University Of California-Irvine

NSF Awards · FY 2024 · 2024-11

Non-Technical Summary This award supports theoretical and computational research and education to enhance the accuracy and efficiency of first-principles quantum mechanical simulations, which are essential for understanding the electronic structure of materials at the atomic level. In today's rapidly evolving technological landscape, developing new materials with superior properties is crucial for advancing modern technologies and industries vital to the US economy, such as electronics, energy, and healthcare. These simulations rely on approximate theories, creating a challenging tradeoff between accuracy and computational cost. Finding a way to make this tradeoff more favorable for accuracy without significantly increasing computational cost is critical. By leveraging advanced machine learning and artificial intelligence techniques, the research team seeks to create innovative methods that refine these approximations, potentially leading to the discovery of novel materials tailored for specific applications. This initiative not only contributes to materials science but also underscores the importance of education and mentorship in fostering the next generation of scientists. The research team is dedicated to preparing students for successful careers in both academia and industry by equipping them with essential skills in artificial intelligence and innovative research practices. By actively engaging with students, the project aims to nurture new talent within the scientific community, paving the way for breakthroughs that can address pressing real-world challenges. Additionally, the new methodologies developed from this project will be incorporated into libraries used by standard electronic structure software packages, which will be made freely available to the research community. Technical Summary This award supports theoretical and computational research and education towards enhancing the accuracy and efficiency of Density Functional Theory (DFT) simulations, a standard method for studying the electronic structure of materials at the atomic scale. While DFT offers a balance between accuracy and computational cost, it relies on approximations that can limit reliability. This project aims to develop innovative approximations to the exact functional using advanced machine learning techniques. The key developments include: 1) Database Optimization: Compiling a comprehensive database of solid materials to inform the development of new approximations. 2) Machine-Learned exchange and correlation models: Implementing new functionals within the established Jacob's ladder approach to ensure compatibility with standard electronic structure codes. 3) New Functional Design: Utilizing non-conventional descriptors to optimize the modeling of strong correlations in solid-state systems. The project addresses the urgent need for improved materials design, with significant implications for industries relying on DFT calculations. By applying machine learning to develop more accurate approximations, the research will contribute to the discovery of new materials. This initiative not only contributes to materials science but also underscores the importance of education and mentorship in fostering the next generation of scientists. The research team is dedicated to preparing students for successful careers in both academia and industry by equipping them with essential skills in artificial intelligence and innovative research practices. By actively engaging with students, the project aims to nurture new talent within the scientific community, paving the way for breakthroughs that can address pressing real-world challenges. Additionally, the new methodologies developed from this project will be incorporated into libraries used by standard electronic structure software packages, which will be made freely available to the research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project is a collaboration between three institutions: University of California-San Diego, Xavier University of Louisiana, and University of California-Irvine. The human blood contains different cell types that are continuously produced, while older cells die. As this process continues while the organism ages, mistakes are made during cell production, generating mutant cells. These mutants can linger in the blood and become more abundant over time. They can contribute to chronic health conditions and there is a chance that they initiate cancer. It is not well understood why these mutant cells persist and expand. One problem that has held back progress is that for obvious reasons it is impossible to perform experiments with human subjects to investigate this. Mathematics combined with epidemiological data, however, offers a way around this limitation. This project develops mathematical models describing the evolution of mutant cells in the blood over time, using experimental mouse data to define the model structure. New mathematical approaches are then used to adapt this model to the human blood system, by bridging between mathematical models of mutant evolution in the blood, and the epidemiological age-incidence of mutants in the human population. There is broad public health impact, since this work can suggest ways to reduce the mutant cells in patients, which can alleviate chronic health conditions and reduce cancer risk. From the educational perspective, the PIs collaborate with Xavier University of Louisiana, an undergraduate historically black university, to foster enthusiasm in continued education and careers in STEM, and equip students with knowledge and skills to potentially continue in graduate programs at top universities, thus promoting social mobility. As higher organisms age, tissue cells acquire mutations that can rise in frequency over time. Such clonal evolutionary processes have been documented in many human tissues and have become a major focus for understanding the biology of aging. Gaining more insights into mechanisms that drive mutant emergence in non-malignant human tissues is an important biological / public health question that needs to be addressed to define correlates of tissue aging. While experiments in mice have suggested possible drivers of mutant evolution in tissues, a central unresolved question is whether (and how) knowledge from murine models can be applied to humans. Mathematics provides a new approach to address this challenge: We propose a multiscale approach that uses mathematics to bridge between cellular dynamics of mice and humans, by utilizing epidemiological data of mutant incidence in human populations. We use “clonal hematopoiesis of indeterminate potential” (CHIP) as a study system, where TET2 and DNMT3A mutant clones emerge in the histologically normal hematopoietic system. Based on stem cell transplantation experiments in mice, we seek to construct a predictive mathematical model of mutant evolution in mice. Using the hazard function, this in vivo model can predict the epidemiological incidence of mutants. Fitting predicted to observed mutant age-incidence data for humans will yield a parameterized and predictive model of human TET2 and DNMT3A mutant evolution. Public health impacts include a better understanding of mutant evolution in the human hematopoietic system, which may lead to evolution-based intervention strategies to reduce CHIP mutant burden. 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

