University Of California Los Angeles

NSF Awards · FY 2024 · 2024-09

In collisionless space plasma, mixing and exchanging different energies, e.g., magnetic field energy and charged particle kinetic energy, are controlled by the formation and destruction of plasma structures like current sheets. Current sheets are universal quasi-1D self-consistent plasma and magnetic field configurations that naturally form as boundaries between different plasmas or as current layers embedded within stretched magnetic field lines (typical configuration for the planetary magnetospheres). This project will systematically examine current sheet characteristics in Martian and terrestrial magnetospheres. These two systems differ significantly in plasma content and energies. The team will reveal details of current sheet formation and destruction associated with charged particle acceleration in different parametric regimes. This collaborative project between UTD and UCLA involves significant contributions from two PIs and two graduate students. The configuration and stability of an essential kinetic plasma structure, the current sheet (CS), determine the efficiency of magnetic energy storage, release, and transport in surrounding plasmas. These properties depend on plasma parameters (the ratio of plasma to magnetic field pressures, the ratio of bulk velocities to magnetosonic velocities, etc.). The main scientific goal of this project is to systematically characterize current sheet configurations in the Martian magnetotail (using MAVEN observations), compare these configurations with statistical results of the Earth’s magnetotail current sheets, and reveal the role of plasma parameters, as well as heavy ion contributions, in various configurations. To mitigate the uncertainties due to single-spacecraft measurements from MAVEN, this analysis will be supplemented by comparisons with kinetic simulations of the Martian magnetotail. To compare Martial and Earth’s magnetotail current sheets, we will use current sheet datasets from THEMIS and ARTEMIS, supplemented by Cluster and MMS datasets. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award supports the development of quantum logic techniques for the control of polar molecular ions. It is expected that the ability to control such samples could lead to significant technological and fundamental scientific advancements. These include potentially learning how to control chemistry in the quantum regime, which could aid novel materials and drug design, and creating a platform that could be useful in producing a robust and scalable quantum computer. Other potential outcomes would be allowing new probes of quantum matter and charge transport, understanding the formation of interstellar clouds, and precision measurement of molecular structure for tests of fundamental physics. The main research objective of this project is to develop techniques for controlling these molecular ions to better understand their usefulness for quantum computing, quantum sensing, and quantum communication. Additionally, this project will allow for the training of several high-school, undergraduate and graduate students, and a postdoctoral researcher in state-of-the-art techniques. Past students have gone on to careers in the quantum workforce, government labs, and the private sector. The project will also support an effort to partner with local elementary schools to bring inquiry-based, active learning, laboratory-based experiments to their classroom. As such, this project has the ability to aid the progress of science and, in the longer term, bolster national prosperity and security. Atomic, molecular, and optical (AMO) physics offers two chief opportunities: a means to understand and harness quantum interactions and the ability to test the framework of fundamental physics. To date, significant progress on these fronts has been due to the study of ultracold atoms and atomic ions, primarily enabled by laser cooling, as well as work over the last decade to extend these studies to neutral polar molecules. Under this award we will continue to push the techniques of AMO physics to a new, unexplored frontier: the study of ultracold polar molecular ions. Specifically, we will develop methods for laser-free quantum logic control of polar molecular ions based on externally applied electric field gradients. This control promises to be robust and potentially more scalable than laser-based control that is currently employed in the field. Further, due to the intricate internal structure of polar molecular ions, the availability of such molecular samples allows for significant advancement on these two chief fronts of AMO physics. Thus, the overarching goal of this project is to break open a new field in AMO physics by providing means for full quantum control of molecular ions and to realize the concomitant benefits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

