University Of Southern California

NSF Awards · FY 2024 · 2024-07

WebAssembly is a low-level, statically typed programming language aiming to serve as a universal compilation target for the Web. It boasts features of rapid compilation and execution, portability across languages, hardware, and platforms, and formal guarantees of type and memory safety. WebAssembly is supported on all four major browsers (i.e., Chrome, Firefox, Safari, and Edge) and compiles from several programming languages, including C, C++, C#, Rust, and Go. Despite these advantages, the direct application of WebAssembly to real-world challenges is hindered by the absence of robust static program analysis techniques within its ecosystem. This project will address this limitation by developing a generic static analysis framework for WebAssembly, facilitating the creation of a wide range of static program analyses. Successful completion of the proposed activities will be a substantial advancement in enhancing web application reliability, thereby benefiting all stakeholders within the web ecosystem. This includes improved productivity for web developers and reduced software release delays, critical assurance for web users by mitigating security problems and enhancing user experiences, and avenues for tooling developers to devise practical testing techniques specific to the WebAssembly environment. Education materials related to the static analysis of WebAssembly applications will be developed and integrated into the computer science curriculum. The core focus of this project is the design, modeling, and implementation of a general-purpose static analysis framework named WAF (WebAssembly Analysis Framework), which enables a broad spectrum of static analyses for WebAssembly. This framework will be underpinned by three key intermediate representations (IRs) – WAF-Low, WAF-Mid, and WAF-High – each modeling the WebAssembly module’s semantics at distinct levels. WAF-Low streamlines the transition to higher-level IRs by reducing the number of instruction types. WAF-Mid abstracts the WebAssembly stack machine into three-address code, simplifying program analysis. WAF-High presents IR units in a concise syntax akin to high-level languages like C or JavaScript by removing unnecessary intermediate statements. Meanwhile, precise code transformation techniques will be designed to facilitate the gradual elevation between IR levels and subsequent lowering to output usable WebAssembly modules. Furthermore, the project entails the development of a myriad of applications harnessing the framework’s capabilities. These encompass research activity spanning WebAssembly program optimizations, binary decompilation, cross-language program analyses between WebAssembly and JavaScript, and traditional compiler analyses tailored to the WebAssembly paradigm. 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 · 2024-07

PROJECT SUMMARY According to NIDCR, temporomandibular joint (TMJ) disorders affect up to 12% of the population and are a major priority for further research. The high incidence in children is unusual for a chronic pain condition, suggesting a significant developmental origin. TMJ development involves precise patterning of the mandibular bony processes and diverse connective tissues including the fibrous disc, stabilizing ligaments, and tendons that connect to jaw muscles. Skeletal and soft connective tissue structures of the TMJ derive from cranial neural crest cells, yet how cell fate decisions are balanced to generate the correct tissues with spatial precision remains unresolved. In a previously funded R21, PIs Gage Crump and Amy Merrill combined single-cell omics with zebrafish and mouse genetics to identify a new function of the nuclear receptor Nr5a2 in promoting connective tissue at the expense of skeletal fates at the developing TMJ. However, the identity of the stem/progenitor cells for TMJ connective tissues, and how they differ from skeletal stem cells, was unknown. Here, by analyzing new single-cell data of the developing mouse TMJ, we have identified the R-spondin receptors LGRs as markers of jaw connective tissue stem cells. Compared to roles in epithelial stem cell biology, potential roles of LGRs in mesenchymal tissues have been understudied. We propose that LGRs function to maintain the stem pool for sustained connective tissue generation at the TMJ by potentiating locally high Wnt signaling. In Aim 1, we will perform lineage tracing to test that Lgr4 and Lgr5 in mouse, and Lgr4 and Lgr6 in zebrafish, mark connective tissue stem cells at both embryonic and postnatal stages. We will also use targeted ablation to test the requirement of Lgr5+ cells for TMJ connective tissue development and postnatal growth. In Aim 2, we will perform real-time in vivo imaging in developing zebrafish to test that connective tissue progenitors transition from a high to low Wnt state as they differentiate into tendons and ligaments. We will then use conditional mouse genetics to test our model that sustaining Wnt signaling in Lgr5+ cells disrupts differentiation by locking cells in a stem cell state, and reciprocally blocking canonical Wnt signaling in Lgr5+ cells depletes stem cells and causes a failure to sustain connective tissue development. In Aim 3, we use mouse Lgr4 and Lgr5 floxed alleles provided by our collaborators at Novartis, and newly generated zebrafish lgr4 and lgr6 CRISPR mutants, to test that LGRs are redundantly required in Lgr5+ cells for high Wnt signaling and maintenance of connective tissue progenitors. We then test whether restoring Wnt signaling levels through genetic or pharmacological means can rescue connective tissue defects in LGR mutants. Strengths of the proposal include the PI team with complementary expertise in zebrafish and mouse models, unique single-cell datasets of the fish jaw and mouse TMJ, and sophisticated in vivo imaging and genetic tools in both species to enable mechanistic understanding of jaw connective tissue differentiation. Our work will reveal how alterations to Wnt and stem cell function impact the balance of skeletal and connective tissue formation, potentially predisposing to TMJ disorders.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT Latinas have the highest cervical cancer incidence rates compared to other racial and ethnic groups, and disparities are greater among low-income and non-US-born Latinas. Routine cervical cancer screening and timely follow-up of abnormal results are key to reducing cervical cancer mortality, especially among low-income and non-US-born Latina patients in safety-net healthcare settings. Higher social capital (resources rooted in social networks), which can be measured at the individual and community level, is positively associated with cervical cancer screening among Latinas, but how this association operates by nativity status and how clinic- based social capital interventions can be implemented in safety-net settings to address cervical cancer disparities among this population has not been explored. The goal of this mixed-methods study is to investigate multilevel ways in which social capital supports cervical cancer screening rates and follow-up and identify important considerations for a clinic-based social capital intervention to improve cervical cancer prevention practices among Latinas. The proposed study will explore two aims in the mentored phase: (1) test the association between social capital (measured at the individual and population level) and healthcare treatment delay; and (2) investigate the processes in which social capital influences cervical cancer screening and follow- up among Latina patients and identify strategies for intervention design and implementation. In the R00 phase, I will: (1) use Delphi method in a clinic-community engaged process to establish consensus on important considerations of a clinic-based social capital intervention for Latina patients; and (2) develop and pilot test the feasibility and acceptability of a social capital intervention in a safety-net clinic. The unique strengths of this proposal include a) its focus on understanding of multilevel social capital and the established association using national All of Us data; b) a unique focus is studying differences between U.S.- and non-US-born Latinas; and c) using a multisectoral approach to better understand community (i.e., stakeholders) and clinic (i.e., patients, providers, clinic staff) perspectives to inform an intervention aimed at increasing social capital that support cervical cancer screening and follow-up. Finally, I will benefit from a strong team of mentors and scientific advisory committee with specialties in health services research, health disparities, Latino health, multilevel research design, implementation science, community research, cancer epidemiology, biostatistics, and social capital. The proposed study is innovative in its plan to understand how social capital can improve cervical cancer screening and follow-up among U.S.-born and non-US-born Latinas to reduce cervical cancer disparities. Results of this study will provide preliminary data for a R01 application using a randomized controlled trial to test the selected intervention among Latina patients in safety-net settings.

