University Of Illinois At Urbana-Champaign

NSF Awards · FY 2024 · 2024-10

Energy generation and storage, environmental remediation, and chemical manufacturing require materials that speed up important chemical reactions such as carbon fixation. Often these materials operate in complex, dynamic environments where their structures change. For designing robust high-performance catalysts that reduce energy consumption and waste, it is important to know the atomic-level structure of the catalyst under operating conditions so as to understand why a specific material catalyzes an important chemical reaction or why performance deteriorates under certain conditions. Principal Investigator Jain from University of Illinois at Urbana-Champaign and his collaborators from Humboldt University and Helmholtz-Zentrum in Berlin are using advanced characterization methods to image in real time the atomic structures of catalysts comprised of light-absorbing nanoparticles of metals that catalyze carbon fixation using light energy. Their research will lead to more robust catalysts for solar-powered carbon fixation and sustainable chemical manufacturing technologies. In the practice of heterogeneous catalysis, the surface structure, composition and/or oxidation state of the catalyst may change into the active form in response to operating conditions. Therefore, there is a need to probe the chemical state of catalysts in situ under the action of stimuli and reactive conditions. As one prime example, plasmonic nanostructures are known to exhibit superlative catalytic activity under light; however, such nanostructures can dynamically evolve in operando due to effects of plasmonic excitation, heat, and reactive environments. Principal Investigator Jain and collaborators will elucidate currently unknown active-state structures and dynamics of plasmonic nanocatalysts using a laser-coupled dynamic environmental transmission electron microscopy at UIUC complemented by high-resolution electron energy loss spectroscopy and in-situ X-ray absorption spectroscopy in Berlin. The team will probe hybrid nanostructures consisting of plasmonic absorbers with catalytic domains during plasmon-catalyzed carbon dioxide and carbon monoxide hydrogenation. The aim is to uncover the structures of the active states of these hybrid nanostructures and elucidate dynamic structural fluctuations that may underlie their catalytic activities. These insights will fill the current knowledge gap in structure–reactivity relationships for hybrid plasmonic catalysts. The success of this project may lead to design principles and protocols for more robust photocatalysts for solar-powered carbon dioxide reduction and sustainable synthesis of fuels and chemicals from carbon dioxide. The work may also advance the use of advanced electron microscopy methods in the chemical industry for examination of nanoparticle-based catalysts and train students for the research and development 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 · 2024-10

Graph neural networks (GNNs) represent a family of deep learning methods designed for interrelated data. These data are called graph data because the graph is used to highlight the interconnections or nodes between the data points. GNNS produce representations of the nodes to explore the interrelationships and have provided a solution for a wide range of scientific applications, ranging from social media analysis, neuroscience, healthcare, climate science, finance, aviation, e-commerce and biology. The existing research on GNNs has generated rich theories, systems for designing GNNs architectures, training GNNs with strong empirical performance. As the landscape of GNNs continues to broaden and deepen, the fundamental safety issues of GNNs are less well studied and several important questions largely remain open. To name a few, how can one rigorously and quantitatively measure the safety of GNNs models? How safe are the existing GNNs models in the presence of hazards? How can one make the GNNs safer during the training, adaptation and testing stages? What are the fundamental limits and costs for enforcing the safety of GNNs? This project investigates the end-to-end safety of graph neural networks, with a systematic effort to examine the safety issues in the entire life cycle of GNNs, taking into consideration various types of dataset shift and realistic external perturbations. The project consists of three integral research thrusts, including: 1) safe graph neural networks training; 2) safe graph neural networks adaptation; and, 3) safe graph neural networks testing. The project seeks to establish new theoretical results in terms of the sensitivity, NP-hardness, confidence, and generalization error bound of safe graph neural networks. It learns realistic perturbations and introduces new discrepancy measures for graphs, which in turn lead to the development of new algorithms for safe GNNs training, adaptation and testing with better efficacy and robustness. The developed theories and algorithms are evaluated on both synthetic and real-world datasets, are integrated into the courses that the investigators teach, and are further disseminated via publications, tutorials and open-source software. The project team actively seeks to engage under-represented students during the course of the project. 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