Limb regeneration is a complex biological process not fully understood at the genetic level. Salamanders are the only vertebrates with limbs that can completely regrow a lost limb. However, some fish, like the African lungfish and the grey bichir (Polypterus), can fully regrow their fins, even if they are cut off at their base. This ability is not found in commonly studied fish such as zebrafish. The proposed research will use a multilayered, comparative approach, looking at salamanders, lungfish, and Polypterus to identify the key elements needed for limb and fin regeneration. The hypothesis being tested is that these species deploy a shared genetic program of regeneration. First, this proposal addresses whether a specific molecular signaling (the mTOR signaling pathway) is a common feature of both limb and fin regeneration. Next, a comprehensive dataset of gene expression information will be obtained from the animal models to search for a shared set of genetic and cellular tools for regrowing limbs and fins. Finally, DNA elements that control gene expression during limb and fin regeneration will be identified and the hypothesis that loss of the ability to regenerate is linked to changes in how tissues control gene activity will be tested. These studies using multiple species will help reveal general mechanisms that control the complex process of regeneration. This project will train researchers at multiple academic stages, from undergraduates to postdoctoral researchers. Outreach to middle school students will provide research opportunities to underrepresented populations and therefore contribute to broadening participation in STEM. Limb regeneration is a prime example of a complex biological trait for which the genetic and genomic underpinnings are poorly understood. Although salamanders are the only limbed vertebrate that can regenerate the entire limb, fishes such as the African lungfish (Protopterus annectens) and Polypterus fully regrow fins even when the amputation occurs at the very base of the fin, across the proximal endoskeleton. This ability to regrow entire fins is lacking in traditional fish models such as the zebrafish. This proposal uses a phylogenetically-informed, multi-scale approach, using the axolotl, the lungfish and the Polypterus, to identify the core components of a shared “toolkit” of limb and fin regeneration. The first aim of the project tests the hypothesis that a rapid activation of an mTOR-mediated translational program is a conserved feature of limb and fin regeneration and identifies transcripts differentially translated during the early event of wound closure that marks the onset of regeneration. The second aim is focused on the integration of bulk, single nucleus and spatial transcriptomics datasets to determine if our animal models activate an evolutionarily shared genetic and cellular “toolkit” for appendage regeneration. In the third aim, epigenetic profiling will be deployed to reveal shared gene regulatory networks of limb and fin regeneration and test the hypothesis that loss of regenerative capacity is associated with widespread divergence of tissue regeneration enhancers. The multi-species, systems-level studies proposed here will bring the field closer to uncovering the general mechanisms governing the complex trait of regeneration. This proposal is co-funded by the Division of Integrative Organismal Systems (via the EDGE program and the Developmental Systems Cluster), The Division of Emerging Frontiers, and the Division of Environmental Biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project presents intelligent anonymization methods for preserving the privacy of clients’ bio-signals while retaining data utility for clinical purposes. Bio-signal anonymization methods proposed in this project safeguard clients’ personal data against potential stigmatization, judgment, and discrimination. This fosters patients' participation in healthcare and research studies without fear of identity exposure, thereby enabling the development of efficient data-driven AI models for smart healthcare. This project develops modular and scalable anonymization methods that are suitable for both bio-signals from clinical settings as well as bio-signals acquired from wearable devices in everyday settings. Bio-signal anonymization models proposed in this project are highly adaptable and can be customized for clients across diverse demographics and existing health conditions, achieving a ubiquitous coverage of clients. This project directly impacts the healthcare sector by minimizing regulatory costs, improving trust and confidence between clients and healthcare providers, and delivering high-quality smart healthcare services. This project will also train the next generation of digital healthcare providers in curating clients’ data and developing fundamentally secure smart AI models for healthcare. The first technical thrust develops anonymization models for multi-channel bio-signals through reinforcement learning guided generative deep models. This thrust will design reinforcement learning models to understand critical details of bio-signals for adaptive pruning of anonymization models for different health conditions. This approach balances the competing objectives of obfuscating re-identifiable information while preserving the structural characteristics of bio-signals critical for diagnosis. The anonymization models use conditional and multi-view generative adversarial networks to generate multi-variate bio-signals by sanitizing the original signals. The second technical thrust develops a privacy assessment and evaluation framework for updating anonymization models subject to different attacks. This thrust develops an evaluation framework comprising utility and anonymity metrics to provide feedback on updating bio-signal anonymization models based on utility-anonymity analysis. This enables bio-signal anonymization models to be customizable for clients across diverse demographics and pre-existing health conditions, ensuring both fairness and utility-privacy guarantees. 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

Bioelectronic devices have numerous potential benefits to human health, from in-home wellness monitoring to diagnosis and treatment of neuropsychiatric diseases. However, safe and effective use of these devices is limited by the rigid, non-biocompatible electronic components that must be incorporated to allow execution of the required functions. This project seeks to study how soft and fully biocompatible materials can be leveraged to interact directly with signals from the body without damaging tissue. A transistor fabricated from these materials will be used to create the circuits necessary for bioelectronic devices to acquire and modulate the activity of neurons in the brain. The outcome of the research will benefit society by improving the design of bioelectronic devices currently used for patients with conditions such as epilepsy or Parkinson's disease by eliminating the need for implantation of bulky or rigid materials in the body. This project will also facilitate understanding of the principles underlying interactions between the body and electronic devices. The educational component of this project leverages ion-gated transistors as biocompatible and low-cost components to be used in student and educator projects that teach principles of bioelectronic device design. These projects will be maintained in a comprehensive database to facilitate dissemination to educators and outreach coordinators, providing evidence-based methods to improve project-based learning in bioelectronics more broadly. The educational objectives of the project are to provide students and educators with hands-on opportunities to design and test simple, biocompatible bioelectronic devices. These efforts will increase exposure to engineering methods in schools and stimulate interest in bioelectronics to benefit health. There is an enormous need to develop bioelectronic components that can merge biocompatibility, ion transduction, high speed, and reliable operation in physiological environments. The objective of the project is to develop ion-driven, conformable, implantable bioelectronic devices to enable efficient interaction with neural circuits. The central hypothesis is that ion-gated transistors will effectively interact with neural signals because they can directly transduce the brain's ionic flux, and are sufficient to create the integrated circuits required for fully implantable, soft, closed-loop devices that do not require rigid encapsulation. The research involves fabrication of integrated circuits comprised of ion-gated transistors with comprehensive in vitro and modeling-based characterization of the parameters governing their operation in physiologic environments. These devices are then used to modulate neural networks in an in vivo animal model of epilepsy and acquire neurophysiologic data from human subjects. The rationale underlying this research is that realization of such devices will transform design of bioelectronic devices with the potential to enhance diagnosis and therapy for neuropsychiatric disease. 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

