campaign in over 40 years, will use modern instrumentation and numerical modeling capacity to provide a long-awaited advancement in hail science. Mobile radars, unpiloted aerial systems, lofted drifters and probes, laser scanning technologies, high-resolution cameras, and more traditional field observations such as atmospheric conditions and surface hail size will all be used to obtain synchronized and comprehensive observations of hailstorms, the hailstones they produce, and the damage they cause. Researchers will deploy a fully mobile network for 6 weeks across the Front Range and Central Plains, gathering observations from a wide variety of hailstorms and hail types. This first-of-its-kind dataset will be instrumental in improving radar-based hail detection, hail models and forecasting, and resulting warnings through diverse collaboration among academic, government, private sector, and international partners. ICECHIP will promote educational efforts through training of 32 undergraduate and 20 graduate students across 10 U.S. universities. ICECHIP will address 5 major research themes, each corresponding to a current significant gap in hail science. In Theme 1, which focuses on hailstone growth and fall behavior, advances in digital photography will be used to explore little-observed microscale hail processes such as tumbling, melting, and shedding with the aim of reducing the uncertainty in microphysical parameterizations and hail growth models. ICECHIP’s comprehensive observations will be used to validate newly developed hail trajectory models and parameterizations in Theme 2, which concentrates on in-storm hail trajectory and convective updraft relationships. Those models will then be used to explore how thunderstorm updraft characteristics and evolution can modify in-storm hail trajectories and surface hail production. Environmental impacts on hail processes and predictability will be examined in Theme 3 by quantifying model hail forecasting skill, with a particular focus on environmental wind, moisture, and temperature profiles. In Theme 4 the surface properties of hailstones and associated impacts will be investigated by linking the internal and environmental storm characteristics to predictable variations in observed hailstone properties (size, shape, density, and strength) in both space and time within the swath and through new insights derived into the impact of wind on hail impacts. These properties will be linked through laboratory and model experiments to different damage modes of materials and structures. Finally, in Theme 5, which focuses on relationships between hailstone physical properties and growth processes to radar observations, radar-based detection of hail size and concentration will be better established through comprehensive ground truth validation that includes the natural variability of hail properties. Radar indicators of updraft width will be linked directly to increases in hailstone mass and damage potential at the surface. 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

A 400 MHz NMR Spectrometer The Analytical Resources Core (ARC) at Colorado State University is requesting funds to purchase a new 400 MHz NMR spectrometer to expand the capacity of our NMR facility for routine 1D and 2D NMR data collection by undergraduate and graduate student researchers and postdoctoral associates in the chemical sciences. The current NMR systems in the ARC can no longer support the growing number of researchers who need daily access to 400 MHz NMR due to recent growth in research programs and new faculty hires in synthetic chemistry all of whom will rely heavily on NMR to operate their research programs. The instrument will be managed within the ARC that is a Colorado State University institutional shared use Core facility homed in the Office of the Vice President for Research. The instrument will be located in the ARC’s NMR laboratory in the Chemistry Research Building, home to all the synthetic chemistry research groups at CSU. At the present time, we do the greatest portion of our routine NMR work on two walkup 400 MHz Bruker NMR systems. One of these lacks automation limiting high throughput sample analysis. The other system is oversubscribed with usage in excess of accessible user time. The proposed 400 MHz NMR will support full automation and be equipped with a sample changer and an automated high performance broadband probe. The magnet solenoid will be actively shielded, have a long hold helium Dewar and will be fully equipped with a helium recovery system. Recovered helium will be purified and reliquefied on the ARC’s helium liquification system making this spectrometer a highly efficient and sustainable system in the current uncertain helium supply environment. The combination of magnet field control, console technology, broadband probe, sample changer and advanced software will provide required sensitivity, resolution and performance to the NIH-supported research at CSU. The new 400 NMR will serve NIH and other funded projects involved with synthesis, catalysis, chemical biology, medicinal chemistry, sustainable polymer chemistry, nanoparticle design for biological imaging and many others. There are five NIH projects supported by this proposal and a similar number of projects funded by other agencies or CSU new faculty startup funds.