By developing a framework to study higher order interactions, i.e., simultaneous interactions, the funded work will provide novel tools to analyze complex systems. The COVID-19 pandemic was challenging to control because people could catch the disease from accumulating many short exposures to multiple infected people, i.e., from higher order interactions, which are rarely considered in epidemiological models. Similarly, the efficient transfer of goods was another casualty of the pandemic due to supply-chain disruptions. Higher order interactions, in which goods are exchanged simultaneously, can substantially expedite the transfer of goods and increase the robustness and resilience of supply-chains to disruptions. The general framework that will be developed in this grant will use a tractable biological system to develop mathematical tools to study the causes and consequences of higher order interactions. The mathematical models and tools developed will be general, to allow application to other systems, such as communication, disease transmission, and social learning. Public health and bioeconomics are two examples of fields that can benefit from the funded work. The work will be published in general journals with a wide interdisciplinary readership and the analysis code will be made publicly available. Both PIs have a strong track record of recruiting and facilitating the success of students from groups that are unrepresented in the sciences and this commitment to mentoring a diverse population of trainees in interdisciplinary work will continue. To further disseminate the work to the general public, podcast episodes will be produced and distributed widely. Collective outcomes, such as the social behavior of animals, emerge from interactions among system components. While substantial work has been devoted to examining the intricate network of interactions among animals, these interactions are described and analyzed as dyadic events. However, multiple individuals can interact simultaneously. For example, an alarm call is broadcast to multiple individuals at once rather than through multiple one-on-one interactions. Despite the important conceptual and functional differences between dyadic and higher order interactions, there are only few methodological approaches that emphasize the higher order nature of social interactions. The proposed work will examine the causes and consequences of higher order interactions, and the feedback between them, by adapting and implementing existing mathematical tools from algebraic topology, simplicial sets, in novel ways. Specifically, the aims include to determine the conditions under which higher order interactions emerge; to examine the consequences of higher order interactions; and to investigate feedback between causes and consequences of higher order interactions to uncover potential evolutionary pathways for their emergence. Social insects are an especially powerful system for examining the questions in the proposal because of the profound fitness consequences of interactions among individuals for the group. Therefore, the proposed work will use foraging and food transmission of Argentine ants (Linepithema humile) as a model system to examine the internal and external causes and consequences of higher order interactions. Project outcomes will enable innovative approaches to fundamental and generalizable questions which are currently beyond our reach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The broader impact of this I-Corps project is the development of a biotechnology tool to increase gene editing efficiency and accelerate the development of cell therapies. Gene editing technology, a method for making specific changes to the DNA of a cell, is used to turn human cells into therapeutic cells. The method may be used as a potential treatment and cure for many diseases including cancer. Currently, however, this application is limited by the low efficiency of gene editing, which results in only a few percent of the cells being engineered successfully and becoming therapeutic cells. This low yield makes cell therapy one of the most expensive treatments and creates significant unmet patient demand. With increased gene editing efficiency, more therapeutic cells may be created, which may lower the manufacturing cost of therapeutic cells and help cell therapies treat more patients. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a microfluidic device to increase gene editing efficiency. To achieve successful gene editing, materials used for gene editing need to be delivered into cells and have access to the target genes. However, many genes are densely packed and hidden within the chromatin, which makes them difficult to reach and leads to low editing efficiency. This technology tackles this issue through a mechanism called cell massage. Cells are gently squeezed through microchannels within the device, and the mechanical stimulation on the cell nucleus opens the chromatin structure temporarily. This opening allows the genes to be more accessible to gene editing materials. This solution has been shown to lead to a 10-fold increase in gene editing efficiency. The increased efficiency may lower the epigenetic barrier and make it easier for gene editing tools to reach and edit target genes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to serve the national interest by closing the academic completion gap for Latina/o STEM students through the implementation of a "Circle of Champions." The Circle of Champions framework seeks to organize individuals around students, actively supporting them throughout their academic journeys. Under this approach, students nominate parents, other family members, friends, former high school teachers, professors, and similar individuals as their champions. With a Circle of Champions around each student, the project tracks their journey, informs champions of progress, and facilitates their learning on how to provide support effectively. The goal is to leverage students' assets and community wealth into traditional forms of social, cultural, and academic capital. This project employs a cultural assets approach to student learning combined with an intentional focus on harnessing the considerable resources students possess within their families, communities, and themselves. By addressing this oversight, the project will set the stage for an equity-oriented approach to supporting student success. This project seeks to accomplish four goals: 1) advance the understanding of converting social capital into academic capital; 2) investigate conditions under which the Circle of Champions model can be optimized to impact student success in STEM; 3) narrow or close the equity gap in STEM at Gavilan College; and 4) develop a replicable model for other Hispanic Serving Institutions. Project activities aimed at achieving these goals include supporting the Circle of Champions model for all Gavilan College students enrolled in STEM courses, expanding and developing an AI assistant platform, studying the effectiveness of research and program evaluation to fully understand variables influencing success, and disseminating findings. The researchers aim to explore existing assets in the lives of Latina/o students, particularly their social capital, and how these assets can contribute to academic success. To investigate the impact of social networks on students' lives, the project utilizes Community Cultural Wealth (CCW) and Funds of Knowledge (FK) models as guiding frameworks and employs a mixed method of analysis, including qualitative analysis of user opinions, quantitative analysis of user activity using machine learning, and quantitative assessment of student academic outcomes. The NSF IUSE: Innovation in Two-Year College STEM Education (ITYC) Program seeks to accelerate the impact of and advance knowledge about emerging and evidence-based practices in undergraduate STEM education at two-year colleges. This project is partially funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project involves modeling and analysis of particle laden flows using nonlinear partial differential equations (PDEs) for the flow thickness and volume fraction of particles in the flow. Such flows arise in many applications including the food industry, mining, and environmental cleanup. These flows are notoriously difficult to model because the dominant physics, especially for viscous flows, is due to many body interactions of the particles. There are no "first principles" continuum models for the physics and instead modelers rely on semi-empirical rules for particle settling and migration. Even at the elementary level of reduced order continuum theory, the mathematical equations are a system of conservation laws with fluxes that need to be estimated numerically. This project addresses fundamental mathematics problems related to these models. The project also develops new models for flows in complex geometries such as spiral separators used in the mining industry. This project is a five-year study that impacts our understanding of particle laden flow dynamics and analysis of PDEs for the novel fluid equations that model the physics of particle laden flows. In addition, this project provides research training for two doctoral students, five undergraduate researchers, and two postdoctoral scholars over a five-year period. This project addresses several interrelated problems in particle laden flow models. (a) Flux functions in conservation law models for particle laden flow must be computed or estimated numerically. This raises the question of structural stability of multi-wave solutions of conservation laws under perturbation of the flux function. (b) Singular shocks have been shown to exist in conservation laws that model particle laden flow. Such solutions have largely, to date, been a curiosity in the mathematics literature. This project considers the actual physics that leads to singular shocks and studies how to continue those solutions after the singular shock formation in a way that is consistent with experimental observations. (c) This project considers models for bidisperse flows with direct comparison to experiments, building on earlier work for bidensity flows. (d) Spiral Separators are devices used in the mining industry in which slurries flow under gravity in a helical trough and species within the slurry naturally separate through turns of the spiral, coming out as stripes at the end. This project develops an asymptotic model for two species flows in spiral separators and studies how to optimally separate the species. 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 The neuroscientific understanding of alcohol use disorder (AUD) has advanced tremendously over the past three decades. However, translation from preclinical models to clinical studies lags behind the evolving neuroscience literature. Two of the most prominent neurobiological models of addiction are the allostatic model and the incentive-sensitization model. The allostatic model focuses on the transition from positive to negative reinforcement as addiction progresses. The incentive-sensitization model also emphasizes dynamic processes in addiction and focuses primarily on the transition from ‘liking’ to ‘wanting’ a substance. Given that the clinical application of these two prominent models is underway, understanding the forward translation (pre-clinical to clinical) of these neurobiological models is more important than ever before. Moreover, identifying demographic moderators of the interaction between these models is a crucial next step in understanding the potential application of these findings in treatment development. Further, these two prominent neurobiological models are being used to inform precision medicine approaches even though the predictive utility of these models on alcohol consumption is limited. Therefore, innovative methodology is necessary to increase the translational efficacy of neurobiological models to human clinical samples. This proposal is based on recent indications that there is an interactive effect between the allostatic model and incentive-sensitization model. Specifically, a preliminary study in the Sponsor’s laboratory focused on the allostatic model and incentive- sensitization model. Their combined effect showed that there is an interactive effect of negative mood and cue exposure on drinking among individuals with AUD. The objective of this NRSA application is to foster the applicant’s development as a clinical scientist with a focus on neurobiological models of addiction, translational science of addiction, and quantitative methods. This proposal aims to fill the gap in the literature by examining the interaction between the allostatic model and incentive-sensitization model, identifying moderators of this interaction, and testing the predictive utility of these models. Specifically, Aim #1 tests the effects of negative mood and cue exposure on same-day alcohol craving and alcohol use. Aim #2 tests the predictive effects of the allostatic model and incentive-sensitization model on longitudinal alcohol use. Exploratory Aim tests demographic moderators of the relationship between negative mood and cue reactivity. To address these Aims 64 individuals (32 females) with current AUD (mild to severe) will complete daily diary assessments of mood, craving, cue-exposure, and drinking for 14 days and will complete remote follow-up visits at 4, 8, and 12-weeks post daily diary. A sophisticated analytic approach including longitudinal multilevel modeling and multilevel moderation will be used to test these aims. The present study represents an important step in furthering the translation of neurobiological constructs of AUD and, with the support of the mentoring team, the applicant’s scientific development as an independent researcher with an interest in translational science of addiction.