Variation in diversity of organisms across geographical regions has captured the attention of biologists for decades. Such patterns of diversity are driven by a combination of biogeography and ecological differences among species. The fish family Leuciscidae (minnows) presents an excellent opportunity to study the interplay of biogeography and ecology. Following migration to North America, these minnows have diversified extensively. This minnow diversity is often overlooked, even though species of Leuciscidae occur in freshwater streams across the continent. This research will test the origins and drivers of biodiversity in Leuciscidae. It will elucidate present-day biodiversity of these minnows, and will improve our general understanding of patterns of diversity across North America and globally. The project will train undergraduate and graduate students, and postdoctoral researchers. The research team will develop a public website on minnow diversity, will collaborate with grade school teachers to develop and implement lesson plans on biodiversity and museums, and will support outreach through traveling science museum programs. An improved understanding of diversification of the family Leuciscidae requires a robust understanding of the evolutionary relationships, information on ecological differences among species, and data on species ranges. This project will reconstruct the phylogeny of Leuciscidae using new genome sequencing data, with broad sampling across the family. Researchers will use the phylogeny to determine timing and number of migrations to North America, and to investigate the biogeography of species and how species distributions have changed over time. They will compile ecological and morphological data, reconstruct how traits have evolved, and use comparative approaches to study adaptation and its role in diversification. This integrative approach using multiple lines of data will yield valuable insights into evolution of this important group of fishes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The goal of this project is to synthesize existing tree-ring proxy data (ring width, stable oxygen isotope ratios, blue light intensity, and gridded Palmer Drought Severity Index (PDSI) estimates) to create a seasonal-resolution paleo-streamflow reconstruction for Southeast Asia. The investigators will compare the reconstruction to paleo-streamflow estimates generated by forcing a Southeast Asia hydrological model with Paleoclimate Modelling Intercomparison Project Phase 4 (PMIP4) hydroclimate variables for the last millennium. The comparisons will include assessment of variability in modeled streamflow and comparison of dominant modes and spatial/temporal coherency of variability in the proxies and models. These analyses seek to illuminate the mechanisms that drive the observed dynamics. The project includes support of two early career researchers from underrepresented groups, a graduate student and plans to include undergraduates in the research, development of an interactive public-facing web application with educational materials, and visualization of streamflow through space and time as part of an effort to help create more effective water management strategies for a densely populated region. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The human brain is made up of many regions that communicate with one another with an extensive array of axons (nerve fibers), forming multiple interlocking networks. “Hub” brain regions, much like airport hubs, have brain connections to many different regions and networks, forming a complex web of neural connections. Hubs participate widely across a diverse set of cognitive functions. Because of their widespread connections, hubs are well positioned to link functions across the brain as needed for executive and task control (the ability to flexibly guide thoughts and actions depending on our goals). Task control is vitally important for achieving both short-term goals (e.g., waking up early and carrying out a morning routine to get to work on time) and long-term goals (e.g., studying rules of the road and practicing supervised driving in order to learn how to drive and obtain a driver’s license) in everyday life. Task control requires that the brain flexibly coordinate relevant sensory, motor and cognitive functions to achieve those goals. This research explores how hub regions of the brain help to organize and coordinate these task control functions. Network neuroscience methods, based on non-invasive human neuroimaging data (using fMRI – functional Magnetic Resonance Imaging), can be used to identify, and elucidate the connectivity of brain hub regions. Prior work has shown that damage to these hub regions leads to widespread brain disconnections, and has a powerful impact on behavior and on brain interactions. However, the functional role of brain hubs in task control is still poorly understood, in terms of their specificity across task contexts and their role in representing task information. This research explores how and when brain hubs are used in different behavioral contexts and across multiple tasks in healthy subjects, and investigates the impact of individual differences in hub organization. This project uses fMRI to measure the connectivity, and task-related activation of hubs, in three inter-related research objectives to: (1) determine how hubs modulate information flow across diverse task contexts and task difficulty levels, (2) determine whether and with what specificity hubs represent task parameters, and (3) determine whether individual differences in hub organization relate to individual differences in task control functions. This investigation deepens our understanding of the functional role of hubs - leading to potential future insights into how brain hubs may be targeted to improve goal achievement. 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

Infectious airborne pathogens and their spread have caused increased incidences of outbreaks and pandemics, which challenge our society with severe impacts on public health, economies, and social well-being. Controlling airborne pathogens is challenging because they are transmitted easily via respiratory aerosols, spread quickly over large distances, and can mutate to become more infectious and morbid in a short time. Reactive approaches such as treating illnesses or responding to outbreaks after they occur are usually ineffective and costly. In contrast, proactively addressing airborne pathogens is of paramount importance for minimizing the likelihood of outbreaks and safeguarding public health. A proactive approach in combating airborne diseases necessitates early detection of target pathogens and effective communication to ensure prompt collective action within communities. However, it remains extremely challenging to achieve rapid, accurate, scalable, and cost-effective on-site detection of airborne pathogens. Meanwhile, along with the deployment of the pathogen sensing technologies, there are important questions to address on how to communicate potential risks and mitigation options to communities. To address these challenges, this SCC-PG project brings together a highly interdisciplinary team of researchers and uses community-based participatory research to engage a broad range of community partners. Success of this SCC-PG project will provide innovative and proactive solutions in response to the grand challenge of public health protection. Such a proactive system will also foster a culture of prevention and preparedness to better handle future community health threats. The research will significantly advance science and technology in designing, constructing, modeling and deploying a novel biosensing system timely in-field airborne pathogen monitoring. The intellectual contributions are three-fold. First, the project will create an intelligent system for automated, real-time, and multiplex monitoring of critical airborne pathogens. In addition, an intelligent automated system to obtain time-resolved measurements of infectious aerosols in-field will be established. Second, the project will create a novel data-driven quality-aware deep learning approach which not only effectively generates inference and reconstruction results but also provides rigorous accuracy quantification and interpretation. Third, the project will develop a new framework of ethical dialogue to provide collaborative spaces that will lead to greater involvement and empowerment of the public with the focus on effective risk communication and mitigation. 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