NIH Research Projects · FY 2024 · 2024-07

PROJECT SUMMARY Approximately 42% of new cancer cases in the U.S. are viewed as potentially avoidable, including 15% caused by excess body weight and physical inactivity. However, most U.S. adults are physically inactive. Physical activity intervention research has focused primarily on modifying cognitions about what a rationally-thinking person “should” do to improve their health, resulting in modest effects on long-term behavior change. A factor that has been largely overlooked in intervention research is that while engaging in physical activity may be pleasant for some, it can be an extremely unpleasant experience for others. How someone feels during behavior can trigger associative processes including implicit attitudes (e.g., favorable/unfavorable) towards the target behavior that operate quickly and involuntarily outside of cognitive control. A recent meta-analysis showed a significant positive association between positive implicit attitudes towards physical activity and physical activity engagement. Our data show implicit attitudes towards physical activity differ from day to day and across situations. A variety of experimental strategies such as guided imagery and visualization have shown promise in changing implicit attitudes towards physical activity. However, rarely have these intervention strategies been delivered in real- world settings on a regular basis to address variations in implicit attitudes. We will examine whether implicit attitudes towards physical activity can be experimentally manipulated by “affect-based” intervention strategies delivered daily through interactive mobile technology and mediate intervention effects on physical activity behavior. The proposed study will add innovative real-time measures of implicit attitudes during daily life among adults (18+ years) at elevated cancer risk due to inactivity and overweight/obesity participating in an ongoing trial (N=120). An “affect” condition provides daily goals related to enjoyment and feeling good during physical activity. In contrast, an “intensity” comparison condition provides daily heart rate goals. Two daily enhancements to the affect-based condition are evaluated: (1) tailored activity type and context recommendations to satisfy personally important psychological needs (TYPE/CONTEXT) and (2) savoring practices to increase the saliency of positive emotions during physical activity (SAVOR). On each day that they plan to engage in physical activity, implicit attitudes will be assessed by a morning mobile Implicit Associations Test (IAT) on their smartphone capturing reaction times to categorize physical activity and sedentary words as good or bad. Physical activity outcomes are measured using accelerometry. Specific aims are (1) determine whether implicit attitudes mediate effects of treatments on physical activity and (2) explore cross-situation moderating effects such as situational constraints (e.g., incidental stress, pain, fatigue). This study will elucidate how, why, and when intervention strategies can influence implicit attitudes and lead to successful physical activity change. Insights about effects on the mediators can be back-translated into refinements in the treatment strategies themselves, leading to more effective and sustainable interventions.

NIH Research Projects · FY 2026 · 2024-07

ABSTRACT Latinas have the highest cervical cancer incidence rates compared to other racial and ethnic groups, and disparities are greater among low-income and non-US-born Latinas. Routine cervical cancer screening and timely follow-up of abnormal results are key to reducing cervical cancer mortality, especially among low-income and non-US-born Latina patients in safety-net healthcare settings. Higher social capital (resources rooted in social networks), which can be measured at the individual and community level, is positively associated with cervical cancer screening among Latinas, but how this association operates by nativity status and how clinic- based social capital interventions can be implemented in safety-net settings to address cervical cancer disparities among this population has not been explored. The goal of this mixed-methods study is to investigate multilevel ways in which social capital supports cervical cancer screening rates and follow-up and identify important considerations for a clinic-based social capital intervention to improve cervical cancer prevention practices among Latinas. The proposed study will explore two aims in the mentored phase: (1) test the association between social capital (measured at the individual and population level) and healthcare treatment delay; and (2) investigate the processes in which social capital influences cervical cancer screening and follow- up among Latina patients and identify strategies for intervention design and implementation. In the R00 phase, I will: (1) use Delphi method in a clinic-community engaged process to establish consensus on important considerations of a clinic-based social capital intervention for Latina patients; and (2) develop and pilot test the feasibility and acceptability of a social capital intervention in a safety-net clinic. The unique strengths of this proposal include a) its focus on understanding of multilevel social capital and the established association using national All of Us data; b) a unique focus is studying differences between U.S.- and non-US-born Latinas; and c) using a multisectoral approach to better understand community (i.e., stakeholders) and clinic (i.e., patients, providers, clinic staff) perspectives to inform an intervention aimed at increasing social capital that support cervical cancer screening and follow-up. Finally, I will benefit from a strong team of mentors and scientific advisory committee with specialties in health services research, health disparities, Latino health, multilevel research design, implementation science, community research, cancer epidemiology, biostatistics, and social capital. The proposed study is innovative in its plan to understand how social capital can improve cervical cancer screening and follow-up among U.S.-born and non-US-born Latinas to reduce cervical cancer disparities. Results of this study will provide preliminary data for a R01 application using a randomized controlled trial to test the selected intervention among Latina patients in safety-net settings.

NSF Awards · FY 2024 · 2024-07

This research seeks to understand the lifecycle of hate language in online communities: how people's use of hateful language evolves over time and across groups, and whether or when members leave such groups. By analyzing large datasets from various social media platforms using advanced artificial intelligence (AI) tools, the project team will identify the key factors that influence the evolution of participation in groups that tolerate hateful language. This research will bridge social science and AI, offering new insights into online polarization. The team will also develop tools that others can use to replicate and extend the analysis, and that can also be used in interdisciplinary courses and summer schools aimed at training future researchers in this area. This project includes developing predictive models to detect initial involvement in a hate language group that leads to participation in others, and evaluating the impact of inter- and intra-group conflicts on user behavior. The analytical approach involves using explainable AI techniques based on Large Language Models to interpret model predictions and causal inference methods to understand the influence of external events on the consumption and production of hateful language in online groups. The developed tools will enable the research team -- and other researchers -- to analyze the language and behavior of users within groups that tolerate hateful language, identify key features predicting membership in these groups, and understand the causal mechanisms behind those processes. 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 · 2024-07