Investigating brain circuit development can facilitate understanding of how the brain becomes capable of performing complex cognitive functions. A key missing strategy is the ability to monitor brain activity as an organism transitions to successful performance of behaviors requiring cognitive processes. This project involves using bioelectronic devices that can interface with different brain structures as they naturally grow to monitor immature rodents as they perform behaviors in naturalistic environments. These devices will be made out of soft, organic materials that can establish an effective interface with biological tissue with minimal damage. The overall goal of this project is to identify neurophysiologic signatures of emerging cognition, using computational analysis on acquired longitudinal data to track developmental trajectories. The outcomes of this research will improve the efficiency of biomedical devices and provide key insights into principles underlying formation of brain circuits that can support cognition. This work holds promise for guiding public health initiatives that could enable appropriate monitoring of childhood development. From an educational perspective, this project aims to expand training in interdisciplinary initiatives, specifically focusing on creating partnerships between engineering and neuroscience trainees and highlighting the iterative feedback process required to transition a device from development to functional utilization. This project aims to addresses focus areas (i) neuroengineering and brain-inspired concepts and designs, and (ii) cognitive and neural processes in realistic, complex environments of NSF Integrative Strategies for Understanding Neural and Cognitive Systems. The overall objective is to use an integrated implantable neural device that enables longitudinal acquisition of neurophysiological data to investigate neural correlates of cognitive processes as animals become capable of performing advanced naturalistic behaviors. The central hypothesis is that organic electronics in combination with soft, expandable substrates can enable monitoring of local field potentials and action potentials from the developing brain without restricting spontaneous behavior. This data will identify predictors of capacity for neural computation supporting cognition in individual organisms. The rationale for this high-risk/high-payoff research is that novel monitoring approaches that merge engineering and neuroscience expertise are required to derive insight into how cognitive processes emerge in complex environments. The materials, approaches, and data generated by this work have the potential to provide notable medical and social benefits, such as: (i) soft, conformable interfaces for acquisition of neurophysiological activity from the human body; ii) approaches to safely expand neuroelectronic devices to use in pediatric age groups; and iii) accessible wearable bioelectronics for preventive medicine and lifestyle management. Generation of novel datasets from animals involved in naturalistic behavioral and social situations will benefit the neuroscience community and lead to further scientific discoveries. The educational aspects particularly emphasize improving diversity of trainees engaged in STEM research, and providing these trainees with the skills required to form, participate in, and manage projects that require strong interdisciplinary collaboration and involve individuals from disparate training backgrounds. Summative evaluation will be implemented for these efforts to evaluate overall success in integrating training about core principles of bioelectronics with neuroscientific analysis, with the goal of creating new opportunities for synergy between engineering and neuroscience fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project develops faster search algorithms for route-planning problems where multiple cost measures are used to determine the best solutions. For example, when transporting hazardous goods it is important to consider both the duration and safety of a route. Other applications include planning power-transmission lines, inspection and manipulation planning in robotics, scheduling satellites, and routing packets in computer networks. These bi- and multi-objective search algorithms work by maintaining many paths from the given start location to each location encountered during the search. This approach currently prevents them from solving realistically sized problems in real-time. This project both investigates techniques for speeding them up to realistic problem sizes and develops new benchmark instances for evaluating their performance. It is part of an international collaboration that also includes the exchange of personnel and the development of educational material. Bi-objective (and multi-objective) search algorithms allow the cost of every graph edge to be quantified by two (or more) real values. They essentially assume that one wants to find the set of all paths, called the Pareto frontier, such that each path in the set is better than all other paths from a given start vertex to a given goal vertex with respect to the sum of at least one cost component of its edges (or equally good with respect to all cost components). The researchers of this project work on finding synergies between ideas from existing bi-objective search algorithms and recent algorithmic developments in the artificial intelligence search community to develop the next generation of optimal and approximately-optimal bi-objective search algorithms. They are also working on generalizing their bi-objective search algorithms to multi-objective search algorithms and applying them in the context of transportation and robotics. 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

As the impacts of wildfires on humans and ecosystems continue to escalate, there is an increasing need for researchers and managers to understand and predict wildfire behavior. Traditional physics-based fire spread models are often limited by the high computational cost of running these models and the challenge of systematically verifying model predictions. Machine learning (ML) advances provide an opportunity to gain greater insight into wildfire processes and improve forecasts of fire activity. However, researchers currently lack large-scale, open-access data on which to train and test ML models for improved wildfire prediction. In this project, a team of researchers at the University of California Irvine and Pyregence, a consortium of researchers and software engineers advancing scientific knowledge of wildfires and building next-generation forecasting tools, will assemble a new dataset of fire observations that combines weather, topography, and fuel information with observations of sub-daily fire spread. The team will use this dataset to create new ML models that more accurately predict fire spread and the placement of fuel breaks in complex landscapes. These modeling advances will provide the scientific foundation for developing next-generation fire spread models used by wildfire managers, helping them limit fire damage to ecosystems and communities. The project team will also host a summer school on ML where diverse early career scientists from across the country will gain hands-on experience in computational methods. The team will develop a new theme for this course on fire prediction and integrate perspectives from fire managers. This project will enhance understanding, prediction, and management of wildfires by addressing the following three objectives: 1) develop a large new public dataset of fire-related environmental observations to support large-scale ML and reproducible research on wildfire spread modeling, 2) advance innovative spatiotemporal ML models for understanding and predicting wildfire spread and systematically compare these ML models to parameter-optimized physics-based models, and 3) develop novel network-based frameworks for optimizing the placement of fuel treatments that appropriately characterize uncertainty and risk. Together, the optimized physics-based and ML fire spread models will be used with graph theory to structure the optimal size, shape, and placement of fuel breaks. In a set of hypothetical scenarios, the research team will re-run model simulations of known fires in the fire database but include different levels of fuel treatment within each domain. The proposed machine learning models offer a promising avenue for improving wildfire forecasting and mitigation strategies. 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

Computer-based learning support systems commonly used in higher education provide instructors with the ability to post course materials for students to review. Even though the students in a typical class have different learning styles and interests, current learning support systems do not provide the instructor with the capability to customize course materials to meet the learning needs of individual students. The goal of this project is to develop and evaluate an AI based learning support system that will automatically build models of students interests, goals, knowledge, and experiences. The system will then use these models to customize course content posted by the instructor to match the learning needs, and interests, of each student in the class. The resulting system will produce personalized educational material at scale, potentially improving student learning, and addressing equity in large university settings. This project advances research in learning by building and evaluating a system that automatically accounts for a student’s learning style based on their individual backgrounds. This system will be developed and deployed in the context of two large undergraduate courses. The core intervention involves three main components: (1) the design and evaluation of a system that can interview students about their backgrounds; (2) transform these findings into a KG representation making its knowledge of the students available to both the instructor and the students; and (3) develop a system to personalize course content posted by the instructor. Research conducted as part of this project will use both quantitative and qualitative methods providing a more nuanced understanding of students’ experiences with both the process and the personalized materials and will provide insights into how the systems and processes might be improved. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Obstructive sleep apnea (OSA) affects a significant portion of the middle-aged population in the US and has been associated with a number of health concerns including cardiovascular disease and cognitive dys- function. The development and progression of these health consequences is believed to be related to the severity of OSA. However, current clinical indicators of OSA severity, which include an intermittent hypoxia pattern at the tissues and the apnea-hypopnea index (AHI), fail to capture the impact of the consequences associated with the disease. The intermittent hypoxia exposure pattern at the tissue-level, a qualitative indicator of OSA severity, relies on pulse oximetry for measurement, which has limitations including inaccu- racies in recording and only a generalized representation of systemic arterial oxygen hemoglobin saturation. In addition, the AHI, a quantitative measure of OSA severity, has not been shown to be strongly correlated to disease development in previous clinical studies. Accordingly, there is a large volume of OSA sleep study data that needs to be re-analyzed. Therefore, to better understand the development of OSA-related con- sequences, there is a need to assess OSA severity with a method that avoids the aforementioned clinical limitations. This can be achieved with mathematical modeling and, in this project, we propose to develop a model for a more detailed clinical representation of OSA. In Aim 1, the model will be constructed using fundamental mass transfer equations to track the transport of oxygen and carbon dioxide throughout the body. Considering the lack of patient-specific approaches in current OSA modeling literature, our model will have the ability to use respiratory and heart rate data from polysomnography studies and will be feasible for clinical application. Therefore, to address the limitations of current measures of intermittent hypoxia expo- sure, the result of Aim 1 will be a presentation of blood gas concentration profiles at the arterial and venous ends of various target tissues, which will allow for a quantification of hypoxia burden. In Aim 2, the model from Aim 1 will be used with clinical data to run a correlation analysis between predicted oxygen decreases and patient characteristics such as daytime sleepiness, a potential indicator of OSA presentation, which could provide an alternative to the AHI. The patient results will also be organized into a multi-dimensional database for scientific rigor and ease of access and analysis.Therefore, the two primary outcomes of this project will be a clinically deployable mathematical model for OSA severity assessment and a large pool of data obtained from simulated cases of OSA and a re-analysis of existing sleep studies, which will play an important role in improving patient care. Additionally, this work will be invaluable for my training in clinically- relevant computational research, which I ultimately plan to pursue in my post-graduate career.