Parenting plays a crucial role in youth development, influencing a myriad of health behaviors. As a result, parenting is a common focus of interventions aimed at improving youth health outcomes. Effective parenting interventions and their study necessitate robust assessments of parenting. However, the state of the evidence for psychometric properties of existing parenting assessments is weak. The Multidimensional Assessment of Parenting Scale (MAPS) is a measure of parenting exhibiting strong psychometric properties. However, the utility of the MAPS remains limited by the omission of certain parenting dimensions, as indicated by preliminary studies. Other limitations impeding progress in measuring parenting include: 1) underutilization of qualitative methods, 2) the need for multi-informant reports (e.g., youth or coparent), 3) the absence of validated ecological momentary assessments (EMAs) of parenting, 4) limited use of advanced psychometric methods, and 5) underrepresentation of various demographic groups, including fathers, in parenting research and theories. Further, preliminary data from my current R36 project show that the MAPS can be enhanced through revisions informed by mixed methods, which resulted in better measurement quality indices and measurement equivalence of the MAPS across various parent identities. Therefore, in this project, I propose introducing multiple innovations to optimize the MAPS. Aim 1 involves conducting qualitative interviews with parents and experts, followed by qualitative and text mining analyses of interview data, to inform revisions to the MAPS and identify unmeasured parenting domains, which will yield the MAPS-S for self-report. Aim 2 will focus on developing and field testing the MAPS-S for coparent (MAPS-C) and youth (MAPS-Y) report through cognitive interviews and national surveys. The objective is to examine factor structures, reliability, and measurement equivalence in two independent samples to ensure the replication of strong psychometric properties. In Aim 2, Spanish measures will be piloted in an additional national sample. Aim 3 employs item response theory to develop an optimal EMA MAPS, which will be tested with parents and youth dyads. Across all aims, I will examine relations between MAPS subscales, MAPS agreement, discrepancy, and youth health outcomes to establish convergent validity. This project is innovative in its use of novel approaches, including iterative, multimethod, multi-informant, EMA, and data-informed methods, to develop and validate novel parenting scales. By involving parents from underrepresented demographic groups, including fathers, we will be able to accurately determine when and how to intervene and prevent negative or ineffective parenting across demographic lines. The assessments emerging from this study can be clinically employed to screen, target, and monitor negative or ineffective parenting in interventions and prevention programs with families from various backgrounds. Ultimately, this project will contribute to the advancement of parenting research and interventions aimed at improving youth health outcomes more broadly.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY In 2018, 51.8% (129 million) of American adults had at least one chronic condition (e.g., diabetes, arthritis). The total cost of chronic conditions is $3.7 trillion; about 84% of healthcare costs are attributed to chronic condition treatment. Self-management is critical to reducing clinical and economic burdens. Multiple sclerosis (MS) is one of the most demanding cases, costing approximately $28 billion for about one million Americans with MS. However, people with chronic conditions, including people with MS (PwMS), often face challenges in applying self-care information and partnering with their clinicians over their illness progression to reflect their ever- changing needs. Unfortunately, the existing self-management interventions and mHealth solutions for PwMS and other patients do not support self-sustained personalization of self-management through lifelong user modeling. To address these challenges, we propose MyMSMentor, a Health Action Process Approach (HAPA)- driven artificial intelligence (AI) agent, to estimate users’ states and provide just-in-time guidance. Overarching goals are: [i] developing personalized, lifelong self-management support that is contextualized in an individual’s daily life and available resources; [ii] making MyMSMentor generalizable for the millions of people who need lifelong self-management of other chronic conditions. The innovation of the study focuses on bridging HAPA, social-cognitive science, and AI algorithms to build a theory-based intelligent healthcare advising agent that can support the adoption and maintenance of health behavior. Specific Aims are: (1a) Build MyMSMentor for proactive patient-centered self-management support; (1b) adopt participatory design to refine and evaluate MyMSMentor iteratively; (2) validate the feasibility and acceptability of MyMSMentor with PwMS. For Aim-1a, we will build [i] lifelong comprehensive user modeling by combining the Information Need Model, Known Information Model, and HAPA status variables and [ii] five function modules (i.e., symptom recording and updating, case summary generation, note-taking, user model inference module, HAPA-based interactive intervention module). For Aim-1b, we will collaborate with patient and clinician advisory boards to co-design and evaluate MyMSMentor iteratively. For Aim-2, 50 PwMS will use MyMSMentor for 30 days. They will use feasibility and acceptance measures to evaluate MyMSMentor and provide user feedback. The study will make a critical and timely contribution to establishing a generalizable novel model to bridge big data, artificial intelligence, and social- cognitive science for precision self-management among people with chronic conditions and support the National Neurological Conditions Surveillance System to advance data-powered research and healthcare services.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Complementary medicine (CM) approaches are increasingly used by health care consumers, accepted by the medical community, and viewed as a cornerstone of whole person health. However, much about the effectiveness and safety of CM approaches, as well as the mechanisms through which they affect human health and well-being, remain poorly understood. Published literature is a growing source of evidence on CM approaches, their effect on human health, and their biological mechanisms. However, much of this evidence remains in unstructured text and specialty journals. Furthermore, the quality of this evidence is often questioned. The size, growth, and the quality of the literature makes it difficult for researchers and clinicians to access reliable evidence on these topics. Concurrently, the number of curated databases on CM is growing, but they remain limited to relatively narrow subtopics. Comprehensive resources and tools focusing on CM approaches are currently lacking. For systematic use of the high-quality evidence on these topics for medical discovery and patient care and effective integration of CM approaches with conventional medicine, scalable methods to distill, standardize, and aggregate knowledge from disparate research literatures (e.g., CM, human metabolism, microbiome, immunology) and curated databases are needed. We hypothesize that informatics approaches, in particular natural language processing (NLP) combined with ontologies and knowledge graphs (KGs), can underpin such consolidation and integration. In this project, we aim to develop and validate comprehensive knowledge resources and NLP methods for mining the literature on CM approaches including their mechanisms of biological action (which we dub COMB literature). We will integrate the mined information with knowledge from curated databases in a KG to support knowledge management and hypothesis generation applications. Specifically, we aim to: (1) construct informatics resources to support COMB-related knowledge integration and extraction; (2) develop NLP methods to mine COMB knowledge from biomedical literature; (3) construct a COMB knowledge graph from literature and curated databases and demonstrate its utility for question answering and hypothesis generation. The successful completion of this project will deliver a comprehensive ontology of CM interventions and their biological mechanisms, the first annotated corpus broadly focusing on CM approaches, novel NLP models, and an integrative KG on CM approaches and their effects on human health. Furthermore, validation of these resources and tools on real- world CM questions by domain experts will demonstrate their potential for patient care and scientific discovery. We anticipate that the KG can be integrated with other biomedical knowledge bases and with evidence generated in omics studies (e.g., metagenomics, metabolomics) as well as clinical data (e.g., electronic health records), bringing us closer to a more complete understanding of the mechanisms involved in whole person health and improving human health and well-being.

NIH Research Projects · FY 2024 · 2024-09

Neonicotinoids are the most widely used insecticides in the world because they broadly target chewing and sucking insects. Imidacloprid (IMI) is a common neonicotinoid that accounts for 30% of neonicotinoid sales globally. IMI is used in commercial agricultural systems, sold for use in home gardens, and found in veterinary pharmaceuticals in the form of flea and tick preventatives for companion animals. IMI is also used as crop seed treatments and spreads throughout crops as they mature. Thus, IMI cannot be washed or peeled off produce. As a result, humans are routinely exposed to IMI through consumption of contaminated food and water as well as interactions with their pets. IMI kills insects by acting as a systemic neurotoxicant after binding nicotinic acetylcholine receptors (nAChRs) in the nervous system. To date, exposure to IMI has not been considered a public health concern because it is a weak agonist for mammalian nAChRs compared to insect nAChRs. However, our preliminary data indicate that the ovary bioactivates IMI to desnitro-imidacloprid (DNI). This is of concern because DNI has a higher affinity for nAChRs than IMI. Further, our preliminary data indicate that the ovary contains nAChRs and that DNI is toxic to antral follicles. Specifically, DNI exposure causes slow antral follicle growth, decreased estradiol production, follicle rupture, and increased expression of the pro-apoptotic factor Bax in mouse antral follicles in vitro. Slow follicle growth, increased Bax, follicle rupture, and low estradiol levels are of concern because they can lead to subfertility/infertility. Although IMI exposure leads to production of DNI by ovarian follicles, DNI is an ovarian toxicant in vitro, and ovarian toxicants often cause subfertility/infertility, we do not know if IMI exposure causes ovarian toxicity and female subfertility/infertility in vivo. Thus, the goal of the proposed R21 studies is to expand our preliminary data using a mouse model to test the hypothesis that IMI exposure leads to molecular changes in the ovary to cause ovarian toxicity, leading to female subfertility/infertility. To test his hypothesis, we will: 1) determine the extent to which IMI causes ovarian toxicity and female subfertility/infertility in vivo and 2) identify IMI- induced changes in molecular factors in the ovary using spatial transcriptomics. Collectively, the proposed R21 studies will determine whether IMI poses a female reproductive health hazard in mammals and should be considered for regulation of use in adult women.

