University Of California Berkeley

NSF Awards · FY 2024 · 2024-09

This research team will study planet formation by characterizing the carbon monoxide (CO) gas in disks that surround new-born stars. This volatile gas may stick on grains and pebble-sized solids far away from the star within the disk, moving with them as they drift towards the star, where they may be released due to its heat. Using infrared spectroscopy to sense CO in the inner disk and millimeter images to sense CO in the outer disk, the team will probe, respectively, the planet-formation region and where most of the disk material exists as in a ‘reservoir’. Three graduate students will be trained in telescope observing and data modeling at the PhD level, and two undergraduate students will be involved in high level research. Calibrated datasets and open-source codes will be made publicly available. A new course on radio astronomy for undergraduate astronomy majors will be developed. There will be broader outreach to local middle and high schools through summer programs and campus visits, and to the community at large through public events. Recent observations and simulations suggest that CO chemistry is being impacted by dynamical and chemical processing. Patterns between the derived CO column densities in the terrestrial planet forming zone (e.g., < 5 au) and in the outer bulk gas reservoir probed at ALMA-wavelengths will be sought. The program will use data from world-class ground-based observatories: the Atacama Large Millimeter Array (ALMA) in Chile; and the Keck observatory and the Infrared Telescope Facility in Hawaii. The collaboration leverages the strengths of the three participating institutions to provide the most complete picture to date of the volatile content of protoplanetary disks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

ABSTRACT Accurate estimates of adult death rates are critical to good science and health policy. In most high-income countries, adult death rates are directly calculated since civil registration systems record every death. But in low- and middle-income countries (LMICs)--including most of Asia and Africa–civil registration systems are weak. Estimates from high quality surveys could be a promising source of information about adult mortality in these countries, but unfortunately, decades of research have revealed that estimating adult death rates from a survey is extremely challenging. A major obstacle is that current methods require many thousands of interviews per survey. Large surveys are prohibitively expensive, and they make it difficult to innovate and improve methods over time. As a result, there is a critical lack of evidence about adult mortality in LMICs. This is a problem: without accurate adult death rates, researchers cannot evaluate the impact of policies intended to confront deadly pandemics such as AIDS and COVID-19, quantify the pace of population aging, produce population projections that inform social and economic policy, or even directly measure life expectancy. This project will develop a new set of statistical and data collection tools make it possible to collect information about adult mortality using surveys with moderate sample sizes (n ≈ 1,000-2,000), thereby greatly expanding the potential sources of data available to understand adult mortality in LMICs. Aim 1 will develop new statistical methods, and apply them to already-collected pilot data; estimated death rates will be compared to a gold standard, allowing for errors and accuracy to be calculated for each method. Aim 2 will collect new qualitative data to help explain why the best-performing methods were successful in this setting (as revealed by Aim 1); and help generate hypotheses that will form the basis for a larger-scale, multi-site test of these methods in the future. The findings will form the basis of a new website and support other tools for disseminating our results. The results will produce tools that can be used to increase the amount of evidence available about adult death rates around the world.

NSF Awards · FY 2024 · 2024-09

Floods, fires, pandemics and other catastrophic risks are a major worldwide concern in the light of climate change, urbanization, and an increasingly complex and interconnected world. These risks are challenging to prevent and manage because they tend to have impacts that cascade in surprising and unexpected ways across communities, political jurisdictions and economic sectors. To address this challenge in a sustainable and equitable way, risk-prone regions must take account of the complex interdependence of infrastructures, organizations and communities operating at different levels and scales of governance. This project develops a strategy of collaborative catastrophic risk modeling to help risk-prone regions collectively recognize, understand, and hopefully act upon their interdependence. A range of approaches have been used to model catastrophic risk. This project is distinctive in that it aims to demonstrate how these models can be developed in collaboration with regional stakeholders. A collaborative approach is expected to both increase the practical relevance of risk models and enhance regional efforts to address catastrophic risks. Catastrophic risk management poses a fundamental challenge: the difficulty of dampening down escalating events or spillover effects from one part of a system to another. This challenge has been referred to as the problem of “cascading risks,” which have been observed to occur in electrical blackouts, infectious disease outbreaks, extreme weather events, natural hazard spillovers into industrial failures, landslides, and floods. Although this challenge is gaining increasing recognition, there is still limited understanding of how risk-prone regions can be encouraged to work together to prevent and manage these cascading interactions. This project develops a methodology of collaborative risk modeling to bring regional stakeholders together to identify and analyze catastrophic risks. While already a recognized tool for analyzing the dynamic nature of cascading risks, catastrophic risk modeling has not previously been used as framework to advance system-scale governance, a concept that acknowledges that risk reduction for large-scale hazard events necessarily occurs at micro-, meso- and macro-scales and includes public, private and nonprofit stakeholders working together A methodology of collaborative risk modeling not only provides valuable input into the development of useful models, but also creates an opportunity for regional stakeholders--particularly vulnerable communities–to collectively perceive and address regional risks. 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