Patient-Specific Factors of Recovery in Rotator Cuff Tendinopathy PROJECT SUMMARY Rotator cuff tendinopathy is a frequent cause of shoulder pain. A known effective treatment is resistance exercise, which is the current standard of care. However, the outcomes of resistance exercise are highly variable, with 40-50% developing chronic or recurrent pain. Resistance exercise should be designed to stimulate tendon healing and individually tailored to change the biomarker of tendinopathy – tendon structure. The lack of knowledge as to how the tendon structure responds to resistance exercise, how this relates to patient-reported pain and disability, and what other factors impact the tendon response is limiting the delivery of patient-specific treatment approaches and outcomes of care. We hypothesize the tendon response and patient outcomes are affected by deficits in muscle function, brain pain processing, and psychological factors as they influence the ability and willingness to move the shoulder. To stimulate tendon structural remodeling, resistance exercise imparts load to the tendon via muscle activation. Deficits in shoulder muscle activation can reduce tendon loading and thus healing. Elevated pain-related psychological distress in the form of kinesiophobia and pain catastrophizing and deficits in pain and sensorimotor processing are associated with poorer outcomes and chronicity in individuals with rotator cuff tendinopathy. Our pilot work supports our hypotheses that higher levels of muscle activation along with lower levels of psychological distress, central pain and sensorimotor processing deficits are associated with a positive tendon structural response and patient outcomes. In this study, we will quantify the effects of resistance exercise on the biomarker of tendon structure and the impact of muscle deficits, psychological factors, and brain dysfunction in pain and sensorimotor processing on shoulder pain and disability outcomes. Patients with rotator cuff tendinopathy (N=70) will be observed during standard-of-care 8 weeks of a resistance exercise. At baseline, 4 weeks, 8 weeks we will use ultrasound to assess tendon structure, electromyography and a load cell for muscle, psychological factors via patient report, functional magnetic resonance brain imaging for pain and sensorimotor processing dysfunction, pain-rating during shoulder functional tasks, and shoulder functional outcomes. We will follow-up at 6-months on patient-reported shoulder outcomes. These data will deliver evidence to design and evaluate a future novel stepped and matched care pathway clinical trial; patients whose tendon is responsive to exercise will continue with resistance exercise, and those with limited response will be `stepped and matched' to alternative care of psychologically-informed treatment for brain dysfunction and psychological deficits, and/or alternative resistance approach to target muscle deficits. Resistance exercise is a known effective treatment, but not for everyone. Using the tendon structure as a marker of treatment response will aid in identifying the dose of exercise, and if other factors effect tendon effects and outcomes. Long-term, we aim to define patient-specific care pathways that can improve the delivery of care and optimize outcomes.

NIH Research Projects · FY 2025 · 2024-07

Project Summary The -omics era has made it possible to identify several molecular markers involved in predicting survival and response to therapies. We currently lack an easy way to obtain high content molecular information while providing high resolution spatial profiling across a patient’s tissue. This proposal aims to provide physicians with an entirely new multiplexed molecular imaging technology that has the potential to offer both high content molecular expression and spatial profiling in a single histology image. Raman spectroscopy in conjunction with surface enhanced Raman scattering (SERS) nanoparticles (NPs) is an optical imaging technique that can offer unsurpassed sensitivity and multiplexing capabilities to the field of histology imaging with the potential to provide rich molecular details on the microscopic level. Clinicians will be able to utilize the imaging strategy on the same tissue sections prepared for histology. Incorporating it into the pathology workflow could enable physicians to better understand the patient’s molecular profile and stratify patients to receive the most effective therapeutic regimen possible. This unique histology imaging strategy also has the potential to identify new molecular trends in patient’s tissue samples that could be used to predict how aggressive their disease is or how well the patient is likely to respond to given therapies. This innovative ex-vivo diagnostic strategy has a high likelihood for clinical translation, offering rapid whole tissue section imaging for multiple molecular biomarkers simultaneously. Our approach begins by developing a new set of sensitive SERS NP batches, each designed with a unique spectral barcode to enable simultaneous molecular interrogation of an entire tissue sample within a single image. After fabrication and characterization of our newly developed multiplexed SERS NPs, we will test their multiplexed imaging capabilities and targeting efficiency on various biomarkers in cell culture and on de- identified human tissue sections. We will first test our new multiplexed imaging technology to target immune cells. Recently, studies have shown that the immune system plays a key role in cancer development, and thus the density, location, and type of immune cells found across a patient’s tumor can predict therapeutic response. Failure to fully understand the immune profile and tumor heterogeneity across a patient’s tumor can lead to administration of ineffective therapies that increase patient morbidity. Our NPs will actively target multiple immune receptors through chemically conjugated antibodies. We will assess the targeting efficiency of our newly developed NPs with microscopic Raman imaging tools and compare with gold standard immunohistochemistry (IHC) staining. These results will be an important step in the clinical translation of this new multiplexed Raman imaging approach; to provide rapid spatial molecular profiling while enabling improved personalized therapy. It’s important to note that we are not limited to interrogating cancer and intend to investigate other relevant clinical applications (ie. wound healing, neurological diseases, infectious diseases, autoimmune diseases) that could benefit from a new multiplexed imaging strategy that offers improved sensitivity and molecular specificity.

NSF Awards · FY 2024 · 2024-07

Over the last decade, machine learning (ML) and artificial intelligence (AI) have often been in the news, thanks to striking achievements in areas such as self-driving cars and AI-enabled chat services. Much of the progress that has led to these breakthroughs can be attributed to the development of deep learning methods (deep neural networks). While these methods have developed very quickly to address a large number of tasks, training them often involves manual tuning and requires enormous amounts of data, resulting in significant costs, including both manpower and power consumption. More sustainable, lower-cost approaches for developing and deploying complex ML and AI systems are needed to ensure that these methods can be widely deployed to accelerate scientific progress and benefit society. For this purpose, it is important to develop a better understanding of why and how certain neural networks outperform others. This is critical for faster prototyping, reduced training times, lower complexity, and better interpretability of deep learning models. The investigators will test their methods through external collaborations and they plan to develop multiple activities to broaden participation in computing, including new course development, REU programs, and K-12 activities. This project focuses on developing techniques for a mathematical understanding of deep learning intrinsic to the given task and data being considered. In this project, neural networks are viewed as operators whose behavior is characterized in terms of their action on the training (or testing) data and, more specifically, on the geometry of the data. One major benefit of this approach is that it can be applied to a wide variety of networks since it abstracts the architecture and considers only how a specific system converts inputs into outputs. This project has three main research objectives. First, when both the model and data are fixed, the goal is to compare different networks and understand how they learn and generalize to unseen data. Second, this project investigates the situation where the data is fixed, but the network model can change. This work focuses on clustering, dimensionality reduction, and importance sampling to reduce the size of the model. Finally, the geometric analysis developed in this project is applied in settings where both the model and data change. This makes analyzing domain adaptation and graph neural network transfer possible, providing efficient algorithms and generalization error 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.