NIH Research Projects · FY 2025 · 2024-09

The long-range goal of my laboratory is to measure the conformations of biomolecules at the single molecule level with nanometer and (sub)-millisecond resolution, ultimately in a cell. Here our focus is mostly on conforma- tional changes within the cytoplasmic molecular motors—kinesin and dynein and eventually myosin— and on a new “limping” mechanism that may lead to short (in distance and time) substeps. We also investigate motors’ response when multiple ones operate on a cargo simultaneously and how they respond to force, which may underpin many clinical abnormalities. Both in vitro and in vivo (cellular) measurements are proposed. On single kinesins, for example, in vitro conformational changes involving sub-steps have recently been revealed and extending this to other motors and as a function of force is a major goal. This work requires MIN- FLUX (Project 1), a fluorescence technique that can achieve extraordinary resolution of 1–2 nm at a rate of 0.1– 1 msec, while extending the photobleaching time to many minutes. Doing this under force with nanometer-size fluorescent probes means that almost any site can be monitored—but without the limitation of optical traps which require large beads to monitor only the center-of-mass. MINFLUX will also be used in vivo, looking at fluorescent peroxisomes moving radially away (via kinesin) and towards (via dynein) the nucleus of a cell. Combined with recent drugs, this approach will enable us to determine whether the speed of the cargo is a function of the type and number of motors, as we (controversially) proposed. The Univ. of Illinois has recently purchased a MINFLUX microscope that we will use in this work. It will also be extended to have an applied force. MINFLUX, however, can look at only one fluorescent molecule at a time, making acquiring statistics slow and the simultaneous conformations of many motors difficult. In contrast, Fluorescence Imaging with One Na- nometer Accuracy (FIONA), which we invented in 2003, can look at a whole ensemble of molecules with na- nometer- and msec resolution. But to see biomolecules with such fast resolution, an unusually bright fluorophore that doesn’t bleach is required. In Project 2, quantum dots (QDs) and fluorescence nanodiamonds (FND)— particularly within 50 nm of a photonic crystal (PC)—give (an amazing) 900––3000-fold increase in fluorescence with little blinking. For the first time, PCs will be applied in vitro to molecular motors (by FIONA and possibly by MINFLUX, all with an applied force) and on living cells (to EGFR and AMPAR membrane proteins). In Project 3, we have a new technique for intracellular, live cell labeling that will enable (organic) fluorophores to penetrate the cell membrane, sometimes in the presence of native oxygen. We can use either new fluoro- phores (from Janelia Farms) combined with a new transfected protein (from E. coli dihydrofolate reductase, eDHFR), or we can use Streptolysin O (SLO) that temporarily permeabilizes the cell membrane and allows essentially any fluorophore to enter the cell. We show that three different fluorophores may be used simultane- ously, extending our previously published technique.

NIH Research Projects · FY 2026 · 2024-09

Healthcare workers represent a large and growing segment of the US workforce, and the strain of the COVID- 19 pandemic on the healthcare system has brought to light the significant stress, trauma, and burnout that healthcare workers experience. These experiences may have lasting effects on healthcare workers’ substance use, mental health symptomatology, and suicidality, particularly among those in lower-wage occupations. These workers often have fewer supports and resources, less autonomy in the workplace (e.g., scheduling/hours worked, workload), and experience greater occupational hazards than their higher-earning counterparts yet remain highly understudied. Most of the published studies related to substance use, mental health symptomatology, and suicidality among healthcare workers have disproportionately focused on physicians and other high-wage healthcare occupations. The objective of the proposed research is to examine the social (e.g., social support, peer network behaviors, perceived norms) and environmental influences (e.g., working conditions, traumatic exposures, life stress) on the substance use, mental health symptomatology, and suicidality of non-prescriber/non-executive healthcare workers over time, with particular attention to the effects of moral injury (i.e., psychosocial and behavioral impacts of “failing to prevent or bearing witness to acts that transgress deeply held moral beliefs and expectations”) and workplace policies, programs, and practices. The rationale for the proposed research is that identification of factors beyond the individual level that confer risk or protection to healthcare workers’ substance use, mental health symptomatology, and suicidality can inform the development of more effective prevention and intervention efforts, particularly as it relates to the implementation of psychosocially safe and healthy workplace practices. Individual-level explanations for people’s risk and resilience to substance use, mental health symptomatology, and suicidality remain dominant in the scientific literature. However, we will also focus on interpersonal, organizational, community, and societal factors – and their intersections with socioeconomic positioning – that affect the health, well-being, and risk for substance use, substance-related harms, and substance use disorders among healthcare workers. This research proposes to 1) examine the effects of moral injury on changes in substance use, substance use disorders, problematic mental health symptomatology, and suicidality; 2) examine the impact of other individual (e.g., occupational level, job satisfaction, sleep quality/disturbance, quality of life), social (e.g., social support, peer network behaviors, perceived norms), and environmental factors (e.g., working conditions, traumatic exposures, life stress) on these outcomes over time; and 3) examine the unique impacts of workplace policies, programs, and practices on the risk and resilience of healthcare workers. We will pursue these aims using an innovative approach and unique focus. The proposed research will examine a diverse sample of healthcare workers recruited via social media, including low-wage healthcare support occupations (e.g., nursing assistants, dietary aides, custodians). The proposed research is significant because the examination of factors external to the individual will identify modifiable social and environmental risk factors that are more efficient and effective intervention targets, given the broad impacts of population-level interventions.