NIH Research Projects · FY 2026 · 2024-09

Research: At the time of cancer diagnosis, 40%–60% of patients are overweight or obese. Despite this, there is a lack in understanding how pre-existing obesity influences the development and severity of cancer cachexia— a multifactorial wasting syndrome characterized by the unintentional loss of lean mass, which contributes to poor physical function, treatment intolerance, and reduced survival. While an “obesity paradox” has been proposed, suggesting higher body mass may be protective, emerging data from my F31 and F99 demonstrates obesity is not protective against cachexia, exacerbates muscle metabolic dysfunction, and impairs mitochondrial quality control with cancer. Both obesity and cachexia independently feature mitochondrial dysfunction and systemic inflammation, and when they co-occur, these perturbations may worsen muscle dysfunction and metabolic decline. Extracellular vesicles (EVs) have emerged as critical mediators of cancer-host communication and are known to be altered by both cancer and obesity. Recent evidence suggests tumor- and tissue-derived EVs contribute to skeletal muscle dysfunction through delivery of inflammatory mediators, lipids, and regulatory RNAs that impair mitochondrial health. Notably, exercise is a potent modulator of EV secretion and cargo composition and may represent a non-pharmacological strategy to mitigate EV-driven muscle impairments in cancer survivors with obesity. The goal of my K00 is to develop a mechanistic understanding of the overlap between obesity and cancer cachexia so future studies can develop effective countermeasures. My central hypothesis is that EVs drive cancer induced skeletal muscle wasting and dysfunction in the obese state, and that exercise modifies EV signaling to counteract these effects. I will test this hypothesis through two specific aims: 1) Define EV-mediated mechanisms linking cancer and obesity to cachexia-induced muscle dysfunction, and 2) Establish the therapeutic potential of exercise to improve quality of life through EV-mediated mechanisms in cancer survivors with obesity. This work will generate mechanistic insight into how EVs contribute to cancer-induced muscle dysfunction in the context of obesity and will identify exercise-responsive EVs as potential biomarkers or therapeutic targets for intervention. Career Goals: My long-term goal is to become an independent, NCI-funded investigator at a leading research institution, focused on understanding and treating cancer-associated muscle dysfunction, with a particular emphasis on obesity and survivorship outcomes. Career Development Plan: This K00 award will provide comprehensive training in EV biology, exercise oncology, and metabolism, complementing my prior expertise in cancer biology, skeletal muscle, and obesity. Specifically, I will receive training in 1) bioinformatics, 2) EV biology, 3) translational research, and 4) professional development. The proposed K00 will equip me with technical and conceptual skills needed to launch a successful independent research career and improve outcomes for cancer patients affected by obesity and cachexia.

NSF Awards · FY 2024 · 2024-09

The iconic American Bison (Bison bison) has historically played an important role in North American grasslands, particularly those known as Great Plains grasslands of central North America. Although bison ranged well beyond the Great Plains, knowledge is limited of their effects in these other grasslands. Therefore, the full effects of large-scale removal of bison from the grasslands of North America are unknown. With new efforts to reintroduce bison to their historic habitats, there is a pressing need to understand the effects of these reintroductions. In 2024, the Blackfeet Tribal Nation in Northern Montana is planning to reintroduce free-roaming bison to 26,000 acres of montane fescue grasslands ~150 years since their extermination. This event provides a unique opportunity to examine how bison affect several aspects of ecosystem structure and function, as well as plant species composition. This project has been designed in collaboration with the Blackfeet Department of Fish and Wildlife and Blackfeet Community College, who play important roles in the research. Further, this project provides improved ability to predict bison grazing effects on plant communities in other reintroduction efforts. A grazing experiment in a previously unstudied montane grassland provides data about one of the largest bison restoration efforts on Tribal lands and the North American continent. In collaboration with the Blackfeet Community College, large, fenced exclosures have been established that prevent bison from accessing grassland areas that then can be compared to nearby areas that are open to bison grazing. Both of the grazed and ungrazed areas will be surveyed for plant species and abundances, annual net primary productivity (ANPP, a common measure of plant growth), and nutrients in plant tissues and soils. Collectively, these data will provide a baseline understanding of these grassland plant communities prior to bison reintroduction, allowing Blackfeet Tribal Nation managers to monitor grasslands into the future and researchers to examine over time the effects of bison reintroduction in a previously unstudied fescue grassland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Abstract There have been dramatic and concerning increases in rates of psychological distress in students enrolled in U.S. colleges and universities over the last decade. The majority of college students in the last year experienced mental health problems, and if left untreated, symptoms of these problems have serious individual and public health consequences, both in the short- and long-term. However, the vast majority of students do not receive professional mental health support because traditional treatments are perceived as ineffective and inconvenient, and because on-campus resources cannot meet the demand of students needing support. As a result, it is critical to identify acceptable and effective interventions to address what is being called a “campus mental health crisis”. Mindfulness-based interventions (MBIs) are very well-liked by college students, most of whom are late adolescents; in addition, they are effective at increasing mindfulness and emotion regulation as well as reducing stress and depression. However, MBI effects have typically been small-to-moderate. Outside of the mindfulness literature, technological supplements to group-based programs like MBIs have been found to be effective at increasing intervention efficacy. Our team developed the first multi-modal adaptive supplement to an MBI (5K01AT009592), Learning to BREATHE PLUS (L2B PLUS), which supplements an evidence-based group MBI with multiple methods of support for practicing mindfulness in daily life. Our program of research provides evidence at a single-site that L2B PLUS is feasible and highly acceptable to adolescents, results in sustained levels of engagement across the group program period, and appears to be more effective than the standard, Learning to BREATHE group program (L2B) for increasing daily mindfulness practice and consistency of mindfulness during stress as well as reducing psychological distress; in turn, L2B appears more effective in reducing stress-related behavior compared to an active, didactic health education control (HealthEd). Building directly on our prior work, the proposed R01 study is a multi-site, pilot randomized controlled trial implemented at four sites in order to prepare for a future multi-site efficacy trial testing the effects of L2B PLUS relative to the standard L2B program and HealthEd on depression, anxiety, and stress. Specific aims of the current proposal are to: 1) evaluate multi-site fidelity of training and implementation of 6- week L2B PLUS, 6-week L2B, and 6-week HealthEd to college students experiencing stress, 2) test multi-site feasibility and acceptability of recruitment, retention, and protocol adherence for a randomized controlled trial (RCT) involving L2B PLUS, L2B, and HealthEd, and 3) modify training/implementation and protocol for a future, fully powered multi-site efficacy trial. Completion of these aims will prepare us for an adequately- powered, multi-site efficacy trial, and ultimately inform a complementary and integrative approach to supporting college students experiencing problems with stress.