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. Regulatory agencies and governance structures make significant decisions about which clean energy investments get made, where they are deployed, what costs are incurred, and how those costs are recovered. This project aims to improve the effectiveness and social impact of clean energy investments in the power sector. The research considers interactions between regulatory processes and energy infrastructure investments in three key contexts. First, an examination of how renewable energy resources are permitted, sited, and deployed across the United States, focusing on the impact of land use restrictions and policy. Second, identification of social and human-centered barriers to expanding transmission capacity and how to address them. Third, an assessment of the affordability and reliability implications of strategies for reducing wildfire risk on the electric grid. This includes estimating the avoided damages from wildfires and studying how current regulations affect utility investments in wildfire mitigation. Stakeholders are involved throughout the project to help identify and evaluate policy modifications based on the findings. This project investigates the siting and permitting of wind and solar electricity generation, the deployment of electricity transmission infrastructure, and power sector investments in climate change adaptation and wildfire risk mitigation. It pursues a three-part approach within each of these contexts. First, an assessment of how infrastructure investments are currently being regulated, incentivized, and deployed. Second, using a convergent research approach, integration of economics and engineering methodologies and analytical tools to develop new metrics and frameworks for evaluating these investments from the perspective of investors, electricity consumers, and society. Third, in collaboration with stakeholders, proposed changes in regulatory frameworks to support more efficient climate transition decisions. This SAI project unites economists, engineers, and policy experts to tackle critical challenges in overhauling power system infrastructure, utilizing advanced methods that require deep knowledge of technology, regulatory landscapes, power systems engineering, econometrics, and machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Large-scale AI models that generate text and images are transforming visual synthesis and recognition tasks. These models can not only generate realistic images from texts but also serve as backbone models for object recognition, tracking, and segmentation. Despite their capabilities, they are complex, challenging to interpret, and sometimes unpredictable. This unpredictability is problematic for safety-critical applications such as autonomous driving and healthcare. Moreover, these models can easily generate copyrighted content, produce harmful content, or amplify stereotypes. Understanding such complex models, with billions of parameters trained on billions of images, remains challenging. Key questions include understanding why certain input prompts lead to failures or artifacts, how these models might amplify biases, and how the training data can impact output quality. This project seeks to provide a systematic, interpretable, rational basis for understanding and controlling the learned computations of multimodal generative models, with the potential to increase the accountable and safe use of state-of-the-art multimodal AI models and mitigate potential harms. To effectively disseminate the research, the investigators will freely release all materials (code, models, and datasets) and host tutorials, workshops, and courses to engage with the research community, enhance students’ participation at the K12, undergraduate, and graduate levels, and engage with policymakers to inform them of the latest technology and future trends. The project aims to develop a new systematic framework for visualizing, understanding, and rewriting the learned computation of multimodal generative models and leverage this knowledge to trace how the training data used influences the internal representation and, ultimately, affects the model outputs. The project will focus on three research thrusts. First, new research methodologies will be developed to visualize the internal mechanisms and the hierarchical structures of pre-trained multimodal generative models, understand their roles in different stages of the generation process, and extract visual concepts and their relationships. Second, the project will explore several model editing algorithms to manipulate these discovered concepts and relationships to pinpoint and fix inconsistencies, failures, biases, and safety concerns of existing multimodal generative models. Finally, the investigators will develop new attribution methods to assess the influence of training images on generated results based on the analysis of internal representations and show several potential applications of the attribution algorithms. 