NIH Research Projects · FY 2024 · 2024-07

PROJECT SUMMARY/ABSTRACT The healthy auditory system processes and ultimately makes sense of complex dynamic sounds that vary spectrally and temporally. This complicated process allows for human and non-human species to communicate in both quiet and noisy environments. Significant advances in the understanding of spatiotemporal processing has come from human behavioral studies and physiological animal experiments using frequency sweep stimuli, which are sounds that glide upwards or downwards with frequency over time. Human behavioral data shows that up-sweeps are substantially more effective at masking a tone compared to down-sweeps (> 20 dB). The behavioral literature attributes the dependence on sweep direction to cochlear dispersion, which involves variations in the shape of the basilar membrane (BM) waveform generated by each masker. Due to dispersion, the BM waveform for the down-sweep is peakier compared to the broader response of the up-sweep. Consequently, the presence of a down-swept masker allows for easier detection of the signal in the dips of its waveform. This interpretation finds support in animal measurements and physiological models. While the dispersion mechanism has been used to explain the masking effects between upward and downward sweeps, recent measurements of cochlear vibration in mouse also reveal differences in the cochlear suppression produced by upward and downward swept suppressors and these differences are dependent on sweep rate. Suppression is thought to be important for auditory tasks such as understanding speech in noisy environments. Similar measures of suppression using up- and down-swept stimuli have not been conducted in humans, nor have the suppression effects been compared to behavioral masking. Furthermore, although human behavioral experiments have shown masking differences for upward and downward swept maskers over a range of sweep rates, none of these studies explored what I hypothesize are perhaps the most interesting range of rates (i.e., sweep rates similar to the velocity of the BM traveling wave or to frequency transitions in speech). The proposed aims use a combination of behavioral and otoacoustic measures to study the effects of sweep rate and direction using swept stimuli as either behavioral maskers or otoacoustic suppressors. Otoacoustic emissions are advantageous as they provide a powerful, non-invasive measure of outer hair cell function, enabling the study of suppression effects in humans and their correlation to behavioral masking. The study will pursue the following aims: 1) Determine the strength of behavioral masking as a function of sweep rate and direction, and 2) Determine the strength of cochlear suppression as a function of sweep rate and direction. Completion of the proposed aims will advance understanding of the role of suppression in processing dynamic sounds that vary in rate and direction. The results may provide a framework for studying how this process is impacted with impairment, as suppression is greatly reduced with cochlear damage.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT In the United States, millions of individuals with diabetes suffer from diabetic foot ulcers (DFUs) that negatively impact physical function, reduce quality of life, increase the risk of amputation, and drive up healthcare utilization and costs. These ulcers frequently recur after healing, making it imperative to prevent their recurrence to avoid adverse limb outcomes, major amputation, and death. Although offloading foot pressure is vital for DFU healing and prevention, many patients find it difficult to adhere to offloading treatment recommendations following healing. This study aims to evaluate the feasibility of a lifestyle-focused occupational therapy (OT) intervention to help patients develop self-care routines and habits that support consistent offloading and foot care. The pilot randomized controlled trial will enroll 60 participants with healed DFUs and randomly assign them to either the OT intervention group or a control group. This mixed-methods study has three aims over two years. Aim 1 is to determine if the OT intervention is feasible for patients at risk of DFU recurrence; Aim 2 is to assess patient and provider satisfaction with the OT intervention and the role that occupational therapists play in diabetic limb care using surveys, interviews, and focus groups; and Aim 3 is to compare DFU recurrence rates and changes in diabetes distress and quality of life between the two groups in pre- and post-evaluation. The lifestyle-focused OT intervention is expected to be feasible and acceptable to patients and providers in DFU prevention. It may improve adherence to offloading treatment and patient-reported outcomes. This study will provide preliminary data on this patient-centered approach to prevent DFU recurrence and improve care for this high-risk population. The results of this feasibility study will support an R01 submission that will rigorously evaluate the cost- effectiveness of the OT intervention through a large randomized clinical trial. The ultimate goal of this R03 proposal is to enhance the ability of the PI (Dr. Tan) to transition to an independent investigator by providing pilot data to help support a subsequent R01 proposal.

NSF Awards · FY 2024 · 2024-07

Comparing probability models is a fundamental task in almost every data-enabled problem, and Optimal Transport (OT) offers a powerful and versatile framework to do so. Recent years have witnessed a rapid development of computational OT, which has expanded applications of OT to statistics, including clustering, generative modeling, domain adaptation, distribution-to-distribution regression, dimension reduction, and sampling. Still, understanding the fundamental strengths and limitations of OT as a statistical tool is much to be desired. This research project aims to fill this important gap by advancing statistical analysis (estimation and inference) and practical approximation of two fundamental notions (average and quantiles) in statistics and machine learning, demonstrated through modern applications for measure-valued data. The project also provides research training opportunities for graduate students. The award contains three main research projects. The first project will develop a new regularized formulation of the Wasserstein barycenter based on the multi-marginal OT and conduct an in-depth statistical analysis, encompassing sample complexity, limiting distributions, and bootstrap consistency. The second project will establish asymptotic distribution and bootstrap consistency results for linear functionals of OT maps and will study sharp asymptotics for entropically regularized OT maps when regularization parameters tend to zero. Building on the first two projects, the third project explores applications of the OT methodology to two important statistical tasks: dimension reduction and vector quantile regression. The research agenda will develop a novel and computationally efficient principal component method for measure-valued data and a statistically valid duality-based estimator for quantile regression with multivariate responses. The three projects will produce novel technical tools integrated from OT theory, empirical process theory, and partial differential equations, which are essential for OT-based inferential methods and will inspire new applications of OT to measure-valued and multivariate data. 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 · 2024-07

Project Summary / Abstract The long-term goal of my lab is to better understand cellular mechanisms that regulate secretory granule maturation, efficient packaging of hormone, and secretion. In doing so, we will identify drug targets that improve secretory efficiency during disease. Defects in storage, maturation, or secretion of peptide hormones can lead to several mental and metabolic disorders, including depression, bipolar disorder, and diabetes. Despite the importance of this widespread secretion mechanism, the cellular signals that regulate secretory granule maturation remain unclear. Dense core secretory granules (DCSG) are organelles for the intracellular storage and stimulus-dependent exocytosis in regulated secretory cells. The hallmarks of granule maturation include: losing the clathrin coat, acidifying lumen, processing of hormone peptide, and formation of a dense core. This is consistent for many peptide processing cells. In this proposal, we will focus proinsulin processing in β cells and proopiomelanocortin (POMC) processing in neurons. In both cell types, distinct subpopulations of DCSG exist. These subpopulations have different protein and lipid content which affects their secretory capacity. For example, specific signals lead to preferential secretion of subsets of DCSG granules. We have a limited understanding of how many subpopulations exist and even less is understood regarding their spatial localization. A key gap in knowledge is understanding how different signaling networks affect maturation and the formation of distinct DCSG subpopulations. Additionally, DCSG maturation and the locations it occurs within the cell are still considered a black box. Thus, use of single-cell unbiased imaging approaches, or those with no requirement for stains or probes, are ideal for capturing DCSG maturation in the context of the entire cell. Over the next 5 years, we will establish a high-throughput, and generalizable quantitative structural cell biology platform. To do this, we will leverage soft X-ray tomography (SXT) and fluorescence lifetime imaging microscopy (FLIM). SXT will be used to map cell organization and quantify the molecular packing of secretory granules (or maturation), which has not been possible with other approaches. We will complement these studies with functional live-cell imaging, using a FLIM-based pH probe developed in my lab to monitor granule pH (maturation). We will explore how different signaling pathways influence DCSG maturation and the spatial localization of specific DCSG subpopulations. For a holistic understanding of how the rest of the cell contributes to maturation, we will quantify cellular reorganization by analyzing organelle volumes and inter-organelle contacts. We hypothesize that cAMP signaling and DCSG interactions with the mitochondria enhance secretory efficiency. The proposed work will validate our pipeline as a generalizable approach for studying DCSG maturation and uncover new mechanistic insights into how the cell regulates maturation. This will provide the basis for future development of more effective therapeutics that enhance secretory efficiency during disease.