NSF Awards · FY 2024 · 2024-08

2420676 (Tong) and 2420677 (Lin). Climate change is threatening water sustainability by causing more droughts and limiting water access to people around the world. For example, the Western United States has suffered from severe droughts and heat waves, and the Colorado River has recently experienced record low water levels. Brackish water desalination (BWD) is a promising approach to produce more freshwater, but it is inhibited by the lack of effective strategies for brine management. The goal of this research is to develop cost- and energy-efficient brine treatment technologies that enable decarbonized BWD for climate-adaptive water supply. This goal is targeted to be achieved through interdisciplinary research that integrates fundamental interfacial processes and thermal transport to achieve a solar driven zero liquid discharge (ZLD) system. The environmental impacts of this system will be evaluated by techno-economic analysis, life-cycle assessment, and assessing public acceptance. Further benefits to society will result from research training of college students from underrepresented groups, curriculum enrichment, and outreach and public engagement activities. The accelerating global effects of climate change have resulted in an immediate need of adapting water supplies to the rapidly intensified drought conditions. The nationwide adoption of BWD as a feasible strategy to augment freshwater supply is hindered by the challenge of brine management. Minimizing brine volume via ZLD is the key to render BWD a practical and viable means to mitigate the adverse impact of climate change on water security and resiliency. The overarching goal of this project is to achieve solar driven ZLD for decarbonized inland freshwater production as part of a strategy to address climate change. Specific objectives of the project are to 1) develop a novel process integrating nanofiltration and reverse osmosis to enable cost-effective brine volume reduction; 2) design an innovative interface enhanced crystallizer for energy-efficient and robust brine crystallization, guided by fundamental understanding of interfacial salt crystallization, 3) develop a novel high-efficiency heat pump to power ZLD with interface enhanced crystallizer; and 4) evaluate the sustainability of off-grid, decarbonized inland BWD with ZLD with concurrent techno-economic, lifecycle, carbon flow, and social acceptance assessments. To achieve these objectives, this project will integrate and converge knowledge and approaches from multiple disciplines including environmental engineering, environmental sustainability, interfacial engineering, thermal transport processes, systems engineering, and social science. The successful completion of this project has the potential for transformative impact through enabling decarbonized ZLD to support the wide adoption of climate-resilient inland desalination that improves water resilience against a changing climate. The project will provide undergraduate and graduate students from underrepresented groups with opportunities of preforming interdisciplinary, convergent research to solve an environmental and sustainability challenge of global concern. 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

Understanding the microorganisms that live together in an environment, known as the microbiome, and how they convert hundreds to thousands of chemicals is critical for revealing their impact on biological systems and improving any microbiome applications, from probiotic supplements to waste treatment technologies. This project aims to uncover the interactions between microbes using a new approach that combines experiments, scientific and engineering principles, and computer simulations. Recent biotechnological advances have allowed scientists to study microbiomes in great detail. Microbial members, their genes, and the molecules present in an environment can be identified. However, despite the wealth of microbiome data, it is still challenging to directly determine how different groups of microbes interact due to the complexity of these communities. This project aims to overcome this challenge and apply a new approach to a new waste treatment technology – Rewired Anaerobic Digestion (RAD). RAD shifts from producing traditional biogas, which primarily consists of methane and CO2, to sustainably producing more valuable chemicals such as butyric acid, a biofuel precursor. In addition to scientific advances, this project will also support the development of a Course-based Outreach and Research Experience (CORE) at Colorado State University. CORE will enable undergraduate students to gain hands-on experience in both scientific research and science communication. The research component of CORE is a laboratory class where students conduct research and learn how to isolate and culture of microbial communities. The outreach component involves students in communicating science to the public and stakeholders of the RAD technology. The project will track whether these experiences improve students’ awareness, retention and readiness for careers in integrative biotechnology. Multi-omics has become a powerful approach in revealing what microbial members, functions, and molecules are present in the microbiome. However, multi-omics data does not directly indicate what individual groups of microbes are doing and how they evolve to function and interact in a specific way. The overarching goal of this research project is to develop a novel microbiome modeling platform that integrates omics data, thermodynamics principles, metabolic models, and artificial intelligence to unravel the metabolism, interactions, and evolution of microbiomes. The modeling approach, coupled with omics data generated from RAD reactors and enrichment cultures, will be applied to address specific research questions regarding the RAD microbiome metabolism: (1) identify the butyric-acid-producing microbes, their interacting members, and the metabolites through which they interact; (2) simulate how the RAD microbiome evolves using a novel artificial intelligence algorithm; and (3) test whether thermodynamic driving force determines the microbial division of labor within the microbiome. The project will result in a generalizable integrated experiment-modeling approach for studying microbiomes and reveal principles of microbiome interactions that could also inform other systems such as the butyrate production in the gut microbiome. The knowledge and predictions generated will suggest new strategies for optimizing the product profile of RAD. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award is jointly supported by the Major Research Instrumentation (MRI) and the Chemistry Research Instrumentation (CRIF) programs. Colorado State University is acquiring a 600 MHz nuclear magnetic resonance (NMR) spectrometer, equipped with one solids and two liquids probes and a 60-sample automated liquid sampler. The NMR will immediately impact a broad group of internal and external users, including 29 faculty from 16 departments and Interdisciplinary Centers, Institutes and Schools across 6 colleges at CSU, 11 additional faculty from regional and national institutions and 8 local industries. The proposal is aimed at enhancing research and education at all levels, especially in areas such as chemical synthesis, soil science, wastewater treatment, materials for energy capture and storage, biological tissue mimics, biomedical imaging, drug development, drug delivery, advanced medical polymers, disease diagnostics, and microbiome research. In general, Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most powerful tools available to chemists for the elucidation of the structure of molecules. It is used to identify unknown substances, to characterize specific arrangements of atoms within molecules, and to study the dynamics of interactions between molecules in solution or in the solid state. Access to state-of-the-art NMR spectrometers is essential to chemists who are carrying out frontier research. The cryogenic probe provides a significant increase in sensitivity relative to standard NMR probes. In addition to supporting exciting and impactful research at the home institution and by local and regional collaborators, the instrument also has substantial impact on scientific training. The instrument enables education opportunities for numerous future scientists, including through research for undergraduates (REU) programs. The award is aimed at enhancing research and education at all levels, especially in areas such as chemistry, Cell & Molecular Biology, Civil & Environmental Eng, Electrical & Computer Eng, Mechanical Eng, Environmental, Rad & Health Sci, Ecosystem Sci & Sustainability, Food Science & Nutrition, Microbiology, Immunology & Pathology, and Soil & Crop Science. Specifically, this state-of-the-art, high-field NMR will allow users to tackle previously unattainable research challenges by enabling measurements of solid samples and diverse NMR-active nuclei, while providing exceptional resolution and sensitivity for complex sample analysis. High-impact research projects from CSU users span chemical synthesis, soil science, wastewater treatment, materials for energy capture and storage, biological tissue mimics, biomedical imaging, drug development, drug delivery, advanced medical polymers, disease diagnostics, and microbiome research. Regionally impacted research spans metabolomics, soil remediation, sustainable explosives for mining, efficient carbon conversion to value added products, and drug discovery, development and delivery. Furthermore, the proposed NMR will have impact through existing collaborations across the United States including its integration into established, national programs such as the Soil Carbon Solutions Center at CSU, for which high field SSNMR would benefit researchers nationwide unraveling the complexity of soil organic matter and Earth ecosystems. The instrument will be coupled to an existing helium recovery system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award funds the research activities of Professor Joshua Berger at Colorado State University. Dark matter is a form of matter that makes up roughly one quarter of the total energy of the universe, yet its properties remain a mystery. Developing an understanding of its nature would help understand the basic laws of the universe and the history that lead to the world as we see it today. In his research, Professor Berger will develop new models of dark matter and study avenues to testing these models, with the goal of ultimately developing a complete picture of dark matter’s role in the universe. His research places a particular emphasis on the potential of current and upcoming neutrino experiments, such as the massive Deep Underground Neutrino Experiment (DUNE) with facilities in both Illinois and South Dakota, to extend their current program to a search for dark matter. Another exciting direction that his work will explore is the potential of high-precision measurements of the properties of atoms to detect dark matter. His research will have significant broader impacts. Professor Berger will work with graduate students, providing training in computing and problem-solving skills that have a long history of value to both academic and non-academic work. He will also give public lectures on his research results and support his students in gaining experience communicating with the scientific and non-scientific public. He has a history of incorporating results from his research into his courses and will continue to do so. More technically, he will study the production and detection of new dark sector particles at fixed-target neutrino experiments such as the Short-Baseline Neutrino Experiments and DUNE. The high intensity beams of these experiments have been shown to have leading sensitivity to models such as Higgs portal scalars and Axion-Like Particles (ALPs). Professor Berger will explore further signals of dark sectors and dark matter at such experiments. He will also explore astrophysical sources of dark matter signals, including scenarios with boosted dark matter and macroscopic dark matter scenarios, at the large volume far detectors at DUNE and other long-baseline neutrino experiments. Beyond the work on dark matter at neutrino experiments, Professor Berger will explore new models for dark matter detection in high-precision studies of atomic properties. Ultralight dark matter has been shown to lead to potentially observable deviations in atomic physics measurements. This work will focus on developing strategies to explore new models and to extend the scope of prior searches. It will be performed in collaboration with the atomic physics group at Colorado State University. Professor Berger’s research extends into probes of particle physics in