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 Humans move their eyes toward relevant information when making decisions, especially when making social inferences (e.g. inferences on emotion, trustworthiness, danger, etc.). While previous research has investigated how human gaze patterns are guided by task demands and stimulus features, little is known about how humans, especially those with abnormal gaze patterns, make eye movements toward socially relevant information in a dynamic environment. This is due to much of the previous literature investigating eye movements in social cognitive tasks with the use of static stimuli of facial expressions, leaving much to be desired regarding the generalizability and ecological validity of previous research. Many novel computational and analytical methods can be used to process dynamic eye-tracking data, such as inter-subject correlations of gaze patterns between observers that can reveal task engagement and information processing. Thus, in order to mimic the complex social environment that humans experience in their everyday lives, more naturalistic experimental designs and stimuli should be implemented by researchers. Advancements in this area of research can help scientists understand how eye movements are influenced during dynamic social cognitive tasks, especially in individuals with known abnormal gaze patterns such as those with depression, autism spectrum disorder, and schizophrenia. F99 Phase: The current project uses a novel dynamic emotion tracking task called Inferential Emotion Tracking in which observers must continuously track the emotion of a target character in a video (e.g. Hollywood movie, home video, or documentary clip) while observers' eye movements are recorded. This emotion-tracking paradigm can capture large amounts of data from continuous emotion ratings and eye-tracking. More importantly, the use of dynamic and context-rich stimuli to investigate human social cognition can more accurately measure the cognitive mechanisms that humans use to infer emotion outside of a laboratory setting. Additionally, using eye tracking while participants complete this emotion perception task will allow us to access where and when observers look for important emotional cues or information to inform their judgments. Investigating how humans shift their visual attention during social cognitive tasks will provide valuable insights into how social visual attention is impacted in vulnerable populations. The training involved in this phase will include instruction in computational methods related to computer vision models and the neuroscience of emotion processing to prepare for the K00 phase. K00 Phase: This phase would include extending my F99 Phase study by investigating the neural mechanisms of visual attention during complex social cognitive tasks by using fMRI. This approach could reveal which areas of the brain are involved during the processing of emotions with the use of dynamic and context-rich stimuli and aims to improve our understanding of how these areas are impaired in vulnerable populations. Multiple computational methods can be used to investigate social visual attention with the use of dynamic stimuli and fMRI which can include using within-brain methods (e.g. event coding, reverse correlation, functional connectivity) and between-brain methods (e.g inter-subject correlations, inter-subject functional connectivity, shared representations). The training plan for this phase of the proposal will include developing expertise in the social neuroscience literature through the mentorship of a prominent sponsor and training in computational methods involving fMRI analysis.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY To move successfully, we must be able to sense our bodies and our environment. This is no more evident than when participants attempt to strike a match to light a candle after their hand’s somatosensation has been numbed. Despite being able to see well, participants fumble with the match and clumsily attempt to accomplish the task. Further, somatosensory-motor integration is notably impaired in many types of neurological movement disorders including Parkinson’s disease, stroke, essential tremor, and dystonia. Developing rehabilitation or therapeutic strategies to improve somatosensory-motor integration faces a central challenge: while the theoretical importance of somatosensory-motor integration (SMI) is clear, how the brain actually processes afferent signals to enable excellent movement control remains unclear. In this proposal, we seek to use a population neural dynamics framework to understand how the brain implements SMI computations. Specifically, we will leverage a novel feedback-dependent dexterous manipulation task, high-density multi-area recordings, and an innovative type of electrical stimulation that can be used to modulate inter-area communication in the macaque monkey. These combined experiments and analysis are designed to uncover how SMI computations, long known to be critical for movement control, are implemented in population neural activity patterns in brain. Further, they will pave the way for developing approaches for restoring damaged SMI in the brain after brain injury or neurodegenerative disease. The main experimental approach of this proposal includes simultaneous high-density, high channel-count (Neuropixel) electrophysiological recordings from the primary motor cortex (M1), primary somatosensory cortex (S1), and cerebellar-receiving motor thalamus (mThal) in non-human primates that are learning and executing a dexterous manipulation task. The main analytical approach includes modeling the timeseries of motor cortical population activity as a combination of intrinsic motor cortical dynamics and inputs from S1 and mThal. The hypothesis of this proposal is that S1 and mThal exert different influences on M1 population dynamics throughout the learning and execution process. Specifically, we hypothesize that S1 provides specific sensory updates that drive M1 population activity, and mThal modulates the temporal dynamics of M1 population activity. We will further develop evidence for or against this hypothesis by leveraging a novel neuromodulation approach that can boost or interrupt communication between distant brain regions. Completion of this proposal will constitute an understanding of how SMI computations, that have long been thought to be essential for movement, are actually implemented in the circuitry and population dynamics of the macaque sensorimotor system. This advance would improve our understanding of how to target somatosensory nodes to improve control of movement following motor impairment.