NIH Research Projects · FY 2025 · 2024-09

Project summary/abstract Fusarium, Aspergillus and Candida are important causes of corneal blindness and vision loss in the USA and worldwide. Preliminary data using a murine models of Aspergillus keratitis show that monocyte depletion leads to exacerbated corneal disease and impaired fungal killing without affecting neutrophil recruitment to the corneal stroma. Aim 1 will examine neutrophil activation by Ca++ influx using a novel reporter mouse developed at UCI and will determine the effect of monocyte depletion on neutrophil pro-inflammatory and fungal killing activity in infected corneas by multi-photon, intravital microscopy. Preliminary data also identified distinct neutrophil clusters by single cell RNA sequencing of total CD45+ myeloid cells from infected corneas that included ICAM-1 expressing neutrophils that were localized at the site of infection in the cornea. FACS isolated ICAM-1+ neutrophils were more transcriptionally active and produced more cytokines than either ICAM-1- from WT mice or from ICAM-1-/- mice. Aim 2 will therefore examine the role of ICAM-1+ neutrophils in Fusarium, Aspergillus and Candida keratitis, and examine the role of the Hv1 proton channel that is elevated in ICAM-1+ neutrophils using Hvcn1-/- mice and novel, highly specific Hv1 inhibitors in fungal keratitis. Aim 3 will examine the processing and the role of the pore forming proteins Gasdermin D (GSDMD) and GSDME in IL-1β secretion by neutrophils and in fungal keratitis. We will also examine the effect of small molecule inhibitors of NLRP3, GSDMD and GSDME. Combined results from these proposed studies will greatly increase our understanding of the pathogenesis of fungal keratitis and identify potential targets for immune intervention.

NIH Research Projects · FY 2025 · 2024-09

Project Summary My career goal is to be an independent, productive clinical affective neuroscientist studying developmental mechanisms of psychosis-spectrum pathology. To achieve this goal, I have developed a training plan that is consistent with the mission of the NIH’s Ruth L. Kirschstein NRSA Individual Predoctoral Fellowship. My training plan has four specific goals. These training goals are to 1) learn how to preprocess and analyze functional magnetic resonance images (fMRI), 2) build an advanced repertoire of statistical knowledge and computing abilities, 3) master effective communication in scientific writing, and 4) develop professional relationships and network with cross-disciplinary researchers. Accomplishing these training goals will be important preparatory steps towards my attainment of a successful career in clinical research. To complement these training goals, I have crafted a research proposal focused on affective and clinical trajectories, and the influence of neural connectivity, in individuals at clinical high risk (CHR) for psychosis. The onset of psychosis is a complex developmental process. Despite interdisciplinary attention, the developmental trajectories of positive symptoms (i.e., delusions and hallucinations) and attenuated psychotic symptoms (APS), or less severe or distressing forms of delusions and hallucinations, are not fully understood. One reason for limitations in our scientific knowledge could be heterogeneity across developmental trajectories and timelines in individuals at-risk for psychosis. Thus, studying changes in APS severity across time in subclinical samples, such as individuals at CHR for psychosis, could help us to understand heterogeneity in clinical outcomes. Heterogeneity in clinical outcomes in individuals at CHR may be explained in-part by risk and resilience factors, such as predispositions to use certain emotion regulation (ER) strategies and related neural connectivity patterns. Defining the roles of dispositional ER and neural connectivity in changes in APS severity can elucidate how these factors may contribute to the progression of APS. Hence, the proposed multi- method, longitudinal study will clarify how the use of select ER strategies (rumination, expressive suppression, cognitive reappraisal) and connectivity in regions of interest contribute to heterogeneity in APS severity through two specific aims. Aim 1 will investigate the relations between dispositional ER strategy use and APS severity across time. Aim 2 will investigate the influence of baseline neural connectivity in regions of interest on between-persons differences in trajectories of dispositional ER strategy use and APS severity across time. The study will leverage five self-report assessments across two years and resting-state fMRI at baseline to address these aims. Overall, the proposed study will advance knowledge on mechanisms of psychotic symptom development in CHR, thereby facilitating the identification of behavioral and biological points of intervention and improving the specificity of early intervention in CHR.