cosmology, including signals of new physics in measurements of gravitational wave spectra and development of new mechanisms of baryogenesis in early universe phase transitions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This doctoral dissertation project investigates the distribution of early human activities and how it may have changed through time at the site of Olduvai Gorge. This site is of key significance as it samples a time period that contains important evolutionary transitions in the human lineage, including more complex stone tool technology, increased meat consumption, and the appearance of a new human species, Homo erectus. Olduvai is also of particular importance because its massive size and the long history of research conducted at the site allows researchers to examine how and why early human activities changed through time at a larger landscape scale. Using geographic mapping methods, this project pulls together multiple lines of archaeological evidence that have previously only painted a partial picture of ancient landscape use. The data generated by this study has value for education, outreach, and conservation at the site in the future, allowing both other researchers and the public to use digital maps and models to explore the landscape. The project builds methods and models that can be applied to other large paleoanthropological sites, furthering researchers' understanding of early human landscape use and evolution. This project also provides important contributions to understanding the evolution of early human behavior and how human ancestors adapted to significant changes in their environment and their place within it. Building off prior paleoanthropological research, this project is rooted in a landscape archaeological approach, focused not only on singular dense concentrations of artifacts, but on the entire network of locations that early humans were frequenting through time. Through this approach, researchers can develop a fuller understanding of where human ancestors were concentrating their activities, and the possible underlying ecological and/or behavioral drivers of the archaeological pattern left behind. The multiple objectives of this project are pursued with a combination of aerial drone survey, geologic mapping, and the compilation and analysis of over 70 years of paleoanthropological data. Broadly, this project investigates how early humans at negotiated their immediate environment, where on the landscape they spent their time, and how researchers can interpret the spatial distribution of the archaeological evidence. One objective is to determine how the surface geology can impact researchers' analysis of where one finds archaeological traces on the landscape. The remaining objectives of the project are to examine different aspects of how the landscape pattern of early human activities changed through time, including how the presence of large carnivores influenced this pattern. A key focus is the impact of the appearance of Homo erectus, a larger brained and bodied human ancestor with more complex stone tools and possibly increased hunting ability, who potentially had a more established presence on the landscape. The adaptive flexibility that is the hallmark of our species has its roots during this time, and as such, the questions investigated by this project have relevance to understanding our evolutionary success. 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

Nontechnical Abstract: Novel quantum materials with exotic electronic and magnetic properties offer a unique opportunity for developing future advanced computing technologies. Two-dimensional (2D) van der Waals (vdW) magnets have emerged as an ideal platform for exploring unique magnetic states and developing functional spintronic devices. Through a collaborative effort between the University of Wyoming and Colorado State University, this research delves into the unconventional magnetic properties of selected 2D vdW magnets. This interdisciplinary team employs both experimental and theoretical approaches, including nanofabrication, magnetotransport measurements, scanning tunneling microscopy, magnetic force microscopy, theoretical modeling, and first-principles calculations. These studies are expected not only to fill critical gaps in our scientific understanding but also to facilitate groundbreaking applications in spintronics-based computing. Furthermore, the project offers substantial research and educational opportunities for first-generation college students in the fields of condensed matter physics, nanoscience, and nanotechnology. The accompanying graduate training program extends into high school outreach, collaborating with educators to integrate cutting-edge research into existing science curricula. Technical Abstract: Controllable magnetization dynamics offers a promising approach for achieving advanced computing using nanomagnetic devices, such as probabilistic bits (p-bits), voltage-controlled magnetic anisotropy, and spin-logic devices in information and communication technology applications. In this collaborative project, the research team focuses on exploring the novel magnetic properties of few-layer two-dimensional van der Waals (2D vdW) magnets. The project has three primary objectives: 1) investigate the fundamental roles and behaviors of unconventional magnetic domains of the novel magnetic properties of few-layer 2D vdW magnets, 2) manipulate metastable magnetic phase transitions within these magnetic domains, and 3) uncover the mechanisms underlying magnetic switching in these materials. Various experimental and theoretical approaches are employed, including nanofabrication, magnetotransport, low-temperature magnetic force microscopy, spin-polarized scanning tunneling microscopy, theoretical modeling, and first-principle calculations. This research project expects to revolutionize the design and function of 2D vdW magnets, enabling the development of simple, energy-efficient, atomic-scale 2D vdW p-bits as fundamental components for the next generation of probabilistic computers. Additionally, the project's educational component provides invaluable opportunities for students and early-career researchers, equipping them with crucial knowledge and skills in advanced materials research and technology, thereby preparing them for significant contributions to the evolving field. 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