NIH Research Projects · FY 2025 · 2024-09

Obesity, the accumulation of excess white adipose tissue (WAT), has become an epidemic and is associated with chronic metabolic diseases, such as type 2 diabetes. While WAT serves primarily as an energy storage organ, brown adipose tissue (BAT) dissipates energy by generating heat mainly via UCP1 for maintenance of body temperature. Increasing BAT activity by activating UCP1 may promote energy expenditure to combat obesity. We have been studying transcriptional activation of UCP1 and thermogenic gene program and previously found two transcription factors, Zfp516 and Zc3h10 and recruitment of their coregulators PRDM16/LSD1 and Dot1L, respectively, critical for activation of UCP1 and thermogenic program. In continuing our efforts to understand UCP1 transcription, our recent ATAC-seq and 3C-based crosslinking identified a potential new enhancer far upstream of UCP1 locus. We will study this region to act as an enhancer by examining histone modification, UCP1 promoter activation, and eRNA production, as well as in the genomic context by CRISPRa and CRISPRi. Moreover, we will examine the involvement of an associated transcription factor for its enhancer function. Finally, we will assess the impact of this enhancer and the transcription factor on thermogenesis, adiposity, and glucose/insulin homeostasis in vivo in mice. Overall, elucidating the role of a previously uncharacterized far upstream UCP1 enhancer and the associated factor will help us to fully understand UCP1 transcription and thus thermogenesis. Our studies may provide new obesity/diabetes therapeutics in the future. 1

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Massive amounts of genomic data are currently being generated, providing unprecedented opportunities for biomedical researchers to characterize various biological components and processes. In order to utilize these data to make new biological discoveries and improve human health, accurate models and scalable computational tools need to be developed to facilitate analysis and interpretation. The central objective of this project is to address this challenge by developing more realistic probabilistic models, scalable algorithms, and user-friendly software tools to enable the biomedical research community to better harness large genomic data. Many prob- lems in genomics rely on computational methods for inferring genealogical information from large sequence data and interpreting the reconstructed trees. In this application, we propose to make significant strides towards im- proving this line of research by developing a suite of robust and scalable algorithms for probabilistic models of molecular evolution and genealogical inference across multiple timescales. We will achieve our goal by carrying out the following specific aims: 1) A fundamental problem in statistical analysis of molecular evolution is estimat- ing model parameters, for which maximum likelihood estimation (MLE) is typically employed. Unfortunately, MLE is a computationally expensive task, in some cases prohibitively so. In Aim 1, we will tackle this problem by combining a novel MLE framework and modern optimization techniques to develop a broadly applicable computational method that achieves several orders of magnitude speedup in MLE for general models of molecular evolution. The ability to estimate model parameters at unprecedented speed will transform the way that phylogenetic analysis is performed and enable the community to consider more complex, realistic models than previously possible. We will apply our tools to improve phylogenetic inference for two clinically important superfamilies of membrane proteins in humans, namely G protein-coupled receptors and Solute carrier trans- porters. 2) Because of meiotic recombination, the genetic variability within humans cannot be represented by a single tree. Instead, there are millions of different trees across the genome, where each position in the genome will tend to have its own tree that differs only minimally from the trees in nearby sites. The collection of all these trees, and the set of recombination points creating new trees, is represented by the Ancestral Recombination Graph (ARG), which has a number of applications in human genetics. Despite substantial recent progress on reconstructing ARGs, however, current methods are either too slow to scale up to large data sets, or they do not sample ARGs accurately from the correct posterior distribution. In Aim 2, we will develop a new computational method to improve ARG sampling. We will test the method extensively on simulated data, develop a number of applications, and generate genome-wide ARGs for several human data sets to facilitate biological discoveries. 3) Applications of genealogical inference methods have been rapidly growing in single-cell genomics. In particular, advances in CRISPR/Cas9 genome editing technologies have enabled lineage tracing for thousands of cells in vivo, and the problem of reconstructing trees from such data has received considerable attention recently. In Aim 3, we will develop scalable algorithms to reconstruct time-resolved single-cell trees for thousands of cells sampled at multiple time points. We will also develop a novel statistical method grounded in rigorous the- ory to improve tree-based fitness inference. We will apply the methods developed here to study cancer evolution as well as B cell affinity maturation in germinal centers.