NIH Research Projects · FY 2025 · 2024-07

SUMMARY/ABSTRACT Post Acute Sequelae of COVID-19 (PASC) may affect 15 to 30% of people infected with COVID-19, which would suggest at least 1 million cases in Los Angeles (LA) County and at least 7 million cases in the United States. The COVID Recovery Clinic at Keck Medicine (Keck CRC) of the University of Southern California (USC) was established in 2020 to treat patients with PASC symptoms. Keck CRC operates under an interdisciplinary, collaborative care model, and brings together a primary care physician, occupational therapist, physical therapist, social worker, respiratory therapist, medical assistant, nurse (RN) navigator, and support coordinator. Keck CRC is well positioned at Keck Medicine of USC and in LA County to accomplish goals to expand and optimize clinical care of PASC, as well as to be a center for dissemination of education and project findings across LA County and nationally across other similar clinics. Through this funding opportunity, the Keck CRC will establish the Keck COVID Recovery Clinic, Optimal Outcomes for Patients in a Comprehensive Assessment and Management Program (Keck CO-OP CAMP). This program will pursue four main goals, in alignment with the eight characteristics of Long COVID clinics specified in the Funding Opportunity “Purpose of the NOFO” (RFA-HS-23-012): Goal 1: Improve current care delivery and system model within Keck CRC (NOFO 1, 2, 3, 4). Activities under this goal will optimize and update the current Keck CRC care model through expanding dedicated staff time and roles, creating and adapting clinical workflows, improving care coordination, establishing mechanisms for oversight, and utilizing data collection and analysis. Goal 2: Establish new models of service to expand services to more patients experiencing PASC, including those who may be limited from ongoing care through the clinic due to insurance or geographic limitations (NOFO 1, 2, 3, 5). Activities under this goal will include creating additional consultation services in-person and virtually, connecting with clinics that may benefit from our resources, adding a support group, and educating patients, caregivers, and healthcare workers. Goal 3: Create multidisciplinary education resources for internal and external providers (NOFO 5, 6, 8). Activities under this goal will include creating clinical workflows, educational materials, and resources, and connecting with clinics that may benefit from Keck CRC resources. Goal 4: Implement consistent and standardized data collection on patient and clinic outcomes, with regular periodic assessment of data, to inform ongoing modifications in care delivery. (NOFO 7, 8). Activities under this goal will include utilizing and adapting technology for data collection, assessment of data, interpretation of data, and dissemination of findings.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY/ABSTRACT Temporomandibular joint (TMJ) osteoarthritis (TMJOA) is a degenerative disease of the joint. It has been challenging to manage and treat TMJOA due to our limited understanding of the molecular, cellular, and neuronal mechanisms underlying TMJOA. The goal of this proposal is to use multi-modal single-cell and imaging technologies to establish a high-dimensional, comprehensive molecular, cellular, and innervation map of TMJ under healthy and OA conditions in mouse models that mimic human TMJOA. We will further validate Netrin- 1/Dcc as the potential therapeutic target of TMJOA. A major strength of this collaborative project is to integrate the findings, skills, and distinct expertise of investigators including PI Jianfu Chen (Neuroscience), co-Is Mildred Embree and Yang Chai (Craniofacial biology and TMJOA), collaborator Xu Cao (Neuroskeletal pain) and Hu Zhao (TESOS volume imaging), which cannot otherwise be accomplished by the individual investigators separately. Strong published and preliminary studies support comprehensive team of scientific approaches in unveiling the mechanisms of TMJOA, including different TMJOA mouse models, methodology for joint pain behavior, dynamic changes in joint pathologies and innervation during OA, 3-D imaging methods to map sensory nerve connectome in the TMJ at micron resolution, antegrade and retrograde tracing of joint innervation, chemogenetic functional assays of neural circuits, and single cell RNA-sequencing approaches. Importantly, our preliminary studies have identified Netrin-1/Dcc as key mediators of TMJ cell-cell interaction and potential therapeutic targets for mitigating TMJOA pain. These exciting preliminary data put us in a unique position to generate the first comprehensive molecular, cellular, and innervation map of TMJ with or without OA and to identify new therapeutic targets. Our guiding hypothesis is that TMJOA causes significant alterations in chromatin accessibility and gene expression of disease relevant cell types including osteoclasts and synovial fibroblasts in TMJ, which in turn cause maladaptive nociceptive innervation leading to joint pain and degeneration. To test our hypothesis, we will: (1) create a comprehensive single-cell transcriptomic and epigenomic cell atlas for the TMJ in TMJOA model mice and identify molecular changes that accompany TMJOA in each cell type. (2) functionally map TMJ nociceptive innervation during OA progression. (3) validate Netrin-1/Dcc as mediators and therapeutic targets of TMJOA pain.

NSF Awards · FY 2024 · 2024-07

Identity-based discrimination is a global problem that reduces opportunities for people from disadvantaged groups; yet its sources, evolution, consequences, and what can be done about it are little studied by economists. This award funds research that collects large survey data over long periods of time to study the causes of identity-based discrimination, how it has changed over time, consequences, how it is affected by policies designed to reduce it. It will conduct interviews to collect information about current and historical discriminatory practices, policies to combat such practices, and the responses of discriminatory practices to these policies across many jurisdictions and make this data set available to researchers. The dataset will allow researchers to study many aspects of identity-based discrimination and how to combat it. The research will use the data to study political, social, and economic factors that have reduced identity-based discrimination. While providing large data set and evidence on the causes and changing nature of identity-based discrimination, results of the research will provide guidance on policies to reduce identity-based discrimination. The results could lead to improved fairness and efficiency in labor markets, better use of resources, increase productivity and economic growth, and establish the US as a global leader in reducing identity-based discrimination. This award will fund research that collects a large-scale dataset on the causes and evolution of identity-based discrimination. The research will survey individuals on the contemporary presence of 15 forms of discrimination in their area, as well as any changes in these practices over their lifetimes in thousands of locations and use the information to construct a panel data on prevalence of discriminatory practices over the past 50 years. Besides making this data available to other researchers, the PI will use the data to test which political, economic, and social factors have reduced discriminatory practices. The research results together with those of scholars who will utilize this data, have the potential to advance understanding of effective discrimination reduction strategies. In addition, this research results will provide guidance on policies to reduce identity-based discrimination and improve the lives of marginalized people around the world as well as establish the US as the global leader in reducing discrimination and providing equal opportunity for all. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