There has been growing demand for high-fidelity and efficient simulations in all areas of computational sciences and engineering and particularly so in computational electromagnetics (CEM), both in industry and academia. While high-performance computing platforms have unlocked applications traditionally beyond feasibility, theoretical challenges inhibit the use of conventional approaches even when computing hardware is readily available. Among the most severe challenges, resolving non-smooth and multiscale behavior is nearly universal for realistic problems across application domains. Material interfaces, sharp or non-smooth geometric features, etc. drive poor solution regularity, resulting in extremely poor rates of convergence without new techniques. In addition, small variations due to manufacturing tolerances, environment, and all sorts of imperfections, neglections, and failures can have significant impacts on the macroscopic solution behavior and the relevant computables and measurables. Methods that can resolve fine grain behavior accurately and efficiently will not only revolutionize multiscale simulations, but will also significantly enhance uncertainty quantification, whether for the design of new devices and systems or the analysis of existing ones. The principal objective of this project is the formulation, development, mathematical analysis, testing, and demonstration of a novel synergistic approach to simulation-based design in the presence of multiscale and non-smooth solution behaviors in CEM. This research provides a means for adaptive modeling, error control, and uncertainty quantification of deterministic and stochastic problems and designs in CEM with exponential solution convergence in smooth, non-smooth, singular, and multiscale environments. It has the potential to significantly enhance accuracy, efficiency, versatility, robustness, and practicality of a broad class of CEM methodologies and techniques, so that they can be made accessible, usable, and beneficial to a broad audience of researchers, practitioners, and students. The project’s educational activities include advising and training of graduate students, recruiting students from underrepresented groups, developing new educational materials, and participating in various retention/outreach programs. The research of this project will result in an adaptive, fully anisotropic multiscale hp-element method, aimed to revolutionize simulation-based design. Here, h denotes geometric refinement, p denotes refinement in the expansion order of local shape functions, and hp denotes both enhancements performed simultaneously (a crucial quality), whereas anisotropic or directional refinement is a methodology that allows different h- and/or p-refinements in different directions. Furthermore, the research will use a refinement-by-superposition approach for adaptive fully anisotropic hp-refinements, which superimposes a set of "child cells" over a "parent cell" without removing the parent cell from the mesh, providing significant gains in accuracy, efficiency, and versatility of the computation, as well as dramatically simpler and easier implementation. The new methodological approach features theoretically guaranteed exponential solution convergence rates in all cases and high efficiency of analysis and design; rigorous multiscale error estimation and error control synergy; ability to accurately and efficiently refine multiscale models and eliminate discretization error fully automatically; and adjoint-based multiscale sensitivity analysis in the presence of uncertain model parameters. Overall, the project will develop a multistage adaptive anisotropic hp-refinement method based on detecting and predicting global errors, local error contributions, and solution regularity to adaptively instruct the discretization of the micro- and macroscale problems coupled with accelerated high-dimensional uncertainty quantification of multiscale simulations. Extensive theoretical analyses will be conducted to rigorously examine the transaction between computational resources and accuracy, to understand the limits of resource-bounded computation and to feed those findings back into further development and advancement of the novel methodology. The new adaptive framework can be readily applied to a variety of objectives and as a vital companion tool for emerging and state-of-the-art techniques in, for instance, machine learning and optimization. This extends directly to numerical methods in general, and to multiphysics and other areas of computational sciences and engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Ecological climate, or the air space from the tops of trees to the ground, is the climate that organisms experience. It can provide habitat that is buffered from temperature and humidity extremes. Ecological climate is, however, not represented in climate models, leaving a gap in scientific understanding of how plants and animals in forests will fare in a changing climate and in how forest management might influence climate change. This project will answer key questions about how the structure of forest canopies influences ecological climate, using data from the National Ecological Observatory Network (NEON), and providing information about whether complex forest canopies can provide a buffer from climate extremes. Forests as natural climate solutions are currently being marketed for investment without a full understanding of climate impacts of forest management. The products produced by this project will address this knowledge gap, provide new materials for educators, and grow a more informed ecological modeling community. Observations of forest canopies available from NEON provide an unprecedented opportunity to parameterize and evaluate cutting edge forest canopy models. The overarching hypothesis driving this project is that forest functional and structural diversity modulate ecological climate through their influence on vertical profiles of canopy fluxes, and that these are further regulated by forest demography and climate feedbacks from local to macrosystem scales. To test this hypothesis, the project integrates site-level and remote sensing observational data from NEON into recently developed versions of the Community Land Model (CLM). Using a latitudinal transect of NEON sites in Eastern US forests, from Florida to New Hampshire, researchers will (1) evaluate the influence of