NSF Awards · FY 2024 · 2024-09

The “Hubble Tension” is the statistically significant difference between direct determinations of the Hubble constant H0 in the local Universe and the value extrapolated from early universe measurements under the standard flat lambda cold dark matter model (Lambda-CDM). The team has previously published a measurement of H0 based on seven gravitational time delay lenses that reached 2% precision and was in excellent agreement with the local distance ladder method based on Cepheids and in strong tension with early universe data. However, these results depend on specific assumptions about the mass density profile of lens galaxies - the key source of systematic uncertainty in this method. A research collaboration between the University of California-Los Angeles (UCLA) and the University of California-Davis (UCD) will improve this important measurement by adding precision and using new observations to resolve some of the ambiguities in the physical models. The investigators will continue their educational/public outreach efforts, focused on increasing the number of young scientists from underrepresented groups and highlighting the importance and a real understanding of the scientific method. To address these issues, the team proposes to extend this work to a sample of 12 time delay gravitational lenses and obtain spatially resolved stellar kinematics of the deflectors. In addition, they plan to combine information from time delay lenses with that obtained from two samples of non-time delay lenses (SLACS and SL2S). The two external samples, SLACS and SL2S, provide additional statistical information about the dynamics and mass distributions of the population of lensing galaxies. The use of multiple strategies, multiple instruments, multiple samples and multiple modeling codes will allow the team to check for residual systematics and selection effects. Finally, they will use samples of elliptical galaxies that are not lenses to extract information about their mass distribution on a statistical basis and further improve the sample. At UCLA, the lead investigator will teach a new course “Energy in nature and society”, aimed at illustrating to students the scientific method and improving the use of quantitative thinking into the environmental debate, and develop an astrophysics curriculum for preschool. Similarly, the lead investigator at UCD has developed a month-long “Introduction to Astrophysics” course for high-school students on the Davis campus as part of the UC COSMOS program. This course introduces high school students, drawn from California’s diverse population, and many from small schools in isolated areas, to a research environment in which they work with data from cutting-edge facilities. 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 Anxiety disorders represent a significant mental health disease burden in the United States today, with roughly 1 in 3 Americans expected to be diagnosed with an anxiety disorder in their lifetimes. Current treatment strategies include psychotherapy and psychopharmacology, which are better than nothing, but still are insufficient. Novel brain stimulation techniques have emerged as putative alternatives, but these have drawbacks, namely imprecision and lack of ability to stimulate deep brain structures. Transcranial focused ultrasound (tFUS) suppression of the amygdala has the potential as an ideal therapeutic due to the combination of depth and precision. Today, unfortunately, it lacks validation of the focality of stimulation. Magnetic resonance acoustic radiation force impulse (MR-ARFI) imaging is a technique that can accurately image the tFUS focus by visualizing the micrometer displacement of tissue due to ultrasound. However, MR-ARFI has never before been shown in-vivo in the human brain. This proposal will show for the first time MR-ARFI imaging in humans. Therefore, in Specific Aim 1, MR-ARFI imaging will be acquired in an anthropomorphic phantom. These MR sequence parameters and ultrasound parameters will form the basis of Specific Aim 2, which will show in-vivo amygdala targeting that the location of the ultrasonic focus as predicted by MR-ARFI imaging will be in the area predicted by modeling software. In Specific Aim 3A and 3B, we will show that the more accurately the ultrasound targets the amygdala, the greater the reduction in amygdalar perfusion and anxiety rating scores will be. The described research will form part of the fellowship training plan, providing the fellow with training in MR sequence design, ultrasound parameter design, phantom MR imaging, human subjects imaging, and analysis of human imaging and behavioral data.. The entirety of the fellowship training plan (including the proposed research project) will take place at UCLA. It will supplement the fellow’s training as part of the UCLA-Caltech MSTP. The training plan will be jointly supervised by Drs. Susan Bookheimer and Martin Monti, forming the basis of the fellow’s dissertation. It will give him the skills and training to be an outstanding clinician-scientist.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Project: Despite advances in HIV treatment programs, marginalized groups, including mobile men, continue to struggle to remain in care. Mobility is associated with worse antiretroviral treatment (ART) retention and viral suppression throughout sub-Saharan Africa. However, mobility is an essential livelihood strategy for many men living with HIV (MLHIV). In the ReMIT study (“Reducing Mobility-associated Interruption in Treatment among Men in Malawi”), we will refine and pilot an intervention to improve ART outcomes among mobile MLHIV in Malawi. In Aim 1, we will use qualitative data from MLHIV, healthcare workers, and a community advisory board to refine an intervention including multi-month dispensing, a hotline for mobile MLHIV, and mobility- specific counseling. In Aim 2, we will conduct a pilot cluster randomized control trial at 6 health facilities with 240 mobile male ART clients (n=120 per arm) to test the intervention refined in Aim 1. Outcomes include a preliminary estimate of the intervention’s effectiveness on ART retention (6-month retention - primary outcome) and acceptability and feasibility of the intervention. Results of the pilot will inform a future, definitive trial. Candidate background: I am trained as a physician and board certified in Internal Medicine & Pediatrics and am transitioning to a career in global health research. In addition to my medical degree, I have a master’s in public administration in international development (MPA/ID) and have worked in Malawi, Mozambique, and Kenya. Career goals: My long-term career goal is to become an independent investigator with expertise in health system interventions to improve access to care among vulnerable populations in sub-Saharan Africa. Career development plan: The proposed K01 award will provide protected time, practical experience, and training resources to develop my skills in (1) health systems interventions, (2) mobile populations, (3) trial design and analysis, and (4) ethics in global health research and decolonizing global health. Environment: The study will be conducted as part of an ongoing collaboration between the University of California Los Angeles (UCLA) and Partners in Hope (PIH), a medical and research organization, based in Malawi. PIH is the prime awardee on a US President’s Emergency Plan for AIDS Relief (PEPFAR) grant to support 123 health facilities throughout Malawi, providing staff mentorship and capacity building, treatment and care, and monitoring and evaluation of HIV and TB services. PIH and UCLA have a 15-year relationship using a Multiple PI model that has resulted in completion of 20 clinical trials and implementation science studies.