ABSTRACT Aging is characterized by the accumulation of amyloid and prion-like proteins. However, the molecular mechanisms by which these proteins arise remain unclear. The overarching hypothesis of this proposal is that the amyloid and prion-like proteins that accumulate in aging cells are generated by mistakes that occur during transcription. To test this hypothesis, we will screen a database of 260,000 transcription errors identified in human stem cells, brain organoids and fully differentiated neurons for candidates that are likely to create amyloid peptides. We will then use a variety of cellular, biochemical and biophysical tools, including cryo-EM, TEM and fluorescent imaging to test the amyloid behavior of these proteins. Our preliminary data indicates that these experiments will create a powerful proof of principle of our hypothesis. Next, we will test how these transcription errors are generated. Several studies suggest that DNA damage plays an important role in this process, indicating that age-related DNA damage could drive the accumulation of amyloid and prion-like proteins. To test this hypothesis, we will use single-cell-sequencing coupled to a new, custom-made bio-informatic pipeline to determine if human neurons exposed to DNA damage display error prone transcription, and if so, how many mutant proteins are generated as a result. Our preliminary data indicates that O6-methyl guanine lesions, a common form of DNA damage in the brain, creates vast amounts of mutant proteins in single cells, strongly supporting our hypothesis. Next, we will determine whether these errors are sufficient to induce protein aggregation in human neurons using a plasmid-based system that contains a carefully placed O6-methyl guanine lesion. Finally, we will test if O6-methyl guanine -induced errors can affect the pathology of Alzheimer’s disease by removing the DNA repair gene MGMT in the neurons of a mouse model that carries a humanized version of the APP protein. MGMT repairs O6-methyl guanine lesions, which will allow these lesions to accumulate over time, increasing transcriptional mutagenesis. Moreover, it was recently found that MGMT is down-regulated in female patients with non-familial cases of AD, so that this experiment closely mimics the conditions of human patients. Accordingly, we think that in addition to testing our hypothesis, these experiments could also lead to the first mouse model that fully mimics late onset, non-familial cases of AD. The most exciting aspect of our work though, is the challenge we pose to the DNA-centric way of thinking in modern medicine. Our work shows that in some cases, the mutations that give rise to disease do not have to occur in the genome, they can also occur in the transcriptome. In doing so, our research could establish a new paradigm in modern medicine and open up a new field of aging research to widespread experimentation.

NSF Awards · FY 2024 · 2024-07

Tens of millions of Americans interact with artificial intelligence (AI) tools to find information, answer questions, or help them solve problems. One key drawback of these systems is lack of personalization: since modern AI systems do not know whom they are talking to, they can only give generic answers to user questions. But the answer to the question “why is the sky blue?” should be different if the person asking the question is a college student or a young child. This project aims to enable an AI model to provide more appropriate responses to users depending on their unique backgrounds, experiences, and needs. It will first gather a diverse dataset in order to characterize what kinds of responses are preferred by different people. The project will then use these data to develop AI systems that can tailor their answers to individual users, as well as evaluate how well the AI systems personalize responses. To achieve this personalization, the AI systems will learn to explicitly represent the kind of person they are talking to, based on their background or previous interactions, and then use this representation to generate an appropriate response. This project will result in AIs that can provide personalized, specific responses based on the person asking the question as well as resources that will help other personalize AIs. These resources will include datasets of personalized questions and answers, interfaces and visualizations to understand why AI provides specific responses over others; interviews and discussions with community members to understand their needs; and code and models that will allow others to build, train, and deploy personalized AI systems. While large language models (LLMs) trained on massive datasets have shown impressive performance on a variety of tasks, they still exhibit biases and struggle to be equally useful for everyone. While initially pre-trained on a language modeling objective, most LLMs are further fine-tuned to align their outputs with human preferences. However, existing techniques assume a “one size fits all” approach, ignoring diversity in user needs. This project will first construct probes to detect cases where models fail to adapt to the diverse needs of different users. Then, this project will develop Personalized Feedback for Diverse Populations (PFDP) to identify when models should be sensitive to the unique needs, knowledge, and background of users by examining the training trajectory of models and comparing models' answers to human preferences. PFDP will enable the development of models that can detect examples that are difficult for computers but not for humans, explain why such disparities in difficulty exist, and represent users’ needs and preferences within the model. To correct those shortcomings in the data, we focus on data curation: we propose techniques to automatically create new examples that ask questions about under-represented groups or require targeted responses to create adversarial prompt and response pairs with a human in the loop. Finally, with these new data, we develop techniques to allow modern architectures to make the most of these difficult (but few) examples. These techniques will allow for fine-tuning LLMs with a small curated subset of data that is robust to variations in prompts and will lead to the generation of acceptable answers for a diverse population of users. 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 · 2024-07

PROJECT SUMMARY/ABSTRACT The goal of this proposal is to provide insight into the structure, dynamic interactions and multifunctionality of the enamel protein ameloblastin (Ambn), and hence to contribute to the fundamental knowledge as relevant to enamel biomineralization. Ambn is multifunctional with critical roles in the regulation of mineral formation, cell differentiation, cell polarization, and cell–matrix adhesion. We recently identified a novel, highly-conserved amphipathic helix (AH) cell binding domain, located within the sequence encoded by exon 5, and adjacent to the C-terminus of the well-established Ambn self-assembly/co-assembly domain. Our preliminary data using two novel CRISPR-Cas9 gene editing mouse models in which different parts of the AH domain are deleted, show that the AH plays essential roles in amelogenesis. Analysis of enamel from homozygous strains with different deletion mutations on AH domain revealed distinct enamel phenotypes that exhibited hypomineralization with different severity. These observations along with recent published reports from our laboratory have led us to hypothesize that Ambn is a matricellular protein, and its complex role in amelogenesis relies on a multitargeting property centered in the region of the sequence encoded by exon 5. Ambn interacts with ameloblast cells via the AH domain and functions to anchor the mineralizing extracellular matrix to enamel-forming cells. To test our hypothesis, we seek to implement a mechanistic approach to interrogate the structure and function of the multitargeting motif in Ambn, including the AH domain. This will be achieved by 1) conducting a detailed investigation of enamel formation in two novel Ambn AH mutant mouse models in vivo (Aim I), 2) examining Ambn-cell interactions in 3-D cell culture (Aim II), and 3) completing an in vitro investigation of interacting domains in the Ambn sequence involved in its self-assembly, amelogenin (Amel), other potential targets, and cell membrane interactions (Aim III). Overall, our studies will advance understanding of the molecular function of Ambn in enamel biomineralization and of inherited enamel disease, elucidate genetic factors leading to caries susceptibility, and contribute to our efforts to regenerate dental hard tissues.