forest structure and function on ecosystem fluxes at local and short-term scales across a latitudinal gradient, then (2) consider change over longer time scales by coupling this canopy model to a demographic model, and, finally, (3) assess how forest structure and function influence regional climate by connecting these local-scale results to an atmospheric model. Upon completion, this project will transform our understanding of the role that forest structure and function play in regulating ecological climate across space and time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Program Summary / Abstract The Rocky Mountain Virology Association (RMVA) will hold its 24th annual meeting September 27th-29th, 2024. The meeting brings together regional and national investigators in virology and prion biology for a 3-day retreat- style conference with extensive interaction and collaboration. The original meeting was organized by investigators in Colorado and Wyoming interested in free and open exchange of scientific data and ideas in virology in a venue that promotes collaboration among students, post-docs and faculty. Specifically, our annual meeting at the CSU Mountain Campus encourages young scientists to present their research and receive feedback from established scientists. The goals are promotion of scientific interactions and training. A major benefit of participation has been the novel collaborations that arise between scientists in different disciplines. Topics discussed include medical virology (vaccines, epidemiology, viral zoonoses), arthropod-borne diseases (viruses, RNA metabolism, viral vectors and vector biology), host defenses (viral immunology and pathogenesis), prion biology, cancer biology and systems biology. Special sessions on vaccine development, pandemic viruses, virus discovery and the global impact of viral diseases have been common features. The 2021 meeting had a retrospective on the COVID-19 pandemic with discussions on successful approaches and establishment of systems for future challenges. The next meeting will feature discussions on Immune mechanisms of arthropod vectors, pathogen genomics, arenaviruses, filoviruses, coronaviruses, noroviruses, arboviruses (flaviviruses and alphaviruses), lentiviruses, prion diseases, paleovirology, neurobiology, host-pathogen interactions, virus latency, epigenetics and host response. The meeting offers excellent collaborative opportunities. Attendees include scientists from CSU, University of Colorado, University of Wyoming, University of Northern Colorado, the Centers for Disease Control (Fort Collins) and the Department of Agriculture Animal and Plant Health Inspection Service, regional biotech companies, and universities in Iowa, Idaho, Nebraska, Kansas, Montana, New Mexico and Utah. The RMVA was incorporated in 2010 as an educational charity (501(c)(3)). Our volunteer board of directors is charged with encouraging student and junior faculty involvement by minimizing costs as we encourage women and minorities to participate in all stages of program presentation and development. Our attendance is limited to a maximum of 125 individuals, and the growth of the meeting to capacity illustrates the desire of regional scientists to participate. Funds for this proposal are requested to provide minority grants and childcare, reduced registration fees for graduate students, post-doctoral fellows and early-stage investigators, and for travel and housing for seven invited speakers. Registration fees, charitable contributions, and sponsorships cover the base costs for the meeting. The RMVA has been a source of communication and collaboration in the Rocky Mountain region, with outreach across the nation, for twenty-three years. NIAID support has expanded meeting interest to national and international levels.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract: The main goal of our proposed studies is to evaluate select SIVs for their potential for human transmission using a humanized mouse (hu-mouse) human surrogate animal model. Cross-species transmission events give rise to several deadly human pathogens including HIV-1 and HIV-2 and more recently SARS-CoV-2. While only four HIVs have established themselves in human populations (HIV-1 group M, HIV-1 group O, HIV -2 groups A and B) outbreaks, there were many other lesser ones, namely HIV-1 group P and N and HIV-2 groups C through I, indicating that SIV transmissions to humans are not infrequent events. More than 40 SIVs exist in the wild in non-human primates (NHP) in West and Central Africa and human encroachment into their habitat continues to accelerate, suggesting that potential for the emergence of new human pathogens still exists. Thus, we need to be vigilant and conduct viral surveillance. In this context, animal models that can permit testing of SIV cross- species transmission and evolution are needed. For this purpose, the new generation hu-mice that harbor a transplanted human immune system appear to be highly suitable. In work centered on SIV progenitor viral evolution into HIVs, we and others found that hu-mice are susceptible to SIV-chimpanzee (SIVcpz) the progenitor of HIV-1 and SIV-sooty mangabey (SIVsm) the progenitor of HIV-2. More recently, we discovered that hu-mice are also permissive to macaque-derived SIVmac251, a widely used virus in NHP studies in the context of HIV research. However, thus far no previous studies examined the human infection potential of more primitive SIVs in the wild. Here we will evaluate two SIV strains, namely SIVrcm from red capped mangabeys and SIVmnd2 from mandrils, viruses from two distinct NHP species. These were previously studied in their native hosts wherein they display high viral loads but are non-pathogenic. These two viruses are genetically linked to SIVcpz which is the progenitor for HIV-1. While a full comprehensive study of these in vivo will be a complex undertaking, here in this R21 grant of a limited budget and scope, we propose to start with the following simpler initial goals. Aim 1: Investigate the potential for zoonotic transmission and sustained human infection by select SIVs, SIVrcm and SIVmnd2 using a humanized mouse model. Aim 2: Characterize key pathogenic attributes of the human adapted viruses for cell tropism, helper CD4 T cell loss and capacity for sexual transmission. Knowledge gained from here will shed light on the initial steps in cross-species transmission.