NIH Research Projects · FY 2025 · 2024-09

Project Summary A mechanistic understanding of reactivity has enabled the development of nearly all modern synthetic organic chemistry, which has in turn revolutionized the discovery and production of therapeutics to treat human diseases. Yet, traditional ensemble analytical tools for investigating mechanisms, like NMR spectroscopy and mass spectrometry, measure primarily the major components in mixtures and provide averaged and non-spatially resolved information, thus missing key reaction intermediates and distributions of behaviors. Approach: Here, focusing on two challenging systems—aqueous–surfactant emulsions for sustainable organic chemistry in water and the synthesis of organometallic reagents and catalytic intermediates directly from metal powders—we now develop fluorescence lifetime imaging microscopy (FLIM) methods. These methods overcome the limitations of prior analytical techniques and, we propose, lead to exciting insights into previously poorly understood classes of organic reactions and processes. Innovation: The experiments described here are the first FLIM studies of any synthetic organic chemistry reactions or processes under preparative conditions. We innovate by using this spatially resolved fluorescence lifetime data to characterize reaction intermediates, assign fates of catalysts, understand reaction mechanisms, and create predictive reaction models. Significance: Information gained from these FLIM studies provides guiding principles for surfactant selection and medium recycling in sustainable aqueous–organic systems, efficient methods for accessing organometallic reagents, and tactics for lowering temperatures, ligand quantities, and/or catalyst amounts in carbon–carbon bond-forming cross-coupling reactions. We focus our efforts on understanding and developing areas of high significance: organozinc, organocopper, and organopalladium reagent and/or catalytic intermediate syntheses, as well as Negishi, Suzuki, and Heck cross-coupling reactions, with applications in the synthesis of drug-like molecules. Beyond uncovering guiding principles, we plan to develop next-generation chemical imaging agents and strategies, including autofluorescence methods that function in the absence of exogenous imaging agents. Instead, these methods will harness the inherent fluorescence lifetime and emission signatures of native reaction components. Once developed, these imaging tools will be primed for use by our laboratory and others for the broader study of mechanisms and processes in synthetic organic chemistry. Expertise in our diverse team uniquely encompasses fluorescence microscopy, FLIM, organic synthesis methods development, transition-metal chemistry, catalysis, surface characterization, and mechanistic studies—exactly the angles needed for the success of this ambitious, multidisciplinary proposal, as demonstrated by robust preliminary results and an impactful publication record. Together, these studies have a positive impact because they lead to efficient, sustainable routes for the construction of carbon–carbon bonds and organometallic reagents and catalysts, thus facilitating the next generation of therapeutic agents used to treat human diseases.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Biological tissues, consisting of different cells, specialize in a group of processes and functions through coordinated activities of many cells. Cell-cell communication (CCC) by ligand-receptor interaction provides a major mechanism for such coordination. Until recently, dissecting CCC required perturbations of selected genes or proteins regulated within a specific CCC link, presenting major challenges for experimental approaches. Single-cell genomics that profiles genes and their activities at individual cell level provides an unprecedented opportunity for systematic screening of all potential CCC links among cells. During the past three years, various computational tools, including ours, have enabled CCC inference and analysis using the nonspatial single-cell (sc) RNA-seq data, leading to many important biological discoveries. With the rapid growth of spatial transcriptome (ST) techniques that preserve the spatial locations of cells in addition to profiling gene expression, there is a pressing demand for new mathematical and computational methods to deal with the unique challenges associated with ST data for CCC inference. This application will focus on addressing three major unaddressed challenges for CCC inference using ST data obtained from a diverse set of current experimental techniques. The first aim is to use scRNA-seq data to a) improve the coverage of genes that are associated with ligands or receptors not well measured in ST data through novel Optimal Transport methods, and b) impute spot-resolution data using physical models to estimate gene expression level for individual cells in the spot – critical information needed for CCC inference. The second aim is to develop a comprehensive CCC inference method accounting for various CCC regulators, co-factors, regulated genes, and potential external signals by incorporating prior knowledge and additional data. The third aim is to create a host of tools by using network analysis methods and neural graph network methods for pattern recognition, systematic comparisons, and classification of spatial CCC networks inferred from ST data. The study premise is based on our novel and extensive preliminary results in CCC inference. The proposed studies are significant because they will create the first comprehensive integrated tool that can impute ST data, infer CCC, and classify CCC networks in a systematic way, and success of the studies will establish a new cornerstone for ST data analysis, leading to novel spatial biological insights for tissues. The proposed studies are innovative because the proposed tools will have novel functionalities that use the ST data to derive crucial biological information which is currently impossible to obtain. They will also result in several novel mathematical and computational methods in the areas of multiscale modeling, optimal transport, and deep learning that will have broad applications in single-cell and spatial genomics data analysis and beyond.

NIH Research Projects · FY 2025 · 2024-09

PROJECT ABSTRACT Proteome integrity is maintained by a complex network that regulates protein synthesis, folding, transport, and degradation. Lysosomes are the catabolic center of a cell and central to maintaining proteome homeostasis by preventing, detecting, and removing abnormal proteins. Major knowledge gaps remain in the regulation, structural components, and substrate specificities of lysosomal substrates. Intracellular proteolysis through the ubiquitin-proteasome system has been the most well-characterized eukaryotic proteolytic pathway as the protein targeting by ubiquitin and the amino acid sequences recognized by E3 ubiquitin ligases are well-defined. In contrast, a major obstacle in understanding lysosomal processes is the incomplete knowledge of protein modifications that enable lysosomal trafficking mechanisms. Our work identified that arginine methylation leads to protein delivery into lysosomes for degradation. We showed that rapid methyl-driven delivery was essential for removing enzymes from the cytosol to promote growth and proliferation. The proposed studies examine the central hypothesis that methyl-driven lysosomal proteolysis is a widespread process that enables natural protein turnover during homeostasis and rapid protein remodeling in response to external stimuli. We address this hypothesis in three areas of research. Area 1 defines novel protein substrates and the peptide motifs required for lysosomal delivery. Area 2 determines the functional impact of rapid methyl-driven delivery as a control mechanism for fundamental cellular metabolic pathways. Area 3 leverages naturally-occurring lysosomal protein signals to develop tools for researchers to rapidly decrease protein levels in endogenous living systems. We test the conceptually novel model that selective lysosomal proteolysis is central for regulating cytosolic, short-lived proteins that were previously thought to be degraded in proteasomes. We anticipate use of our publicly available database of novel methyl-degraded lysosomal proteins will provide an essential resource for the fields studying protein control. We develop technically innovative tools to gain new mechanistic insight into lysosomal biology for the present studies while also providing a tool for the broader research community that significantly improves current strategies for endogenous protein depletion. 11

NIH Research Projects · FY 2026 · 2024-09

Abstract One of the most recent and significant public health problems deals with addressing vaccine hesitancy and mis/disinformation. As new types of vaccines become effective and available, and bundled together, it becomes increasingly important that researchers and health departments learn how to best communicate to the public the correct science behind vaccines. Black/African American and Latinx populations might be especially impacted by misinformation due to health inequity and low health literacy. However, despite extensive exposure to vaccine mis- and disinformation, many Blacks and Latinx are resilient to misinformation and still choose to get vaccinated. This application takes a unique approach in leveraging that positive outcome by identifying the predictors of Black and Latinx individuals who are frequently exposed to misinformation, yet demonstrate broad vaccine acceptance for different vaccine types. Many factors play a role in vaccine acceptance/hesitancy, including social media and social networks, as well as traditional multi-level factors such as mental health, political ideology, stigma, access to health services, trust in the healthcare system, and educational opportunities. Our team has conducted extensive research on the factors influencing attitudes and behaviors among Black and Latinx populations, for COVID-19 vaccine uptake, as well as ways to use digital data and tools to gain insights and intervene to improve them. In this study, we seek to use similar artificial intelligence methods on social media, social network, and other multi-level data to identify factors influencing vaccine acceptance among Black and Latinx populations. We seek to enroll 500 Black and Latinx individuals who are Twitter (X) followers of known vaccine-hesitant influencers. We will collect baseline, 3-, 6-, and 12-month follow-up data on participants’ vaccine acceptance; misinformation exposure; perceptions about vaccines, including willingness to receive future vaccines or enroll in a vaccine clinical trial; digital contextual data (e.g., social media content; social network ties; and other multi- level factors associated with vaccine knowledge and acceptance (e.g., political ideology, medical distrust, structural factors). We will study the factors affecting acceptance of vaccines. We will also develop a tool to visualize the data to inform researchers about how to add these new data/approaches to surveillance efforts. Specifically, we seek to 1) Identify the relationship between social media data and vaccine acceptance among Black and Latinx followers of influencers spreading vaccine-hesitant information, 2) Examine the influence of social network factors on vaccine acceptance, and 3) Develop a visualization tool to graph and map contextual data (e.g., social media content and geographic and network location of social network ties).