NIH Research Projects · FY 2025 · 2024-09

Project Summary In nature, metalloenzymes make up approximately one third of the known enzymes and play central roles in biological processes such as photosynthesis, respiration, and nitrogen fixation. Heme-containing metalloenzymes are particularly important in the context of energy conversion reactions due in part to their ability to cycle between redox states. Furthermore, heme-containing enzymes can oxidize a broad range of substrates in both a stereospecific and regiospecific manner, attracting interest for use in challenging chemical reactions requiring C-H activation, C-C bond cleavage, or heteroatom oxidation. These enzymes are thus prime candidates for biotechnological and synthetic applications, including production of pharmaceutical drugs, chemical conversion of fatty acids and steroids, detoxification of drugs and toxins, and the synthesis of the industrially relevant chemicals, fragrances, and flavors. While heme proteins have been studied extensively, increasing amounts of genomic data have led to the prediction of many additional heme, diheme, and multiheme protein families, of which the functional roles and biochemical properties remain unexplored. Once such protein is MbnH, originally identified in methanotrophic bacteria. MbnH is a member of the PF03150 bacterial cytochrome c peroxidase (bCcP)/MauG superfamily and utilizes a rare bis-FeIV cofactor for the oxidation of a specific tryptophan residue within a partner protein, MbnP. While other high-valent Fe states, such as compound I and compound ES, are relatively well understood, given the limited biological examples of diheme bis-FeIV states, there is a fundamental gap in our understanding of how nature stabilizes and uses bis-FeIV states without causing cellular damage. Nearly all previous research has focused on MauG, limiting the generalizability of the role of the bis-FeIV species and the chemical capacity of such an oxidant. Thus, the proposed research will leverage the huge amount of genomic information available to expand the scope of studies on the bCcP/MauG superfamily. This includes representative members of the annotated but uncharacterized protein families, SPOA0271 and TIGR3891, as well as new cluster identified by the Enzyme Function Initiative Genome-Neighborhood Tool (EFI-GNT) tool as clusters 4 and 11. The broad objective of this application is to establish how divergent bacterial diheme proteins stabilize high-valent bis-FeIV states and then use that intermediates to accomplish site-specific substrate oxidation. It will address outstanding questions related to the formation and stabilization of bis-FeIV, the catalytic capability of the bis-FeIV species, and the biological role of this cofactor.

NIH Research Projects · FY 2024 · 2024-09

Emerging diseases are a growing global problem, and wildlife pathogens are the principal source of disease. Interactions between wildlife and human communities are on the increase, leading to greater zoonotic and epizootic spillover. The genetic similarity of humans and non-human primates (NHPs) provides an opportunity for viruses to impact both human and animal populations, especially in areas where contact is continuing to increase. Notable knowledge gaps persist concerning the dynamics and likely routes of viral transmission within such heterogeneous natural environments. Here, we examine a model pathogen, adenovirus (AdV), common to humans, NHPs, and many other hosts and sources, to determine and model transmission routes in an interconnected biodiversity hotspot in East Africa. We will apply next-generation DNA sequencing, combined with demographic data and advanced phylogenetic analysis techniques, to identify likely transmission pathways and to model host factors and interactions affecting viral transmission. Our four main objectives are to: 1) Obtain fine-grained, longitudinal surveillance data on AdV from wild NHPs, domestic animals, flies, ticks, dung beetles, water, and soil in a natural, heterogeneous spillover hotspot; 2) Apply advanced phylogenetic and bioinformatics tools to next-generation sequencing (NGS) data for the presence, abundance, variation and routes of transmission of AdV within and among hosts and environs; 3) Identify source factors impacting viral transmission, e.g., host or source type, proximity, density, other viruses and pathogens, host evolutionary relationships; 4) Develop prediction models of how factors (e.g., host species,, phylogeny, proximity, source) influence viral transmission routes, with the ultimate aim to inform mitigation efforts regarding disease transmission for the benefit of human and animal health. By focusing on a common, prevalent virus like AdV, we can identify, quantify, model, and ultimately predict key aspects of viral transmission and the factors affecting spillover in complex natural environments, which will be applicable for understanding and preventing disease transmission more broadly. In addition, this research builds capacity and supports broader African and U.S. genomic and biostatistics training. The knowledge gained will be instrumental for policy decision-making to prevent and mitigate zoonotic and anthroponotic disease outbreaks and improving quality of life with conservation and public health benefits. Specific Aims Section Our central hypothesis is that variation in AdV strains and distribution provides an effective means of identifying, tracking, predicting, and ultimately limiting pathways for disease transmission. These invaluable insights, integrative analyses and predictive potential can be extrapolated to model transmission of other, less common, but potentially more dangerous viruses to mitigate disease transmission. Specifically, we hypothesize that: 1) Phylogenetic analyses of viral variation can be used to identify host origins and routes of transmission, generally and under changing natural and anthropogenic conditions; 2) The prevalence of anthroponotic or zoonotic virus transmission is greater among organisms that share proximity (e.g., cattle or chickens); and 3) Viral transmission is predicted to be more prevalent among organisms that share closer evolutionary relationships (e.g., Catarrhine monkeys). To address these hypotheses, we have three main objectives: 1) Use molecular approaches to detect and identify the presence and abundance of AdVs among interconnected wild and domestic animal and insect hosts and the environment in rural Western Uganda; 2) Apply bioinformatic and computational analyses of AdV genomes to identify the presence, identity, abundance, and variation of AdV strains, and then applying phylogenetic and phylodynamic analyses of AdVs to identify routes of viral transmission within and between host species and environs; and 3) Develop predictive models based on these data to enable greater understanding of how cross-species viral transmission occurs in a complex environment. Modified