NSF Awards · FY 2024 · 2024-09

The transition to adulthood is an important period of human development that is characterized by changes in nearly every area of life, including education, friendships, and family life. Despite the importance of this developmental period, little is known about how neurobiological and contextual factors support transition towards greater independence in adulthood. This study advances understanding of factors that support successful transition from adolescence to adulthood by longitudinally measuring brain development, social support, and global functioning (wellbeing in terms of school, work, relationships, and health) in a diverse sample of youth 17-19 years of age. This project uses a prospective longitudinal multi-method design to examine factors that promote healthy development during transition to adulthood. Researchers use functional magnetic resonance imaging (fMRI), behavioral measures and questionnaires to test the prediction that (1) neural function associated with extreme levels (too little or too much) of reward sensitivity and tolerance for ambiguous threat is associated with worse global functioning relative to moderate levels of reward sensitivity and tolerance for ambiguous threat, and that (2) social support from parents and friends moderates the link between neural sensitivity to reward and ambiguous threat and global functioning. The project examines these factors in a sample of young people as they transition out of high school or General Education Development programs, following project participants for a period of three years. The outcomes of this research advance understanding of how young people move toward healthy adult lives, and can inform policies and programs aimed at supporting emerging adult independence and success. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Estimates of the magnitude of sea-level rise largely come from computer models that predict ice-sheet behavior. For these models to accurately predict sea-level rise, physical ice-sheet processes must be properly represented. Accurate predictions of ice-sheet behavior and the magnitude of sea-level rise are critical for decision makers. This project will simulate the complete disappearance of the Laurentide Ice Sheet that once covered much of North America. The retreat of the Laurentide Ice Sheet that began about 20,000 years ago presents a natural test case to better understand 1) the physical driving mechanisms that result in the complete disappearance of an ice sheet, and 2) the rate at which an ice sheet disappears. Lessons learned from this research will provide key insights into how large-scale ice-sheet retreat occurs which has clear relevance for predicting the future evolution of Earth’s vulnerable extant ice masses, such as the Greenland and West Antarctic ice sheets. A key question in ice-sheet science is determining how the influence of dynamic mass loss changes through time for a retreating ice sheet and what might control this variability. Lessons from prior episodes of ice-sheet retreat in the geologic record can elucidate how the role of dynamic mass loss changes with time in a fluctuating climate and improve ice-sheet model performance. The researchers will use the next-generation state-of-the-art Ice Sheet System and Sea-level Model (ISSM) to explore the disappearance of the Laurentide Ice Sheet over the last 20,000+ years. In a first-of-its-kind application, ISSM will be used at a spatial resolution capable of capturing large-scale ice-streaming and ice discharge through narrow fjords, along with implementation of coupled solid-Earth-sea-level feedbacks to investigate the role of dynamic ice discharge in driving the disappearance of the Laurentide Ice Sheet. To test model performance, the researchers will compare simulations of Laurentide Ice Sheet retreat against both existing and new geologic benchmarks generated over the course of this project. This project will provide the first quantitative estimates of how the percentage of dynamic mass loss versus surface mass balance evolved over the course of a full deglacial sequence and how this evolution influenced the fate of the Laurentide Ice Sheet. The project will develop a new collaboration with California State University, Long Beach, which is a designated minority serving institution, to recruit students from the Los Angeles area for internships at NASA’s Jet Propulsion Laboratory by leveraging an existing program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Water scarcity is a growing challenge for many regions, particularly in areas like California, where competition for water among cities, farms and the environment is intensifying amid increasing climate variability. Projects to intentionally infiltrate water into underground formations (aquifers), a strategy called managed aquifer recharge (MAR), are getting traction as an alternative to surface reservoirs, because they can provide long-term water storage and water resilience to groundwater-dependent communities. Properly implemented, MAR can enhance communities’ access to safe and reliable drinking water, lower costs, reduce flood risks, and provide ecosystem benefits. However, the development of infrastructure projects by highly fragmented water management institutions has often excluded groundwater-dependent, rural communities from decision-making, resulting in inequitable and contested outcomes. This project’s goal is to analyze the role of decentralized investment partnerships in developing aquifer storage infrastructure that promotes economic productivity and environmental benefits, while enhancing community access to safe and reliable drinking water under a changing climate. The results will be presented to communities, water suppliers and state decision makers to evaluate and model political responses and improve the overall societal outcomes. The resulting decision-making framework for MAR development, accounting for political feedbacks, will provide useful direction to regions throughout the Western U.S. and beyond. The project will generate a better understanding of the socio-environmental systems surrounding water management in agriculture-intensive regions. The project will analyze the role of decentralized, multi-stakeholder, investment partnerships in developing aquifer storage infrastructure that promotes economic productivity and environmental benefits, while enhancing community access to safe and reliable drinking water under a changing climate. The research will clarify how decentralized investment partnerships can promote economic productivity and environmental benefits, while enhancing community access to safe and reliable drinking water under a changing climate. The research will integrate political and institutional feedbacks into simulation-based water resources models that can inform decision making in the challenging context of western U.S. water management. The project will use a co-production framework that involves collaborating with fourteen cooperating partners representing a diverse array of stakeholders, to advance theory and produce actionable results related to: water portfolio design, collaboration risk in large-scale infrastructure partnerships, multi-objective assessment of MAR benefits under uncertainty, and political advocacy by groundwater-dependent, rural communities during periods of institutional development and change. Research dissemination will include policymaker briefings, stakeholder workshops and interactive web content. The resulting decision-making framework for MAR development accounting for political feedbacks will provide useful direction to regions throughout the Western U.S. and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