NIH Research Projects · FY 2024 · 2024-09

Mechanistic and Translational Investigations of HSPB8-associated dominant rimmed vacuolar myopathy. Autosomal dominant mutations in the heat shock protein family B member 8 (HSPB8) have been associated with (i) distal hereditary motor neuropathy; (ii) axonal Charcot-Marie-Tooth disease; and (iii) most recently autosomal dominant rimmed vacuolar myopathy (RVM). Patients with HSPB8 RVM primarily develop a distal myopathy in their 30s-40s, with proximal limb girdle weakness in their 40s-50s, and eventually become wheelchair-bound. Muscle biopsy shows fatty replacement, fibrosis, and rimmed vacuoles. HSPB8 is involved in chaperone-assisted selective autophagy (CASA), and in conjunction with BAG3, recognizes and promotes the autophagy-mediated removal of misfolded proteins. Our long-term goal is to develop a potent therapy to stop/reduce the progression of HSPB8-associated dominant-rimmed vacuolar myopathy. Major gaps: The mechanisms through which mutant HSPB8 results in aggregation, and the availability of treatments that preserve or restore proteostasis by enhancing autophagy for HSPB8-rimmed vacuolar myopathies. Preliminary results: To investigate, in vitro and in vivo, the molecular mechanism of HSPB8-associated myopathy, and to assess the potential of new treatments, we generated: (i) patient myoblasts derived from induced pluripotent cell lines (iPSCs); and (ii) a clinically relevant C57BL/6NJ-Hspb8 knock-in mouse model with the c.515dupC variant using CRISPR/Cas9 technology which manifests myopathic weakness starting at 6 months. We demonstrated that the HSPB8 fs mutant is associated with: (A) increased TDP-43 and autophagy markers in the patient muscle, fibroblasts, and myoblasts. (B) The Hspb8 knock-in mouse muscle histology revealed central nuclei and muscle degeneration with fibrous and adipose replacement; and immunohistochemical and biochemical studies revealed aggregates, increased TDP-43, and autophagy pathology resembling human pathology. Our group performed two high-throughput drug screenings and identified colchicine and trehalose to reduce the aggregates, additionally, trehalose benefitted a mouse model of neurodegeneration. Hypothesis: Based on both in vitro and in vivo studies, our central hypothesis is that mutated HSPB8 exerts a toxic gain of function, and leads to HSPB8 mutant aggregation. Our current results support the hypothesis that compounds that stimulate autophagy favor the removal of protein aggregates related to HSPB8 fs mutations. Specific Aims: To test our hypothesis, we propose three specific aims: Aim 1: To investigate the molecular mechanism of pathogenesis of HSPB8- associated myopathy in vitro patient iPSC-derived myoblasts and the Hspb8515dupC mouse model. Aim 2: To reverse the mutant HSPB8 pathology in vitro in patient myoblasts with an autophagy inducer trehalose. Aim 3: To stop or reduce the HSPB8-associated myopathy by upregulating autophagy in the mutant mouse model. Based on our previous studies, we will use trehalose in vivo in the Hspb8515dupC mouse model. Successful completion of the present mechanistic and translational study will pave the way for the treatment of HSPB8- associated dominant-rimmed vacuolar myopathy, and will also benefit other related disorders.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Conventional vertical sleeve gastrectomy (cVSG) accounts for approximately 60% of all bariatric surgery procedures. Its popularity is attributable to procedural simplicity, low complications, durable weight loss and significant improvement of obesity-related comorbidities. However, numerous studies have found that the cVSG is associated with new-onset gastroesophageal reflux disease (GERD) and persistence or worsening of pre-existing GERD. Chronic GERD is associated with increased risk for Barrett’s esophagus and esophageal cancer. Hence, it is imperative to develop strategies to minimize GERD associated with the cVSG. The pathophysiology cVSG-associated GERD is attributable to its surgical technique. First, the patients evaluated for bariatric surgery are often found to have a defective antireflux barrier (ARB) and hiatal hernia, which increase the risk of GERD. Second, the cVSG removes the entire gastric fundus resulting in 1) higher intragastric pressure which is conducive to reflux, 2) disrupts gastric sling fibers leading to altered lower esophageal sphincter function, and 3) disrupts the gastric cardia leading to loss of the gastroesophageal flap valve (GEFV). The current project will test a modified VSG (mVSG) which will preserve the gastric sling fibers, anchor the cardia to the distal esophagus, and recreate the GEFV. INNOVATE-VSG is a randomized single- blind clinical trial with a 12-month follow-up. A total of 40 subjects with BMI 30-50 kg/m2 and GERD, meeting eligibility for bariatric surgery, will be randomly assigned in 1:1 ratio to the conventional VSG (cVSG) or the mVSG at two academic sites. Aim 1. to determine whether mVSG, in comparison to cVSG, will be associated with lower acid exposure time (AET, measured by the Bravo pH test) at 12 months. Aim 2. To elucidate the mechanistic basis for Aim 1, we will perform following tests, before and at 12 months post-surgery: 1) High resolution esophageal manometry (HREM) to determine the LES and intragastric pressure. We expect higher LES pressure and lower intragastric pressure in patients with mVSG vs cVSG. 2) EndoFLIP testing to examine changes in compliance of the LES. We expect lower LES compliance in patients with mVSG vs cVSG. 3) Measure the length of the gastroesophageal flap valve (GEFV) on the retroflex view during endoscopic exam. We expect that GEFV will be present after mVSG vs absent after cVSG. Aim 3. Examine the impact of GERD on quality of life (QoL) with two validated rating scales – GERD-HRQL and SF-36. We hypothesize mVSG patients will have superior QoL compared to cVSG patients at 12 months. Additionally, we examine for the presence of sling fibers in the resected stomach specimens. We expect to see sling fibers to be present in the cVSG specimens but not in the mVSG specimens. We believe that the mVSG will lead to improvement of pre- existing GERD along with improved quality of life for patients with obesity. The study findings have the potential to transform the way VSG will be performed in future. This pilot study will generate more than adequate data to help design a larger multi-site definitive clinical trial.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Adolescent mental health has worsened over the last decade and increased time spent on social media is often cited as a contributing factor. Parents, policymakers, and educators are being told to curb social media use. However, whether restricting or changing exposure to social media use will impact mental health is largely unknown. This research builds on our previous prospective, longitudinal, and intensive study of a population representative sample of young adolescents. Mental health symptoms and social media use will be captured at the daily, weekly, and yearly levels via ecological momentary assessment (EMA), passive sensing, wearable technologies, and a new youth co-created open application program interface (API) toolkit. Social media engagement will be captured via real time assessments alongside the extraction of social media histories via a youth co-created API toolkit. A large representative sample of 2500 11- to 15-year-olds will be followed over four years, with a subset of 750 adolescents followed intensively via EMA to experimentally test whether social media restriction versus scaffolding (a) modifies social media engagement and (b) impacts mental health symptoms in the moment, across days, and over years. Early adolescence is a key period for testing bi-directional associations between social media use and mental health given the onset of common and costly mental disorders like anxiety and depression, evidence of heightened response to interventions, and the fact that young people begin to navigate online spaces independently at this time. The study is positioned to impact on science, practice, and policy by (1) advancing discovery related to differential bi-directional influences between social media and mental health, (2) testing whether experimental modifications to social media-engagement impact same day and future mental health symptoms, (3) identifying subgroups of adolescents for which bi-directional linkages and/or intervention impacts may be amplified, and (4) creating a novel resource for the field that will allow adolescents to access, control, and share their digital trace data to advance research, interventions, and policy.