NIH Research Projects · FY 2024 · 2024-09

Project Abstract The University of Illinois (UI) Veterinary Diagnostic Laboratory (VDL) is a full service, AAVLD accredited, all species, reference veterinary diagnostic laboratory. As a member of the VET-LIRN, UI VDL is one of the labs actively involved in several VET-LIRN activities including iSeq-sequencing Listeria project as a lead lab, providing isolates for MiSeq WGS sequencing project as a source lab, participation on antimicrobial resistance pilot project, and evaluation of automatic nucleic acid extraction methods as a lead lab. Molecular testing process usually takes couple hours or more to get the results out to clients. Therefore, ultrafast molecular equipment with same sensitivities as commonly used molecular equipment is urgently needed and can significantly enhance testing service efficiency in Vet-LIRN network laboratories. In this proposal, we request funding to purchase a 5- minute superfast nucleic acid extractor Xtractor and a 5-minute real-time PCR machine XDive to enhance UI VDL molecular service quality for testing animal diseases and routine case investigation and surveillance coordinated by Vet-LIRN. This funding support is crucial for maintaining our capacity in molecular testing and critical response to animal food/drug emergency or disease outbreak.

NIH Research Projects · FY 2024 · 2024-09

Understanding and decoding the intricacies of gene regulatory networks is crucial in genomics for insights into gene expression and cellular functions. Traditionally, research in this field has heavily relied on transcriptome data and machine learning to infer these networks, but this approach has mostly used bulk tissue samples. This method overlooks the nuances of individual cells and their microenvironments, limiting our understanding to a broader, macroscopic level. The advent of spatial transcriptomics marks a significant shift, promising to unravel these networks at a single- cell and spatial level. This technology allows for the exploration of gene expression in relation to spatial dynamics, enhancing our understanding of tissue organization and cellular functions. However, adapting machine learning to spatial genomics faces challenges. One major issue is the scarcity of spatial transcriptome data, which hampers the effectiveness of deep learning methods known for their superiority in network estimation. Another challenge is the need for models that account for the physical positions of cells, as traditional methods treat data as independent and identically distributed, ignoring spatial relationships. To address these challenges, this proposal outlines two main objectives: Aim 1: Developing deep learning methods for cell-type resolution regulatory network estimation capable of transferring between scRNA-seq and spatial transcriptomics data. We will develop machine learning mdoels that can integrate components that explicitly model the regulatory network, distinguishing cell types based on transcriptomic data. The approach will use domain-invariant regularization to adapt from scRNA-seq to spatial transcriptomics, employing GTEx and HuBMap data sets. Aim 2: Developing deep learning methods with spatial regularization for estimating regulatory networks at spatial resolution within spatial transcriptomics. We will develop techniques that factor in the spatial positioning of cells during the learning process. The hypothesis is that cells in close spatial proximity have similar regulatory structures. This aim will also use GTEx and HuBMap data, along with collaborative efforts on spatial transcriptome data of the human dorsolateral prefrontal cortex. Overall, this proposal seeks to lead the development of advanced deep learning models, integrating cell-type resolution and spatial dimensions to revolutionize our understanding of regulatory networks in genomics to both the cell-type and spatial resolution.

NSF Awards · FY 2024 · 2024-09

Discovery and telescope follow-up of rare and exotic sources is critical to pushing the boundaries of understanding of fundamental physics. A relatively new capability of combining information from different messengers to more completely understand the universe is called multimessenger astronomy. Multimessenger time domain astronomy is a powerful new tool for exploring the cosmos. The four messengers that astronomers study are light in all its forms, cosmic rays, neutrinos, and gravitational waves. Many source types change rapidly with time. It is critical that observations occur simultaneously or within a short time span so that astronomers capture the properties of different messengers before the source changes. The key challenge is coordinating community follow-up across multiple observatories with many instruments, minimizing duplication, and maximizing the value of the combined dataset. Over a 3-year award, a team led by the University of Illinois at Urbana-Champaign will build a Hop-Enabled Real-time Observatory Information and Coordination (HEROIC) service. HEROIC will collate, save and publish observatory and instrument status (present, planned, and archival), from a worldwide network of ground- and space-based facilities. Moreover, HEROIC enables teams from smaller institutions to more effectively participate in MMA science. HEROIC will provide the entire astrophysics community a single destination to quickly see what facility can point to a multi-messenger source, what observations are currently being scheduled or undertaken with them, and which groups to coordinate with. HEROIC will be a central component in a fully integrated, interoperable cyberinfrastructure for astrophysics, where scientists can receive alerts, plan and trigger follow-up, and share observations with each other, breaking barriers between previously siloed facilities. The HEROIC service can potentially be extended to all major ground- and space-based facilities. The tools that HEROIC provides are essential for all time-domain astrophysics, and will enable entirely new studies, as scientists will be able to use existing facilities and archival data more effectively, while also encouraging coordination between teams. By developing critical infrastructure that is useful for multiple facilities, HEROIC also frees observatories up from having to develop/maintain similar services themselves, reducing their development burden, and increasing the incentive to participate. 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 NSF project aims to create new techniques for fault detection in inverter-dominated power systems. The most typical faults are unintentional short circuits caused by, for example, lightning and tree contact, and can cause substantial damage if not quickly dealt with. Detecting and extinguishing short-circuit faults are thus critical aspects of reliable power system operation. In power systems with synchronous machine-based generation, large, unbalanced fault currents provide clear information about the existence and location of faults. Inverters prevent such currents even during faults. As a result, conventional fault detection schemes can fail in grids that are rich in inverter-based resources like wind and solar generators. One way to make it easier to distinguish normal operation from a fault is for the inverter to add a perturbation, or auxiliary signal, when there is suspicion of a fault. This project will design new, auxiliary signal-based fault detection schemes for inverter-dominated power systems. The intellectual merits of the project include characterization of when and what auxiliary signals are necessary, how many inverters in a grid must inject them, and minimal infrastructure investments necessary to guarantee fault detection. The broader impacts of the project include enhanced reliability of modern power systems, which will further facilitate the integration of inverter-based resources like renewables and energy storage; and curriculum development at the graduate and undergraduate levels, and energy-oriented programming for K-12 students. This project will develop the use of auxiliary signals in inverter-dominated power systems. Typical choices of auxiliary signal include negative sequence current, as in IEEE Standard 2800, and harmonics. The auxiliary signals will be optimized so as to minimize disruption while guaranteeing detection of all possible faults. The existing theory will be extended in scenarios not covered by existing tools, for example, networks with multiple inverters and relays. The mathematical formalization of this problem will constitute a streamlined, optimization-based procedure for designing new detection schemes, which is relevant today as grids and grid codes continue to rapidly evolve. All new detection schemes will be validated in electromagnetic-transient simulation and in controller hardware-in-the-loop testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research project addresses the pressing global challenge of reducing carbon emissions from cement production. Ordinary Portland cement, produced at a rate of 4 billion tons/year worldwide, alone contributes to 6-8 percent of worldwide anthropogenic CO2 emissions. Here, the focus is on developing sustainable belite-rich cements that can be produced with existing rotary kilns and raw ingredients, requiring lower energy and emitting less CO2 compared to conventional alite-rich cements. Belite, traditionally underutilized due to its lower intrinsic reactivity, holds the potential for a sustainable alternative if its performance can be enhanced. This project will systematically identify and test various substituting ions and dopants to improve the reactivity and mechanical properties of belite-rich cement pastes. The successful development of high-performance, belite-rich cements will not only reduce the carbon footprint of the cement industry but also support national interests in advancing sustainability and lowering energy costs associated with cement production. The project includes an educational outreach component, leveraging virtual reality (VR) to engage and inspire students in STEM fields, thus contributing to workforce development and promoting science and engineering among younger audiences. The technical objectives of this research include the 1) identification of effective foreign dopants to enhance belite reactivity, 2) comprehensive experimental validation of these dopants, and 3) the synthesis of cementitious clinkers with improved performance characteristics. The project will then employ advanced characterization techniques, such as Raman spectroscopy, solid-state NMR, and synchrotron-based X-ray scattering, to understand the structural and performance changes in doped belite samples. Finally, the research will also involve the analysis of their long-term hydration behavior and assessment of the mechanical properties of belite-rich cements. By integrating novel dopants into the belite structure, the project aims to significantly reduce energy consumption and CO2 emissions during cement production, paving the way for the widespread adoption of more sustainable cementitious materials in the construction industry. 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