NIH Research Projects · FY 2025 · 2024-09

Pioneering work by our MPI team established biomechanical signals as a critical driver of brown adipose tissue (BAT) function. We demonstrated a new signaling cascade in interscapular BAT that integrates ß-adrenergic signaling, PKA activation, Ca2+ release via L-type calcium channels, and myosin light chain kinase 1 (MLCK1) activation. MLCK1 in turn stimulates force generation by myosin 7 (Myh7, a.k.a. cardiac/skeletal myosin heavy chain beta) and translocation of YAP and TAZ to the nucleus, leading to greater UCP1 expression and enhanced thermogenesis. Besides classical BAT, inducible beige adipocytes (BeAT) can also contribute to non-shivering thermogenesis by forming UCP1-positive cells within white adipose tissue (WAT) depots and are thought to be major drivers of non-shivering thermogenesis in adult humans. While BA are also activated by ß-adrenergic signals, we found that BeAT do not express Myh7, raising the question of whether BeAT thermogenesis is driven by contractile signals or some distinct mechanism. Here, we postulate that biointerfacial and biomechanical forces regulate b-adrenergic receptor (b-AR) signaling in BeAT to link catecholamine signaling to BeAT differentiation and induction of thermogenesis via Myh9-driven tension. Further, we propose that extracellular matrix (ECM) modulates ß-AR signaling by integrating, at the level of FAK, myosin-based intracellular tension signals with biomechanical extracellular matrix clues and outside-in integrin signaling resulting in spatio-temporal control of BeAT induction.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Pregnancy is a critical period of development: stress during pregnancy can lead to adverse birth and long-term health outcomes, as well as compromise infant development. In the United States, racial inequities in pregnancy and birth outcomes are stark and persistent, signaling the need for novel interventions that address the social determinants of health. The California Abundant Birth Project (CA-ABP) is a guaranteed income program for pregnant populations at elevated risk of preterm birth in five California counties (San Francisco, Alameda, Contra Costa, Riverside, and Los Angeles), funded by the State of California, municipal governments, and philanthropic funding. The CA-ABP program will provide 12-18 months of unconditional, monthly income supplements during pregnancy and postpartum to randomly selected participants, with the goal of curbing financial stress and promoting healthy pregnancy outcomes. The goal of the CA-ABP Evaluation is to use a rigorous, mixed-methods, community-based participatory research approach to determine whether providing guaranteed income to Black pregnant people can advance perinatal health equity through the reduction of adverse pregnancy outcomes and stress, and improvements in maternal mental health and infant development. The proposed project significantly expands the scope of the impact evaluation of CA-ABP by adding three additional survey data collection timepoints (6-weeks postpartum, 6- months postpartum, and 12-months postpartum) and medical records abstraction. The study focuses on the subset of Black-identified participants to determine the impact of income supplementation during pregnancy on: (1) perinatal health, as measured by composite measures of adverse pregnancy outcomes (preterm birth, low birth weight, small-for-gestational age, gestational hypertension, preeclampsia, and gestational diabetes); (2) stress and perinatal mental health (depression and anxiety); and (3) infant development. Eliminating maternal and infant health inequities requires a paradigm shift in research, interventions, and policies that emphasize upstream solutions rather than placing responsibility on affected communities. Understanding the impact of CA-ABP on perinatal health and infant development will provide the foundation for interventions that address stress and economic inequality caused by structural racism. Conducting the evaluation in partnership with the organizations implementing ABP provides a direct pathway to move forward the results for policy change and sustainability.