People often put off unpleasant tasks and responsibilities for far too long, a phenomenon known as procrastination. In addition to its detrimental effects on individuals' health, wealth, relationships, education, and well-being, procrastination costs the U.S. economy about $10,000 per employee every year. How do people decide when to start working on a task or project? Why do they procrastinate more in some settings than in others? What makes some of us more susceptible to procrastination than others? And what are the most effective strategies to overcome procrastination? This project answers these interrelated fundamental research questions, with the eventual goal of translating these research insights into practical application in educational and professional settings. People who procrastinate are repeatedly confronted with the decision of whether to work on a task they have been putting off or do something else. This project combines behavioral experiments with mathematical modeling to achieve two main goals. The first goal is to understand how people make these decisions. The second goal is to devise and test potential solutions to the problem of procrastination. One series of experiments investigates the effectiveness of different ways to incentivize people to complete a series of repetitive tasks on time. A second series of experiments investigates which goal-setting strategies are most effective at helping people overcome procrastination and how well people understand the benefits of goal-setting. The third part of the project uses advanced computational models of decision-making, questionnaires, and behavioral experiments to identify different types of procrastinators and understand how and why they differ from each other. This project strengthens the scientific foundation for preventing and reducing procrastination in professional and educational settings through strategies such as goal-setting and creating incentives that motivate people. 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 Psychiatric disorders remain a leading cause of disability in the US and are associated with increased morbidity and mortality. Early detection and treatment is essential to improving long-term outcomes, yet a substantial proportion of patients with psychiatric complaints experience long diagnostic odysseys before receiving an appropriate diagnosis and initiating effective treatment. “Learning health care systems” aim to short-circuit this slow process by leveraging the diagnostic, treatment, and utilization patterns left behind in “big data” (e.g., clinical, genomic, and social determinants of health) to more efficiently and accurately match the right patient with the right diagnosis/treatment, at the right time. Furthermore, over the past several years, a new paradigm– precision medicine–has moved to the forefront of biomedical research and clinical practice. Precision medicine has been defined as “an approach to disease treatment and prevention that seeks to maximize effectiveness by taking into account individual variability in genes, environment, and lifestyle.” Since its inception in 2018, the mission of the PsycheMERGE network has been to advance precision psychiatry in a learning health care system framework. This application, which was developed collaboratively by PsycheMERGE Network members, represents an opportunity for profound advancement of both basic and translational research in precision psychiatry. We propose extending our foundational efforts to now address barriers to scalability, utility of genomic data, clinical application, and translation to clinical practice in a precision psychiatry paradigm. Specifically, Aim 1 creates a nation-wide federated transfer-learning platform for the development of generalizable and bias-aware algorithms. Aim 2 integrates state-of-the-art methods to perform inclusive trans-ancestry genomic analysis of biobank samples and further innovates by leveraging the breadth and depth of medical record data to discover novel biology that can further inform precision psychiatry paradigms. Aim 3 addresses the application of algorithms by focusing on two use cases including (a) differential diagnosis between bipolar disorder 1 and other mood disorders, as well as (b) probabilistic treatment response to antidepressants for acute depressive episodes. Lastly, Aim 4 uses mixed methods to assess the feasibility, utility, and attitudes towards precision psychiatry tools. Our combined sample of clinical EHR data exceeds 29 million individuals and of those, nearly 2 million also have genetic data already available for analysis across the twelve sites included in this application. A cross-cutting theme throughout the application is the intentional focus on equitable performance of algorithms, innovative integration of social determinants of health, and inclusive methods for genomic analyses. The sites included are also representative of many diverse communities across the United States including the East and West Coasts, the South, and the Midwest. This application represents a major step towards equitable precision psychiatry and brings the field closer to the goals outlined in the updated NIMH Strategic plan.