NSF Awards · FY 2024 · 2024-08

Observations from meteorology, climatology, and oceanography indicate that the global intensity of ocean waves has increased since the mid- to late-20th century. Increased ocean wave size and energy have fundamental role in erosion and other coastal hazards, as well as to human infrastructure, marine and coastal ecosystems, marine-terminating glaciers, and to other natural systems and human activities spanning the ocean, atmosphere, and solid Earth. The impact of ocean waves on the sea floor are measurable by the same instruments that detect earthquake activity across the globe. Seismic data thus provide a unique record of large-geographic-scale and long-duration ocean wave activity, and global seismographic networks are sufficiently widespread and long-lived so that the progressive seismic signal of ocean wave intensification has emerged. This project analyzes global seismic data extending back four decades to understand, localize, and quantify ocean wave state, incorporating related observations from other Earth science fields. A public-facing, non-specialist- accessible website will display and distribute near-real-time regional and global microseism metrics, placing the present in historical context. Global ocean wave energy intensification has recently become apparent in the long-term continuous seismic record, particularly but not exclusively as increasing primary microseism Rayleigh wave energy, which is sensitive to ocean waves in near-coastal regions between approximately 20 and 14 s period. With an international group of collaborating experts, this project estimates, interprets, and globally models the frequency- and polarization-dependent regional, seasonal, and secular spatial-temporal evolution of Earth’s microseism wavefield since the late 20th century in the context of global change. The project has three primary objectives. First, it advances our understanding of the long-term observational record of Earth’s incessant ocean gravity wave and seismic microseism field, including forward modeling of wave hindcast metrics, corroborated in the Mediterranean with in-situ MERMAID water column data. Second, it studies geophysical source processes guiding end-to-end seismic signal generation and the informed use of seismological proxies to infer ocean wave activity variations at multiple time scales. To this end, the project applies and advances state-of-the art long-period seismic modeling codes driven by wave hindcast inputs to improve understanding of microseism source regions, geographic influences, and seismic source efficiency. Project PIs, postdocs, collaborators, graduate students, and undergraduate students conduct this work in the context of ocean wave state studies emerging from the meteorological, oceanographic, and climate communities. Third, the project incorporates outreach activities that advances public awareness and appreciation for seismology and geophysics in the historical context of the long-term ocean wave evolution and the occurrence of exceptional wave events and communicates results so that they may complement other ocean-wave data products from the atmospheric, oceanographic, and climate communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Any systematic study of Nature, from exploring sub-atomic structures to observing remote galaxies, finds that the texture of our universe is highly symmetric. In other words, there is a set of fundamental symmetry principles, which appear to hold universally on all length scales and energy scales. For instance, Einstein's theory of relativity rests on the concept that spacetime is symmetric: The laws of physics are supposed to be the same in all inertial reference frames and the speed of light is assumed to be the same in all directions. But how well can we experimentally test such symmetry assumptions? Given that physicists have by now gathered overwhelming evidence that our current understanding of Nature is incomplete, symmetry test experiments are crucial when looking for hints of "new physics" beyond the established models. In this project, the PI and his team want to further improve the precision of tests of fundamental symmetries by developing a particularly sensitive quantum detector for potential symmetry violations. They propose that a novel optical atomic clock based on ytterbium ions in a low-temperature environment may discover minute asymmetries or can deliver improved bounds on several potential symmetry violations. This project will also provide training for graduate and undergraduate students. These students will develop technical skills related to the research, as well as critical scientific judgment. More specifically, the team wants to investigate via high-precision laser spectroscopy a so far unexplored optical electric octupole (E3) transition in singly-charged ytterbium 173 confined in a cryogenic ion trap. Compared with measurements in room-temperature environments, black body radiation induced frequency shifts will be reduced to a negligible level. Similarly, frequency-shifting background gas collisions will be strongly suppressed. The improved spectroscopic precision combined with favorable atomic properties of the 173-Yb ion will make it possible to turn this optical atomic clock into a very sensitive antenna for potential effects of new physics beyond the Standard Model, e.g., the coupling of dark matter to quarks and gluons and violations of Lorentz symmetry in the strong force and electromagnetic sectors. 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.