NIH Research Projects · FY 2025 · 2024-09

Candidate: I am an epidemiologist in the Division of Biostatistics at the University of California, Berkeley School of Public Health, and I completed my Ph.D. in Epidemiology in August 2022 at UC Berkeley. Since my graduation, I have worked with the Center for Targeted Machine Learning and Causal Inference to apply cutting-edge biostatistical and causal inference methods to pressing research questions using electronic health record (EHR) data from the National Clinical Cohort Collaborative (N3C). I aim to become a leader in applying innovative biostatistical, causal inference, and machine learning methods to address impactful research questions in infectious disease epidemiology using electronic health record data. Environment: To achieve my career goals, my training and mentorship plan will focus on recent advances in biostatistics, causal inference, and data science methods, as well as infectious disease epidemiology. I have assembled an interdisciplinary team of expert biostatisticians, epidemiologists, and clinicians who will support my training. Alan Hubbard (primary mentor) and Mark van der Laan (co-mentor) will provide expert guidance and mentorship on biostatistics, data science, and causal inference. Rena Patel (co-mentor) and Jack Colford (scientific advisor) will provide mentorship and guidance in infectious disease epidemiology. Research: Researchers and clinicians have made significant progress in understanding, preventing, and treating the acute stages of viral infections. However, there is considerable uncertainty regarding interventions to prevent or treat the long-term sequelae of viral infections, which are a growing source of morbidity and mortality. In this project, I will evaluate the effectiveness of several promising medications and interventions in preventing long-term sequelae and mortality following viral infection. In Aim 1, I will evaluate the impact of diabetes medications (metformin and GLP-1 receptor agonists) on mortality and long-term symptomatology among patients with comorbid diabetes and viral infection. In Aim 2, I will assess the impact of interleukin-6 modulating drugs (tocilizumab and sarilumab) on mortality and long-term sequelae in a cohort of patients with comorbid moderate rheumatoid arthritis and viral infection. In Aim 3, I will develop, evaluate, and disseminate advanced methods for applying causal inference, machine learning, and biostatistics to EHR data. In Aim 4, I will evaluate the relationship between vaccination timing, relative to acute infection, and long-term sequelae of infection, in order to determine an optimized vaccination schedule. I will apply targeted machine learning methods to achieve these aims, which will prepare me for an R01-level application to utilize these methods in research questions related to infectious disease epidemiology and analysis of EHR data.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY Cryogenic electron microscopy can generate atomic resolution views of cells and is therefore a promising technology to study the molecular mechanisms underlying key cellular processes in cells. However, cryo-EM images cannot be interpreted directly to atomic resolution because cryo-EM imaging introduces radiation damage to biological macromolecules. Current strategies to address radiation damage require combining multiple copies to generate a single 3D reconstruction representing an average of the individual molecules. This not suitable for cellular imaging because only a select few macromolecules are present in sufficiently high numbers to generate an interpretable average. As a postdoc I contributed to the development of an alternate, single molecule strategy to characterize the structure, interactions, and organization of macromolecular complexes in cryo-EM images called 2D template matching (2DTM). Since 2DTM does not require averaging, it has the potential to simultaneously visualize all cellular complexes individually, presenting a potentially revolutionary new understanding of cellular function in health and disease. However, the potential of 2DTM is currently limited by the difficulty to localize smaller complexes. With support from the NIH New Innovators Program, I will pursue new strategies to extend 2DTM to detect and characterize smaller structures. New technologies, particularly new imaging technologies, open new avenues of research because they allow us to ask questions and see things that were never previously possible. Opening the cell to visualization at the resolution of individual macromolecules will undoubtably reveal new, previously unanticipated biology.