NSF Awards · FY 2024 · 2024-09

With the support of the Chemical Catalysis Program in the Division of Chemistry, Professors Diaconescu and Alexandrova at the University of California at Los Angeles are studying design principles for catalyst development (catalysts are chemical additives that help a reaction occur or proceed more rapidly) by combining theoretical and experimental methodologies. The majority of syntheses are multistep and require several different catalysts to achieve reasonable overall yields. This feature requires time and energy, especially if extensive purification of intermediates is required. In contrast, catalytic systems that are reversibly tuned using external stimuli offer opportunities to carry out multiple transformations in a single step with the same catalyst. Such systems are referred to as “switchable catalysts”. Currently, catalysts that are able to perform in this manner are triggered using prompts that range from light to electrons, protons, and even other molecules (molecular recognition). The Diaconescua and Alexandrova groups are developing redox switchable polymerization catalysts for the formation of biodegradable copolymers by combining machine learning with experiments to generate new polymer structures. In addition to contributing new catalysts and catalyst designs, this project is contributing to the development of human resources in science, technology, and engineering through the education of postdoctoral fellows, graduate students, undergraduates, and high school students including students from diverse backgrounds. By combining their expertise, Professors Diaconescu and Alexandrova are providing students with unique opportunities to learn about the controlled synthesis of polymers, factors that determine the properties of materials and polymers, and the power of machine learning in catalysis and materials science. Professors Diaconescu and Alexandrova are combining theoretical and experimental expertise to design redox switchable polymerization catalysts for the generation of biodegradable polymers. In addition to positively impacting the environment by combining multiple catalytic reactions into a single step, the proposed work is generating biodegradable polymers that have improved properties. Ultimately, this work will lead to biodegradable polymers that are competitive with polymers that come from petroleum derived precursors. The present project is benefitting society by promoting interdisciplinary research, strengthening our fundamental knowledge of catalysis, and generating useful biodegradable polymers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Robots capable of navigating unstructured terrains in diverse environments, such as water and land, are crucial for many real-world applications. While soft robots can navigate challenging environments like narrow tunnels and rough surfaces due to their flexibility, most current designs are limited by slow speeds, reliance on ties to the base unit (i.e., tethered), and use in only one type of environment, such as land or water. Additionally, soft robots are time-consuming and expensive to create compared to rigid robots, which benefit from centuries of innovative generation. This project aims to create a new class of untethered, reconfigurable (i.e., able to change shape), and multimodal amphibious soft robots (URSoRo) assisted by a machine learning (ML) design tool to overcome these limitations. These robots will leverage a new class of soft electromagnetic (EM) actuators that can operate in more than one state, enabling them to swiftly adapt to challenging environments. This project will leverage the reconfigurability of soft robots for environmental adaptation and promote their practical applications, such as search and rescue operations, monitoring of animals and plants, and inspection of infrastructures in extreme environments. Additionally, the project will contribute to an annual inter-university soft robot competition across the United States and integrate findings into graduate-level courses on soft robotics at the University of Michigan, Ann Arbor, and the University of California, Los Angeles. This project addresses two primary challenges in soft robotics: designing shapes and achieving bistability in soft actuators while maintaining a simple, low-cost fabrication process, and tightly integrating and engineering untethered reconfigurable soft robots with fast multimodal locomotion. The research will develop a soft bistable EM actuator with high force output (∼0.4N), high activation frequency (>30 Hz), and the capability to be powered by miniaturized onboard electronics (<15 g). An ML-assisted physics-based simulation tool will be developed to guide the design, fabrication and robotic integration of these EM bistable actuators, enabling a fully planar rapid fabrication process. Liquid metal embedded elastomers will be used to enhance both thermal management and electromagnetic field generation, boosting the actuator's performance. Overall, this project will result in a new class of untethered soft robots driven by soft bistable EM actuators, alongside ML-assisted physics-based modeling and design tools, achieving an unprecedented combination of speed, size, mass, and reconfigurability. By addressing these technical challenges, it will contribute to the field of robotics with versatile, efficient, and cost-effective solutions for creating soft robots with rapid reconfiguration and advanced locomotion performance in unstructured and diverse real-world environments. 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 Facial paralysis from stroke or other neurological disorders often causes loss of the eye-blink reflex, leading to pain and visual disability. Eyelid dysfunction leaves the eye chronically exposed, which is not only painful, but is fundamentally incompatible with functional vision. Further, because eyelid movement plays a critical role in facial expression and human communication, loss of natural eyelid motion can also have negative social and cultural implications. Unfortunately, current surgical management strategies have major limitations in both functionality and appearance. In theory, dynamic natural blink restoration via a facial neuroprosthesis would be an ideal solution; unfortunately, this ideal has proven elusive, due in large part to a lack of knowledge regarding the neurophysiological mechanisms that enable eyelid function. There is a critical need for neuroprostheses that reproduce functionally complete and aesthetically natural eye closure, blink, and other behaviors. In response to this need, our long-term goal is to advance a novel class of neuroprostheses that are informed by a deep understanding of the fundamental neuromechanics of the muscle that controls the eyelid. To achieve our long- term goal, we will first carry out fundamental neuroscientific studies to establish the currently-unknown mechanisms that link segmental muscle activation to eyelid motion and function. The innovation of this work lies in our ability to measure intramuscular activation and three-dimensional eyelid kinematics with unprecedented precision and resolution. This sets our work apart from all other prior research into eyelid function, and will allow us to develop the first predictive dynamic neuromuscular model of the eyelid. In the present work, our objective is to study the neurophysiology of how activation sequences and intensities produce blink and other eyelid behaviors under both healthy and pathological activation. We will accomplish this by first studying eyelid function in persons without paralysis during a range of eyelid behaviors, including spontaneous blink, reflexive blink, and forced closure. As the participants perform these behaviors, we will record high-resolution intramuscular EMG from multiple points within and around the eyelid, while simultaneously tracking the three-dimensional motion of several points along the eyelid margin in high definition. We will use these data to implement a mechanistic neuromuscular model of the eyelid musculature, which can then inform where and when stimulation from future neuroprostheses should be delivered. We will then repeat these experiments in a group of persons with partial facial paralysis, to study the mechanisms by which eyelid function can be compromised. Upon completion of this work, we expect to have established the mechanistic basis for model-informed facial neuroprostheses that restore natural blink. These results are expected to provide the foundation for development and evaluation of neuroprostheses with the potential to improve eye health, vision, and confidence for patients with facial paralysis.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT Traumatic brain injury (TBI) is one of the top causes of death and disability worldwide. However, due to its complexity, it remains one of the most challenging neurological conditions to treat. Advances in our understanding of TBI pathobiology and improvements in clinical care for TBI require multidisciplinary exchange of scientific ideas and encouragement of promising young investigators in the field. Our first Western Neurotrauma Symposium (WNTS) was well received and especially noted for the attention given to the trainees and early investigators and the collaborative environment. The WNTS will continue to be a unique regional conference providing an annual venue for a broad range of neurotraumatology research, with a focus on trainees and early career investigators, presentation of cutting-edge data in a safe environment, multidisciplinary collaborative ideas and a more intimate setting to allow direct interactions between attendees. This meeting will be instrumental in developing the next generation of investigators and ideas, without duplicating or replacing opportunities provided at existing National and International Neurotrauma Symposia. While the focus of continued WNTS funding would be development of regional young investigators and collaborators, attendance at the meeting would be open. The first WNTS showed participation of a more diverse population from centers throughout the Western U.S. with specific outreach and travel awards to encourage active participation of underrepresented groups in neuroscience/neurotrauma research. In 2022, the R13 supported 17 young investigators and trainees, and faculty. In 2023 19 were supported, enhancing diversity at all levels. The additional support provided by renewing this grant for 2024 would specifically enable expanded inclusion of investigators from outside of the UC, bringing in research and clinical trainees and faculty from across the western states, for whom travel, expense and time constraints may limit opportunities for young persons to attend national or international conferences.