In the era of advanced robotics and AI, there is a notable gap in leveraging these technologies for personalized human assistance, especially in settings that require nuanced understanding. Current robotic systems, despite their impressive locomotion capabilities, often lack the flexibility needed to adapt to the diverse needs of users, as their interactions are typically restricted to predefined functionalities. This limitation is particularly evident in scenarios where humans teach robots to perform highly personalized tasks within shared physical spaces, which may lead to discomfort and safety concerns among users. This EArly-concept Grant for Exploratory Research (EAGER) project will fund research that attempts to address the challenging task of a quadrotor to pick up/drop off a small package from/onto a human's outstretched hand following the human's instructions. The challenge of the task comes from the quadrotor's close proximity to a human, which can trigger stress due to noise and movement, potentially undermining task completion. The research effort seeks to address the above-mentioned task utilizing a framework that enables anyone to safely instruct robots in customized tasks. This research is crucial for the exploration of harnessing the intelligence enabled by machine learning to expand the capabilities of robots toward humans’ needs, especially in the industries that urge rapidly growing robot participation and coordination with humans, e.g., manufacturing, logistics, transportation, and national defense. This research aims to attain the objective of allowing quadrotors to safely interact with people for the chosen task of direct hand-to-hand package delivery, ultimately leading to robots that can genuinely adapt to and meet individual needs for more personalized and safe human-robot interactions. The researched effort incorporates the human's cognitive state into the quadrotor's decision-making and action processes, fostering a bidirectional sensorimotor interaction and allowing both the human and the quadrotor to sense and influence each other's decisions and actions while the quadrotor conducts the task in close proximity to the human. Two interconnected research thrusts will be pursued: (i) planning and control for safe human-robot interaction with models for human cognitive states and (ii) iterative learning for enhanced performance towards efficient and safe human-robot interactions. The research framework builds upon the advances made by the PIs in control theory/engineering and psychology and is expected to make important contributions to the society of the future, in which humans and robots behave and interact safely and effectively while occupying shared spaces. This EAGER award has been co-funded by the Dynamics, Controls, and System Diagnostics and the Mind, Machine, and Motor Nexus 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 · 2024-09

Can one induce a desired behavior on another through incentives? This NSF project aims to answer this question through mathematical modeling and analysis. The project is transformative in that it seeks to remove stringent assumptions required for existing incentive design mechanisms that limit its practical use. The questions are motivated via target application contexts. The intellectual merits of the project include a careful combination of many topical areas and wide applicability of the resulting designs in engineering, environmental policy making, and organizational structure, among others. The broader impacts of the project include inter-disciplinary graduate training, research dissemination through publications and seminars, promotion of diversity within the research teams, and engagement with K-12 students. Incentive design is a hierarchical decision-making problem that considers how one rational agent might induce a desired behavior on another through the design of policies. The proposed research will draw on recent advancements from a variety of fields, including control theory, game theory, operations research, machine learning, and behavioral economics to circumvent restrictive assumptions on incentive design. By doing so, the theory will apply much more broadly. Five thrusts of the project will build along the three following themes: (1) Can agents learn each other’s preferences through repeated interactions? (2) How do behavioral traits influence incentive structures? (3) Can incentives be designed to accommodate complex hierarchies of interactions among a population? Besides advancing the theory of incentive design, the project will investigate select applications of the theory in the domain of power systems, specifically in the coordination of distributed energy resources. 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 broader impact of this I-Corps project is the development of a general software tool to improve online information management and discovery. The goal is to help helps communities of users organize saved information around the questions that the community members ask and where they ask it. As a result, the information needs of community members can be anticipated and resolved, which minimizes user effort and mitigates many of the problems of finding online information. This technology has the potential to be applied in any setting where groups of individuals share common contexts and goals. For example, in an educational setting, students may use the tool to efficiently ask and answer questions on course-based material, such as lectures, assignments, and readings. In an enterprise setting, knowledge workers can leverage the tool to reduce the reliance on tacit knowledge, thus making the organization more efficient. This technology may help mitigate inefficient online information discovery by using saved information to guide the learning process. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a collaborative software platform with the goal of helping mitigate inefficient online information discovery by using saved information to guide the learning process. The solution is based on the use of large language models and search algorithms. It uses generative large language models to help predict the questions that a user will have in a given context, a novel framework for using the historical interactions of community members to guide future community members in similar contexts, and an actively developed software platform that supports these technologies via various interfaces, including both a website and a browser extension. In addition, the platform may provide an ecosystem for researchers to learn more about many previously understudied problems in information retrieval, such as proactive search, through the de-identified data collected by the platform. 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