NSF Awards · FY 2024 · 2024-09

Artificial Intelligence (AI) applications to cosmological observations could provide significant improvements to our understanding of the universe. However, when it comes to analysis of real data there have been very few successes; real data contain many additional complications that need to be accounted for. This research program will develop AI methods that are sufficiently realistic to be applied to actual cosmological data of the next generation of surveys. The research team will focus on weak lensing surveys, where we learn about the universe from the distortions of the galaxy images, which allows us to trace the dark matter through its gravitational effect on the galaxy images. The research program will engage a diverse group of students, and the lessons learned will be incorporated in the curriculum of Physics and Data Science education programs. One of the promising AI venues is Simulation Based Inference, which aims to extract information from the data via simulations. The most common approach is discriminative learning, which first summarizes the information in the data via data compression, followed by their emulation to obtain the cosmological parameters of interest. An alternative is to learn the field level data likelihood directly using generative learning. The goal of this proposal is to develop and compare these AI methods and apply them to actual weak lensing data of the next generation. The investigators will pursue Multiscale Normalizing Flows in the context of generative learning, and compare to Neural Network based compression architectures in the context of discriminative learning, to develop optimal methods for upcoming weak lensing surveys in terms of precision, reliability and robustness. 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

Learning mathematical concepts is an essential STEM skill. Although the capability to learn mathematics is found across human cultures, how mathematical skills depend on universal, underlying cognitive processes is still not understood. This project seeks to uncover the core cognitive underpinnings of children’s ability to learn and reason about arithmetic and probability by examining mathematics acquisition across cultures with differential exposure to pedagogical practices targeting early numeracy. By examining these abilities in a culture with relatively fewer supports for learning mathematics, the indigenous Tsimane’ peoples of South America, this project will be able to disentangle culture and human nature as factors in learners’ success, and correspondingly uncover likely targets for future educational innovations. In addition to arithmetic, this project tackles early understanding of probability in these groups in order to provide a strong test of developmental hypotheses that thinking probabilistically is at the core of how children form an understanding of the world. This study will first characterize indigenous methods for arithmetic, beginning with addition, then expanding to multiplication, division, and fractions using field experimental methods as well as structured interviews. Both of these components will quantify the algorithms and techniques that individual learners develop for solving mathematical problems using state of the art machine learning tools. This will formalize the systems of knowledge that learners acquire and allow us to create a rigorous theory of how this knowledge changes with schooling, culture, and development. Building on work with fractions and division, we will then test Tsimane’ children on their understanding of probability in order to explore whether probabilistic inferences are a shared human ability, even in the absence of formal schooling or strong cultural support for number. The final aim of these studies tests children’s ability to update probabilities in the face of evidence, as in Bayesian inference, which is hypothesized to be a core mechanism of cognitive change. This theory makes a strong prediction that learners in any culture should succeed on tasks requiring probabilistic thinking. By testing a particularly remote population with few cultural supports for these abilities, this project will be able to determine whether such abilities are a shared part of human nature that supports cognitive development more broadly, and in turn whether these cognitive skills would be a promising target for educational interventions. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. 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