NSF Awards · FY 2024 · 2024-06

Cyber-Physical Systems (CPS) are typically composed of interconnected hardware and software components, which individually may not be inherently highly reliable or secure. However, several CPS applications demand a high degree of safety, security, and reliability. Thus, the fundamental problem is constructing highly dependable CPS applications from building blocks that are, in themselves, not inherently reliable. There has been enormous progress made in understanding and patching various classes of vulnerabilities in large-scale distributed CPS. However, these efforts at designing and operating resilient CPS have often been stymied by the lack of understanding of the impact of any perturbation to the overall system, under the economic and policy constraints involved in any realistic real-world CPS. We define perturbations as failures due to: (1) unintended errors in hardware/software, (2) security attacks, (3) unexpected interactions among cyber-physical and human elements including natural disasters, and (4) incomplete cooperation among stakeholders. In this project, we address these shortcomings to challenges to create resilient, large-scale CPS through our CHORUS Frontier award. Chorus will develop rigorous, scientific mechanisms to enable CPS resilience against a large universe of perturbations. Our application domain is Connected and Autonomous Transportation Systems (CATS) and thus, the benefits of CHORUS will be demonstrated through improvements in safety and security in this domain. We will achieve goals of CHORUS through three interacting intellectually challenging thrusts in the project. Thrust 1 is on Modeling which will create executable specifications of cyber, physical, and human assets, their interconnections, and the economic and policy constraints. The models will capture various stakeholders in the system and their degree of information sharing and cooperation in defense of the target CPS. Thrust 2 is centered on Proactive planning and deployment. We will develop rigorous game-theoretic formulations to model the spread of perturbations (natural and man-made), their effects, and the appropriate resource allocations that can be applied for resilience at the planning stage, i.e., prior to system deployment. We will also consider which defensive investments are feasible under a rational versus a bounded rational behavioral model of the stakeholders. Thrust 3 focuses on Runtime distributed detection and response. We will determine, at runtime, the security state of the system, through novel uses of existing sensors in the system even though they are imperfect. This will then trigger the response mechanisms, which will be proven to be approximately optimal, through analysis and experimentation. In terms of broader impact, the greatest impact will be that CPS owners will gain a higher degree of trust in the operation of the CPS and policy-makers will understand what level of cooperation among multiple stakeholders in a CPS to incentivize. We will create compelling demonstrations of CHORUS on a connected vehicle testbed distributed between our academic institutions and our industrial partner GM. We will also organize an annual student security competition and develop two MOOCS, both having foundational material on resilient CPS and one focusing more on the CATS application domain. 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 · 2024-06

Adult stem cells maintain our organs throughout life by continual replenishment of the specialized cells of that organ. Recent discoveries have shown that events occurring in early life can impact adult stem cell function many years later. This project will test whether specific early-life events – mediated by diet or growth/metabolic signaling pathways – can influence blood stem cell function years later. This question will be addressed by combining stem cell transplantation and sequencing experiments in mice with new mathematical models to predict the dynamics of stem cell populations over time. By jointly measuring the expression of genes and the epigenetic marks present on the DNA of single cells, it will be possible to test the hypothesis that early life events induce a stem cell ‘memory’ that persists throughout life. If such an effect is found, the investigators will test whether it is possible to reverse it by exposing stem cells to specific molecular factors to partially reprogram the cells to a state in which the effects of early-life signals are erased. The investigators expect the results to provide a proof-of-concept that stem cell memory impacts the selection of stem cell subpopulations during life. These findings will impact our understanding of cell signaling more generally and stem cell memory across other organs and development systems. This multidisciplinary study will also offer diverse training and educational opportunities for students in a variety of settings ranging from K-12 classrooms to medical school. The maintenance of the blood system throughout life is controlled by an intricate combination of cell-autonomous and extrinsic mechanisms in hematopoietic stem cells (HSCs). Throughout life, HSCs undergo somatic evolution and selection of subpopulations of HSCs occurs. This project will investigate the role of growth signaling in early life and its impact on HSCs by analyzing the influence of early life IGF/growth signals on the installment of epigenetic memory and the selection of molecularly defined subpopulations of HSCs during adult life. HSC transplantation studies will be used to study HSCs perturbed in early life and then implanted into non-perturbed niches (or vice versa) to investigate the extent to which HSC-intrinsic memory effects vs. extrinsic (i.e. niche/systemic effects) dominate stem cell dynamics throughout life. HSC subpopulation selection will be assessed using time-course single cell sequencing analyses of genetic mouse models and dietary interventions that change IGF/growth-signaling during development. Mathematical models will be developed to study how dynamic transcriptional networks change in HSCs of adult mice in response to transient perturbations in developmental growth signals. Finally, transient induction of pluripotency factors (partial reprogramming) will be employed to test whether it is possible to revert memory effects of developmental IGF/growth signaling and whether this would ameliorate developmental influences on epigenetic memory, transcriptional changes, and the selection of molecularly defined HSC subpopulation during adult life. This collaborative US/German project is supported by the US National Science Foundation (NSF) and the Deutsche Forschungsgemeinschaft (DFG) where NSF funds the US investigator and DFG funds the German partner. 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 · 2024-06

This collaborative project will contribute to the advancement of national prosperity and economic welfare by enabling process monitoring in personalized manufacturing, specifically one of a kind parts produced using additive manufacturing processes with complex geometries and novel materials. Monitoring personalized manufacturing processes for fabrication of high variety low volume products is a daunting task, requiring novel dimension-reduction and change detection methods beyond existing frameworks for mass production. This project will provide methodologies to reduce complexity in data representation, learning, and quality control for personalized manufacturing, facilitating the adoption of cost-effective personalized manufacturing technologies through improved product quality and simplified supply chains. The PIs will develop interdisciplinary curricular materials to train the next generation of manufacturing analytics workforce. This project will establish a new latent space monitoring methodology based on process-informed dimension reduction of the shape space for geometric quality control in smart personalized manufacturing. The methodology exploits the concept of manufacturing primitives to construct a process-informed latent space representation of 3D shapes. This representation enables the development of efficient domain-informed algorithms for (1) learning of the in-control shape quality distribution, (2) transfer learning of shape quality distributions between different process settings, and (3) latent space monitoring of part-to-part shape quality. The developed methodology will be validated in both metal-based wire-arc and polymer-based additive manufacturing processes. 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 · 2024-06