NSF Awards · FY 2024 · 2024-09

Most of the stars that make up our home galaxy, the Milky Way, are arranged in the shape of a rotating disk. Many other galaxies in the universe today are also shaped like disks. However, when astronomers look back in time with large telescopes, they see that the fraction of galaxies that are disks goes down. The very first galaxies we have seen in the primordial universe are not disks. The investigators will use simulations that model the formation of galaxies, from the earliest times to today. They will explore when and how galaxies begin to take the shape of disks to understand the reasons why. The investigators will also compare their simulations to observations of stars in the Milky Way and distant galaxies to help us understand how disk galaxies came to be. The research program will support the education and training of PhD students, increasing their understanding of physics, data science, scientific visualization, and programming. The investigators will also mentor a diverse population of undergraduate students pursuing careers in STEM fields. The investigators will study the physics that underpins galaxy disk formation using a large set of zoom cosmological simulations with Feedback In Realistic Environments (FIRE) galaxy formation physics. They will study the connection and causal correlations between thin and thick disks: Do thin disks emerge first in cosmic history, with thick disks constituting a descendant, heated population? Or, are thin disks a relatively recent phenomenon, with thick disks a natural outcome of high-redshift galaxy formation? The investigators will explore the degree to which galaxy potentials becoming centrally concentrated over time may help enable galaxies to “spin up,” and track how the disordered interstellar medium (ISM) of galaxies at early times transitions to more ordered, thin-disk-dominated populations at late times. A crucial component of this work will be to understand the astrophysics that regulates galactic disk formation as a means of understanding what may be missing in models that do not produce disks with the correct frequency and character to match observations. The simulations will allow the investigators to explore connections between the baryon cycle and morphological structure. The investigators will inform deep-field imaging and velocity-field studies of galaxy disk “settling” as well as local studies with surveys including MaNGA, the Local Volume Mapper, GALAH, and Gaia. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Adolescent cannabis use has been linked to negative long-term outcomes in humans, including increased rates of experimentation with “harder” drugs like opioids later in life. Yet these associations cannot be tested causally in humans, so animal studies showing that adolescent THC exposure (adoTHC) indeed causes developmental disruption of reward and cognition circuits are notable and potentially alarming. Here we employ a well- characterized, translationally-oriented adoTHC exposure model in rats to examine how opioid-addiction relevant behaviors are altered, and to uncover novel brain mechanisms by which these behavioral changes may manifest. Notably, microglia, the resident immune cells of the brain, are increasingly being recognized for their key roles in neurodevelopment, including in adolescence. In pursuit of a mechanistic understanding that can inform novel interventions or strategies for opioid addiction, my project examines the effects of adoTHC exposure on microglia, and how these changes may impact opioid drug seeking behavior. I employ translationally relevant behavioral models of opioid addiction in rats to fully characterize the adoTHC- induced pro-opioid phenotype, and employ both hypothesis-generating RNA sequencing approach, as well as an experimental microglial “resetting” approach to test the causal relevance of microglia in the observed pro- opioid behavioral phenomena seen in males and females. During the F99 phase of this award, I will replicate and extend my findings, and obtain training and new data employing RNA sequencing of FACS-isolated microglia in prefrontal cortex. The proposed training will facilitate my transition to a competitive postdoctoral position focused on in vivo imaging and monitoring of microglia, building upon my current expertise in addiction behavioral models. My sponsors will be instrumental in helping me build skills in experimental design, guided analyses support, scientific communication, and grantsmanship. They will also guide me in finding a postdoctoral training environment focused in neuroimmunology aligning with my long-term research and career goals. Altogether, the F99/K00 award serves as an invaluable asset in propelling me on a trajectory towards becoming a tenure-track addiction behavioral neuroscientist.

NIH Research Projects · FY 2025 · 2024-09

The overall goal of our research program is to gain a comprehensive understanding of the molecular mechanisms underlying the regulation of protein phosphatase 2A (PP2A) family phosphatases, including PP2A, PP4, and PP6, in response to environmental signals. Reversible protein phosphorylation is a major regulatory mechanism by which cells respond to their environment and regulate cellular behavior. Although much is known about the regulation of protein kinases in specific signaling pathways, the regulation of protein phosphatases in response to environmental signals to counteract kinase functions is not well-established. Unlike kinases, serine/threonine phosphatases are promiscuously active, and their specificity is largely governed by associated proteins, which makes their analysis exceedingly difficult. Our recent efforts to address this major knowledge gap have led to significant conceptual advancements in understanding how PP2A family phosphatases respond to the extracellular environment. We have developed an innovative view of serine-threonine phosphatase complexes, proposing that these complexes are unstable and constantly regulated by disassembly and reassembly. Additionally, we found that specific regulatory subunits are induced in response to specific stimuli to determine PP2A substrate specificity and influence physiological functions. A key discovery in our lab was the identification of a regulatory subunit of PP6, SAPS3, which is essential for the dephosphorylation of AMPK in response to metabolic environmental signals. This discovery has enabled us to further study the molecular mechanisms underlying SAPS3 complex assembly and the functional implications of phosphatase complexes in cellular operations. Over the next five years, our research will continue to elucidate the molecular mechanisms by which the SAPS3-containing PP6 phosphatase complex assembles in response to extracellular signals and determine its cellular and systemic functions in response to environmental stimuli using cellular systems and animal models. For the long-term pursuit of studying PP2A family phosphatases, we will also identify other novel phosphatase complexes involved in specific signaling pathways. The accomplishment of these studies will provide transformative insights into the molecular mechanisms by which cells respond to their environment and will lay an essential framework for the development of novel targeted therapies to restore cellular homeostasis.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Recent research in the study of brain functions is highlighting the important role of glial cells in several human neurological and psychiatric disorders. The dopaminergic control of brain functions is classically assigned to the effect of dopamine on neurons. However, D2R expression has also been reported in astrocytes, questioning what their contribution in the dopamine-dependent control of brain functions is. Based on preliminary evidence showing an increase of astrocytes and microglia in the PFC of mice with an altered control over dopamine synthesis and release, we hypothesize that D2R signaling in astrocytes influences the activity of neighboring cells and induces microglia proliferation. This mechanism could play a role in the control of PFC dependent behavior, gene expression in astrocytes and microglia, as well as on the metabolites that they produce. Thus, we propose to perform experiments to determine the impact of D2R signaling in astrocyte on PFC functions. We will use viral vectors to knockdown D2R specifically in astrocytes in mice with either a normal control of dopamine synthesis and release or in mutants where these functions have been altered. This project is timely due to the increased involvement of glia in brain disorders and promises to elucidate mechanisms previously unexplored.