Compelling historical evidence from accidents such as the Fukushima Daiichi nuclear disaster, the Boeing 737 Max accidents, and the Deepwater Horizon oil spill has shown that human performance (e.g., errors, confusion), physical failure mechanisms (e.g., material degradation), and organizational factors (e.g., organizational culture and training) all played important roles in contributing to the outcomes. To prevent catastrophic technological accidents, risk engineers need to consider the interactions of technical, human, and organizational factors, yet they rarely do so. This project aims to (a) advance modeling of potential risks in technological systems, (b) apply new methods to diverse industry and regulatory settings, and (c) develop an educational platform for socio-technical risk analysis. By integrating knowledge from risk science and engineering with insights from organizational and social sciences, this initiative informs regulation and policymaking. Next-generation leaders can utilize the results to contribute to ensuring they deliver safe, resilient, sustainable, and socially responsible technological advancements. The solutions derived from this project help to protect the public and environment and foster a new generation of risk engineers equipped to tackle socio-technical challenges. Despite progress in Probabilistic Risk Assessment (PRA) and Human Reliability Analysis (HRA) within engineering systems, the spatiotemporal interplay of human, physics, and organizational (HPO) factors has not been fully captured. The Principal Investigator has advanced theoretical foundations, methodologies, and computational platforms for (i) incorporating organizational factors into PRA (ii) spatiotemporal coupling of physical failure mechanisms with human performance, and (iii) incorporating spatiotemporal human-physics coupling into PRA. Yet there is still the need to account for the complex interplay of HPO factors in a unified manner and to demonstrate how the spatiotemporal interactions among them influence the safety risk of technological systems. The project bridges this gap by focusing on causal relationships among HPO factors and enhancing the theoretical and methodological bases to incorporate HPO interactions into risk models. The project focuses on bringing about the mid-career advancement of Principal Investigator Mohaghegh by enabling her to work intensively over time with senior experts in organizational psychology and behavior (Ostroff), risk regulation and policymaking (Rowell), and the PRA of critical infrastructures (Bier). 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

Quantum technologies have the potential to radically impact science and society in the information age. In particular, quantum networks may eventually enable distributed quantum computing and new forms of quantum sensing. However, a deeper theoretical understanding of quantum networking is needed in order to verify the functionality and guide the development of novel architectures and use-cases for quantum networks. This project will explore new theoretical tools to study how entangled states can be created, manipulated, certified, and characterized in realistic quantum networks. This project will make a strong connection with current experimental capabilities while developing methods and protocols that are of direct relevance to current and near-term quantum network implementations. This project is structured along two main research lines. In one direction the research team aims to characterize the structure of multipartite entanglement in networks. The key questions are: which entangled states can be generated in a network? How can one efficiently certify multipartite entanglement in networks? How can one quantify network entanglement in an operationally meaningful way? In a second direction this team will explore the properties of quantum measurements at each network node. The fundamental questions they will explore are: what type of local measurements are possible when multiple quantum signals are received at different times and quantum memory is limited? What classes of multipartite entangled states can be generated under constrained entanglement swapping measurements? How do restricted local measurements lead to novel notions of network quantum steering and data hiding? This research project is poised to initiate new experimental collaborations, such as with researchers at both the PIs home institutions, the University of Geneva and the University of Illinois Urbana-Champaign. The US-Swiss scientific collaboration supported by this project will therefore grow beyond just the two PIs. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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

CI Pathways is an innovative project designed to help researchers effectively use advanced cyberinfrastructure (CI) tools in their scientific work. These tools are essential for modern research, aiding in critical tasks such as efficient and large-scale data collection, analysis, and sharing. However, many researchers struggle to integrate these resources due to a lack of training, an environment for practice, and continued support. CI Pathways addresses this by offering a structured, cohort-based training program where participants learn alongside peers and receive guidance from experienced mentors. By including participants from diverse backgrounds and disciplines, CI Pathways will promote inclusivity and broaden access to these critical resources. The project aligns with NSF's mission by promoting scientific progress and fostering education and diversity. It helps bridge the gap for underrepresented groups in science and engineering, advancing national prosperity and welfare. Additionally, by equipping researchers with the skills to use CI tools, the project contributes to national interests in scientific advancement and innovation. The program's success will be shared with the wider research community, enabling other institutions to adopt similar models and further amplify the project's impact. CI Pathways aims to empower researchers with the skills and knowledge to integrate advanced cyberinfrastructure (CI) resources effectively into their research workflows. The project addresses two primary challenges: a lack of awareness and training on the benefits and opportunities of CI resources and insufficient guidance and support in accessing and using these resources. By overcoming these challenges, CI Pathways seeks to enhance research productivity and innovation across diverse scientific disciplines. The project employs a cohort-based, mentor-supported training model. Participants will be selected through a diverse, equitable, and inclusive application process, emphasizing representation from underrepresented groups and non-traditional domains. The program offers a combination of self-paced and live training sessions with a focus on CI tools, data science, and machine learning. These sessions and their supporting content will be made available to the community at large through incorporation into the NSF ACCESS Knowledge Base. Each cohort will be supported by experienced mentors who will guide participants through their individualized learning paths. Participants will have access to CI resources to practice their newly acquired skills in real research contexts. A mentor training program will be offered to participants who complete their learning paths, preparing them to serve as mentors in future cohorts. By fostering a supportive and collaborative learning environment, CI Pathways will cultivate a community of researchers who can share knowledge and support one another in their use of CI resources, especially those provided by NSF’s ACCESS. 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.