NSF Awards · FY 2024 · 2024-09

As mature speakers of a language, humans can produce and understand an indefinite number of sentences. This ability comes from a powerful cognitive system - syntax - whose properties reveal the types of computations that human minds can engage in. One core property is the capacity to encode abstract grammatical dependencies that can hold at a distance. When and how this property emerges in development is an important, outstanding theoretical question. This project examines infants’ representations of non-local syntactic dependencies before their second birthdays, even before they regularly produce full sentences of their own. This investigation illuminates the active syntactic development that occurs during the second year of life. In doing so, this project provides an important step for understanding the origins of the core computational capacities that syntax relies on. This project supports education by providing training opportunities in language sciences research. In addition, the project benefits society because it includes outreach about issues in language development to local families and high school students from under-represented backgrounds. The project focuses on the acquisition of the types of non-local syntactic dependencies in wh-questions, in which a fronted wh-phrase can act as the argument of a verb at a distance (e.g., What did the chef burn). The investigators examine when infants know that an object wh-phrase and a local object of a verb cannot co-occur, because they both express the same argument relation (e.g., *What did the chef burn the pizza). Recent work finds that 18-month-olds, but not younger infants, are aware of this complementary distribution pattern, suggesting awareness of the non-local grammatical dependency between the wh-phrase and the verb. This project provides a set of behavioral experiments that identify more precisely how 18-month-olds represent these dependencies, and the development that occurs prior to this age. Through this case study, this research establishes a firm foundation for the initial steps of syntactic dependency learning in infancy. This empirical foundation provides boundary conditions on theories of when and how initial grammatical knowledge is acquired, illuminating how learning from experience interacts with children’s early capacities for representing the speech they hear. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Quantum computing has seen substantial progress, yet its high hardware error rates hamper its utility. The emerging solution to the problem is the use of sophisticated error correction, which is challenging to implement correctly. This project tackles this challenge at the software level. The project's novelty lies in its development of a verifiable quantum compiler that integrates formal methods with quantum computing. The work involves the automatic synthesis of fault-tolerant quantum programs, ensuring the correctness of quantum programs even after errors occur. The project's impacts are enhancements to the technological capabilities of quantum computing and assurances that these systems can be practically implemented and maintained in real-world applications with high reliability and efficiency. Quantum computing, if realized at scale, offers the potential to transform a variety of high-impact applications, including drug development, materials discovery, traffic optimization, and much more. This project supports the foundation on which these applications will be built. Further, the project's broader impact contributions include integrating project results within the academic curriculum at the investigators' home institutions and broadening participation through mentorship of students from populations underrepresented in computing. Collaborations with industry leaders will also support the adoption of innovative, verified quantum compilers and the development of widely applicable, empirically validated open-source technologies. The technical approach is multifaceted, aiming to advance Fault-Tolerant Quantum Computing by automating the generation of fault-tolerant quantum programs and ensuring their accuracy through rigorous verification. The team leverages complementary expertise in quantum computing and formal methods to address three critical tasks: (1) optimizing the translation of quantum algorithms into fault-tolerant implementations; (2) creating verifiable error detection tailored for quantum environments; and (3) developing decoders that deliver timely feedback to quantum hardware. 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 The goal of this project is to elucidate the behavioral and neural mechanisms that mediate host seeking and host invasion in skin-penetrating, human-parasitic nematodes. Nearly one billion individuals globally are infected with skin-penetrating nematodes, particularly in socioeconomically disadvantaged communities with limited waste management and healthcare infrastructure. Strongyloides stercoralis, the human threadworm, is estimated to infect over 600 million individuals globally, resulting in chronic gastrointestinal infections, respiratory distress, growth delays in children, and fatality in immunosuppressed individuals. Infections are expected to rise with climate change and resistance to available anthelmintic drugs is a growing concern; therefore, elucidating the mechanisms that mediate parasite host seeking and host invasion is crucial for developing novel strategies to prevent and treat individuals infected with S. stercoralis and other parasitic nematodes. Skin-penetrating nematodes freely navigate the environment as developmentally arrested infective third- stage larvae (iL3s) and rely upon sensory cues to seek out and infect new hosts. Previous studies have shown that the iL3s of S. stercoralis and other skin-penetrating nematode species are robustly attracted to host- associated odorants. However, how olfactory cues drive host-seeking and host-invasion behaviors remains poorly understood, and the neural mechanisms that mediate odor-evoked behaviors have not been investigated. This proposal hypothesizes that skin-penetrating nematodes have evolved unique neural and behavioral responses to olfactory cues that facilitate specific host-seeking and host-invasion behaviors. To test this hypothesis, I will investigate the odor-evoked responses of skin-penetrating nematodes at the behavioral and neural levels. I will use S. stercoralis for these experiments because it is a human parasite with direct health relevance and because several genetic manipulation tools have been successfully developed for this species. Experiments proposed in Aim 1 will characterize and quantitatively analyze motility, host-seeking, and host- invasion responses to host-associated and non-host-associated odorants in vitro and ex vivo. Experiment outlined in Aim 2 will identify putative olfactory neurons and utilize in vivo calcium imaging and chemogenetic silencing to functionally characterize parasite-specific olfactory neuron response properties. Collectively, my results will illuminate how skin-penetrating nematodes respond to host-associated odorants and how these responses enable the parasites to specifically target human hosts. This work will provide fundamental insights into the poorly understood field of endoparasite chemosensation and may contribute to the development of novel preventative and treatment methods to control nematode infections.