The 2024 International Symposium on Combustion will be held in July 2024. It is a gathering of combustion researchers from academia, industry, and government, both from the United States and around the world. The conference's technical program consists of a series of plenary lectures, contributed oral presentations, poster sessions and social gatherings. The best work presented is published in the Proceedings of the Combustion Institute. This award will provide funding for students to attend the symposium, where they will have a unique opportunity to meet members of the research community, discuss their research, and exchange ideas with both senior researchers as well as their student peers from around the world. Moreover, several panel discussions on Industry Perspectives will be held and one of the topical review presentations will be followed by a panel discussion that includes experts from academia and industry. Combustion is ubiquitous in traditional energy conversion systems such as automotive engines, stationary, and aircraft gas turbines, rocket and space propulsion, electrical power generation, industrial furnaces, and home and institutional space heating. This year there will be special emphasis on transition to renewable fuels such as hydrogen, ammonia, methanol, sustainable aviation fuels (SAF), etc. produced via clean renewable energy and CO2 sequestration. Moreover, emerging technology areas such as hypersonic propulsion, microscale power generation and material synthesis depend critically on chemically reacting flow processes. The world’s dependence on combustion processes has led to many technological challenges including air quality, energy efficiency, and fire/explosion safety. Thus, combustion is still an active, vital area of research. The community of researchers shares knowledge in this important area through the symposium. Student travel support will engage the next generation of combustion researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Microplastic pollution—present on land, in waterways, and in the ocean—is the result of the environmental degradation of consumer and industrial plastic waste. Microplastics have been the subject of significant concern in recent years due to their potential incorporation into the food chain and their capacity to harm plants and animals. Microplastics can degrade further into environmental nanoplastics: particles of plastic waste smaller than a biological cell. Because they are so small, nanoplastics have been hard to isolate from the environment and have proven a stubbornly difficult class of materials to study. This project will develop a library of laboratory-created nanoplastic particles that mimic the size, shape, and composition of nanoplastics isolated from river water. This library will facilitate a detailed study of how nanoplastics can interact with, damage, and degrade the lipid bilayer membrane that serves as the boundary of cells. There is much evidence that engineered plastic nanoparticles can damage cell membranes, but these engineered particles are uniform in shape and composition. Environmental nanoplastics on the other hand are highly irregular and diverse. Identifying how these characteristics of irregular shape, size, and chemical composition control the interactions between environmental nanoplastics and cells is key to understanding how plastic pollution can be biologically harmful. The research project will incorporate a set of training and outreach efforts designed to train undergraduate and high-school students in the physical biology of environmental plastics. These efforts will include a summer undergraduate research program in collaboration with the Wrigley Institute at the university that will take students from field studies of plastic waste to laboratory investigations of nanoparticle-membrane interactions. Two high-school interns will engage in afterschool research in each year of the project. Sub-millimeter particles of plastic waste are broadly distributed in the environment, with particularly high concentrations in waterways, lakes, and oceans. Environmental nanoplastic particles are particularly concerning due to their capacity to infiltrate biological tissues on the sub-micron scale and to interact on the cellular level. In understanding these interactions, it is critical to consider how nanoparticles encounter and damage the key cellular interface—the plasma membrane. Research has demonstrated that nanoparticulate materials interact with cell membranes: they can deform, permeate, and damage cell membranes. The particles that have been studied so far, however, are engineered materials that are perfectly spherical, uniformly sized, with surface ligands that control charge and stability, and made of only polystyrene. Environmental nanoplastics are radically different: irregularly shaped, polydisperse in size, with surface properties determined by their environmental history, and with a diverse chemical makeup. This project leaps over this gap—transforming the understanding of membrane-nanoparticle interactions by putting real-world environmental nanoplastics under the literal microscope. This project executes three tasks: (1) Develop tools for creating plastic particles that mimic environmental nanoplastics and isolate these nanoplastics from environmental sources. (2) Discover how as-isolated mixtures of environmental nanoplastics can alter cell membrane morphology, integrity, permeability, and rigidity. (3) Understand how specific environmental nanoplastic characteristics (shape, size, protein coating) contribute to cell membrane damage. To accomplish these objectives, the team will use a suite of tools for understanding engineered nanoparticle-cell interactions and leverage new resources being developed at the University of Southern California to study environmental plastic pollution. The research project will incorporate a set of training and outreach efforts designed to train undergraduate and high-school students in the physical biology of environmental plastics. These efforts will include a summer undergraduate research program in collaboration with the Wrigley Institute at the university that will take students from field studies of plastic waste to laboratory investigations of nanoparticle-membrane interactions. Two high-school interns will engage in afterschool research in each year 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.

NIH Research Projects · FY 2026 · 2024-06

Project Summary Reliable, reproducible estimates of the costs of Alzheimer’s disease (AD) and AD related dementias (ADRD) are critical for stakeholders and decision makers to fully understand the size of the economic and financial disease burden and to inform priority setting. Dementia cost estimates typically include medical costs and the cost of long term services and support and some indirect costs such as the value of the time of unpaid caregivers for persons living with dementia. Yet, dementia cost magnitude, when based on a limited set of costs, is likely a significant underestimate of costs, particularly as it relates to costs associated with unpaid care provided by family and friends. Time spent caregiving may result in productivity losses and future income and wealth losses. Caregivers may experience health and quality of life impacts and medical costs associated with the challenges of caregiving. Additionally, participation in interventions or clinical trials may impose costs to both persons living with dementia (PLWD) and their care partners and caregivers. Vulnerability to financial and physical abuse is more common among persons living with dementia than for persons without dementia, with important financial and non-financial costs associated with both. We propose to provide national, annual, comprehensive cost estimates of dementia from the societal perspective. Concurrently, we will build the infrastructure to greatly increase national research capacity for generating reliable, reproducible cost estimates and for utilizing innovative methods for analyzing what impacts these costs, how and for whom. We have brought together dementia experts from across the nation and the experience in convening lived experience panels through partnership with the Alzheimer’s Association. We will draw on the significant expertise of our multidisciplinary team in dementia cost estimation using nationally representative survey and administrative data sources. We will build upon decades of investment in validated and sophisticated dynamic microsimulations models for estimates and projections of population health and spending, the Future Elderly Model (FEM) for middle age and older US adults and the ADRD FEM for dementia estimates, and Future Adult Model (FAM) for young to middle- aged adults. We will innovate on these models to build the U.S. Costs of Dementia Model (USCDM) that will fill gaps in cost estimates and facilitate reproducibility and use by the research community. Utilizing accessible platforms for data and code sharing, and a comprehensive pilot program with innovative mentoring, we will expand the capacity of the research community to produce comprehensive, robust and replicable estimates of the costs of dementia in the US over time, and to use dynamic microsimulation and modeling tools to quantify how changes in population health, demographics, care models and systems, public and workplace policies, and new treatments impact costs of dementia today and in the future.