NIH Research Projects · FY 2025 · 2024-09

Our goal is to build Sickle Cell Disease Pain Analgesia and Integrative Network (SCDPAIN) as an innovative, dynamic and interactive platform that will advance the NCCIH’s bold mission for research on mechanism based complementary and integrative whole health (CIWH) approaches for pain. Pain is one of the major comorbidities of sickle cell disease (SCD) leading to poor quality of life, frequent opioid use and reduced survival. Compared to most other painful conditions, pain in SCD is unique because of the unpredictable and recurrent episodes of acute pain due to vaso-occlusive crises, in addition to chronic pain which continuously affects the majority of individuals. Pain in SCD can start during infancy and continue throughout life. Guided by an unmet need to address the morbidity associated with pain in SCD, our network is committed to profoundly impacting the science of sickle cell pain through leading expertise in pain, SCD pathobiology, end-organ damage and integrative interventions, assisted by cutting-edge technological advancement through 3 specific aims: [#1] “Science without borders.” To develop a collaborative network of multidisciplinary scientists, clinicians, analysts, and community partners to advance CIWH approaches and pain mechanisms; [#2] “Promoting the future” for innovative, technically advanced, multidimensional, multidisciplinary and holistic team science; and [#3] “Hub to health,” multimodal dissemination to maximize access to SCDPAIN. To achieve these goals, we will establish 5 focused working groups on, priority areas, pilot funding, sabbatical review, training workshops and networking for developing multidisciplinary approaches. We propose 3 critical priority areas, [i] to determine the central mechanisms involved in the persistence of pain and opioid use in SCD, [ii] study “interoception of sickle pain perception” and/or improve SCD pain responses in the brain and other organs within animals and humans, and [iii] examine chronic and acute pain and treatment side-effects requiring CIWH approaches. Finally, we will maximize access to SCDPAIN via multimodal dissemination efforts to propel scientific advancements in SCD. The MPI team has extensive experience in propelling SCD pain research forward, bringing multidisciplinary teams together, and mentoring the next generation of CIWH & pain scientists. In addition, a team of 9 collaborators bring extensive, diverse and cutting-edge technology which will lead research into a new era of mechanism-based translational understanding of pain. Their passion for successful mentoring and community education is poised to provide a continuum of success to the network. The impact of SCDPAIN will be monumental in: [1] Building multidisciplinary research capacity to fulfill critical unmet needs of CIWH in the model of SCD pain; [2] Incentivizing novel initiatives through Pilot funds leading to R-series, HEAL grants, etc [3] Catalyzing the future generation of scientists to accelerate SCD pain research. SCDPAIN will lead to a transformative framework and excite experts and novice researchers for mechanism-based, whole-person understanding of pain and advance the science underlying CIWH.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The national comprehensive societal cost from alcohol-impaired crashes is estimated at over $348 billion, with $296 billion attributable to ≥ .08 BAC cashes. In the last 10 years, the national alcohol impaired driving crash fatality rate has increased by 23%. Young drivers are the most vulnerable group and at highest risk of being seriously injured and/or killed in an alcohol impaired driving crash. Much of this vulnerability exists in the context of young drivers navigating life at a time when individual freedom and mobility via driving is high as is exposure and ease of access to alcohol and drugs. Prior to turning age 21, the Minimum Legal Drinking Age and Zero- Tolerance laws (making it unlawful for those <21y/o who drive to have a BAC ≥.02) are in effect for youth and young drivers with the intent of reducing harm and death due to negative consequences of alcohol use. However, when a young driver who drinks turns 21y/o, they are no longer subject to key effective alcohol prevention/public safety policies. Instead, when getting behind the wheel, they are subject to a BAC per se policy with a threshold of ≥.08 g/dl. Unfortunately, this is a higher BAC limit with a well-established greater risk of serious injury and fatal crash (i.e., BAC per se of ≥.02 before age 21 vs. a BAC per se of ≥ .08 upon turning age 21y/o). Given the current state of an increasing national alcohol crash fatality rate and high vulnerability of young adult drivers, there is a critical need and salient opportunity to innovate policy focused in the young-impaired driver domain. An early reduced risk exposure approach at the time young drivers turn 21y/o and can legally drink could yield measurable harm and fatality reduction effects. Except for the state of Utah, where the BAC per se policy is ≥.05, all US states are at a BAC per se of ≥.08. International studies prove reductions in BAC limits to .05 significantly reduce alcohol-impaired traffic injuries and deaths. Using epidemiologic and system dynamics methods, this study will provide a novel robust examination and modeling of a conceptual national age-based Graduated-BAC per se policy (Grad-BAC) so that at the moment a young driver turns 21y/o through age 24y/o, the BAC per se would be ≥.05. Thereafter, at age 25y/o, the BAC per se would be ≥.08 (except for Utah that is already at a BAC per se of ≥.05). This study will first examine longitudinal pre/post-age 21y/o driving after drinking behavior as well as state-/national-level crash fatalities specifically among drivers 21-24y/o by BAC levels. Further, changes in fatal crash rates among drivers 21-24y/o pre- vs. post-BAC per se policy of ≥.05 in Utah and in neighboring states will be evaluated. Next, national public survey and focus groups of state policy leaders will be conducted to assess support for or against the Grad-BAC per se policy vs. a national BAC per se of ≥.05 for all drivers ≥21y/o. Finally, the construction of a robust and comprehensive simulation system dynamics model will facilitate the examination of potential effects of the Grad-BAC policy among 21-24y/o that drive after drinking. This study will effectively leverage historical and recent landmark findings in impaired driving research and policy. Study findings will prove to be highly novel and pivotal in informing new policy and prevention efforts.