NSF Awards · FY 2024 · 2024-09

Consistency and reliability are necessary for algorithms used for data analysis and high-consequence decision-making. The project will contribute to this goal by developing algorithms for time-series analysis and anomaly detection that expand capabilities of understanding subtle features from multiple distinct data sources. In addition, this work will advance the spatial reasoning abilities of artificial intelligence algorithms which could be applied to other engineering or scientific problems. The project will train PhD students through involvement in the research. The aim is to develop mathematical algorithms for forecasting time-series, predicting spatial dynamics, and detecting anomalies using a multimodal transformer-based model. The project will construct methods for analyzing systems that switch dynamics or change behaviors. This can be applied to downstream tasks such as data analysis and anomaly detection. The research will address how to utilize contextual information with time series for more reliable predictions and how to consistently incorporate multiple pieces of information and observational modalities into prediction and anomaly detection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Abstract Tuberculosis (TB) is second only to COVID-19 as the most lethal cause of death from a single infectious agent. In 2020, an estimated 10 million people developed TB, nearly half a million of which were infected with drug- resistant tuberculosis (DR-TB). Early detection of infection and drug resistance is critical to controlling DR-TB as this enables rapid engagement into effective care. Unfortunately, only 71% of newly diagnosed TB patients are ever tested for rifampicin resistance, and even fewer receive more comprehensive testing. Additionally, despite improved treatment success rates for DR-TB globally, these success rates do not reflect upstream losses resulting from undiagnosed (missing cases) and untreated patients. Currently, bacterial culture and nucleic acid testing remain the primary methods for diagnosing infection, with smear microscopy being phased out. However, these methods present significant limitations for diagnosing drug resistance such as lengthy time-to-result for phenotypic tests, as well as the need for a priori knowledge of resistance mutations and prohibitive cost for molecular tests. Clearly, there remains a critical need for a fast, accurate, and cost-effective DST, particularly for resource-limited settings. To address this, we propose to design and develop a rapid phenotypic drug susceptibility test that can be easily adapted in TB endemic regions. The trehalose- based DST, termed Tre-DST, is based on novel trehalose probes, which require metabolic conversion to emit fluorescent signals, giving them their unique ability to specifically detect live Mycobacterium tuberculosis (Mtb). Agnostic to mechanism(s) of drug resistance, Tre-DST can be used with all WHO-recommended DRTB drugs, as well as any future TB drugs as a companion diagnostic. In Aim 1, we will develop, characterize, and optimize a family of novel fluorescent trehalose probes (3HC-Tre and RMR-Tre) that are specifically designed to improve performance over DMN-Tre and to distinguish live Mtb, making them ideal as biomarkers of drug susceptibility. We will also evaluate probe specificity to TB and probe performance across a variety of bacteria typically present in oral mucosa. In Aim 2, we will develop and optimize Tre-DST as a multi-drug DST for first- and second-line TB drugs. We will evaluate Tre-DST’s performance in accurately determining drug resistance using drug susceptible and drug resistant Mtb strains, treated singly and in combination with the anti- TB drugs that fulfill the WHO Target Product Profile (TPP) for next generation DST. We will also evaluate several point-of-care (POC)-friendly detection methods to optimize efficiency and cost-effectiveness. Lastly, in Aim 3, we will perform preliminary evaluation using banked clinical isolates in Johannesburg, South Africa to assess the performance of Tre-DST in the field. We will evaluate pre-validated readout methods in this clinical study, benchmarking the performance of Tre-DST against bacterial culture.

NIH Research Projects · FY 2025 · 2024-08

: EMERGE: Early Markers of Expressive and Receptive (language) Growth in Emergent autistic toddlers The majority of 18-24-month-old autistic children have no words, demonstrating significant delays in their language development, a leading source of concern that often brings them to the attention of physicians or other professionals in community settings91,92. About half of these children continue to show significant language delay, speaking no words at 30-33 months2 and exhibiting delays in language greater than expected for their nonverbal cognitive age2. The period of development between 18-30 months is critical for language learning, coinciding with the period of time parents note differences in their children’s development93. We do not understand why some children begin to use words and others do not but speaking early (before 36 months) has long- lasting and cascading effects on development94,95. This may be especially true for children who are diagnosed later, and when diagnosed, often have lower cognitive/intellectual abilities8. Starting out with language delays can limit opportunities for children, tracking them into specialized settings that result in poorer outcomes overall. Understanding why language outcomes diverge over this critical language learning window, is essential to optimize the targets and timing of early, effective interventions. Therefore, a major gap in our knowledge concerns the measures and timing of when we can predict spoken language outcomes of young children with autism. To explore the vast heterogeneity in language outcomes, it will be necessary to deeply phenotype children using a range of concurrent neural and behavioral markers of spoken language and examine how these changes progress over time. This study will be the first to collect simultaneous social communication, language, sensory, motor development, and neural activity (via remote EEG) measures in the homes of families who have typically not been engaged in research studies, which we will do at three distinct times over the 18-30-month window of development. Participants include 132 18-month-old toddlers with autism who screen as having no words at study start. Our outcome will be the total number of novel words on a language sample. This study has the potential to dramatically improve our understanding of language growth among developmentally delayed autistic toddlers. It also addresses a high priority need of the Interagency Autism Coordinating Council and NIH, which includes a focus on minimally verbal, intellectually disabled children and community samples.