Electronics are ubiquitous in modern society, driven by remarkable innovation and fast paced technological development in the semiconductor industry. Accordingly, the density of transistors in integrated circuits has been continuously increasing. With the most recent 3 nm- and 2 nm-node technologies, the number of transistor units on one single chip of fingernail size approaches 50 billion. Consequently, nanoscale thermal management of densely packed devices is an important concern. Recent advances in nanoscience and nanotechnology have enabled both theoretical descriptions and experimental interrogations of new heat transfer mechanisms. However, as devices approach fundamental laws of physics, it has become challenging for conventional thermal characterization tools to comprehensively understand the behaviors of energy carriers and measure the temperature distribution. Addressing this challenge, the research will develop new methods to probe energy carriers and analyze thermal transport in nanomaterials by combining ultrafast optical measurements with nanoscale spatial resolution. This project will enable characterization of high-performance optoelectronic devices and the development of new thermal management methods for future semiconductor technology. The goal of the research is to develop a new methodology for investigating nanoscale thermal transport phenomena in both equilibrium and non-equilibrium regimes. By coupling temporally modulated laser beams of femtosecond pulse duration with tip-based scanning near-field optical microscopy, pump-probe ultrafast nanoscopy will be demonstrated and applied to study energy carriers (electrons and phonons) with ultrahigh temporal resolution (below 100 fs) and beyond-diffraction-limit spatial resolution (on the order of 10 nm). The project will focus on the following aims: (i) validating the ultrafast nanoscopy as a powerful experimental platform for probing nanomaterials with high spatiotemporal resolutions; (ii) incorporating spatial separation of the pump excitation from the probe tip to measure energy carrier transport; (iii) developing a methodological framework to distinguish different microscopic degrees of freedom and elucidate electron-phonon interactions in different material systems. Fundamental scientific concepts and experimental methods developed in the project will enable new strategies for nanoscale thermal management in the next-generation electronics. 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 level of fertility in the United States has fallen consistently since the end of the 2000s, reaching a low of 1.6 children born per woman in 2023. Maintaining the current population requires a birth rate of 2.1 children per woman, so the present birth rate implies that the United States population is shrinking. If this trend continues, it could threaten the country’s economic prosperity and the financial stability of the social security system as there will not be enough working-age adults to support an increasing older population. It is important to understand the reasons behind the fertility decline and monitor future trends. However, research has been limited due to a lack of data. This project addresses this issue by creating a database of fertility indicators for each state and the District of Columbia for every year since 1959, and for each county since 1982. The database is freely accessible to the scientific community and the public, helping researchers and policy makers study changes in reproductive behavior and study how local economic and social factors influence the level of fertility. The data series created by this project provides both period- and cohort-based fertility indicators that are commonly used by demographers to study fertility patterns. The primary data sources are from the National Center for Health Statistics, which records all births occurring every year in the United States, and the Census Bureau’s annual estimates of the number of women of reproductive age. Basic demographic techniques are used to produce state-level fertility indicators. However, these methods are not appropriate for county-level estimates due to the small populations in many counties and associated large year-to-year random fluctuations. To address this problem, the project uses a statistical model based on previous research that takes advantage of the regularity in age patterns in fertility and leverages the fact that demographic behavior typically changes very gradually over time and space. 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

SUMMARY/ABSTRACT A longstanding challenge in neuroscience is how to monitor neuronal signaling and activity at cellular resolution over a large area/volume with millisecond time resolution. At high frame rates, conventional two-photon fluorescence microscopy (2PFM) methods are limited in their achievable fields of view by their point-scanning mechanisms. Although camera-based one-photon widefield fluorescence microscopy (1PWM) can be used to image fast activity events at kilohertz frame rates, it requires sparse fluorescence labeling/excitation and is limited to superficial tissue depths. Previously, we implemented an all-optical and passive ultrafast laser-scanning technique called free-space angular-chirp-enhanced delay (FACED) in 2PFM. Capable of generating millions of line scans per second, our FACED module has enabled 2PFM to image large fields of view at kilohertz frame rates. Applying FACED 2PFM to the mouse brain in vivo, we recorded supra- and sub-threshold electrical activity and characterized ultrafast cortical blood flow dynamics deep below the brain surface. Here, we aim to further optimize FACED technology to maximize its accessibility and impact. First, we will develop a FACED module with fully motorized positioning and active beam stabilization, to enable alignment-free operation with flexible configuration. We will also build software support for FACED data acquisition into ScanImage, a widely used 2PFM control program. Together, the development of user-friendly hardware and software will facilitate the adoption of FACED in labs that specialize in biological applications. Next, we will expand the impact of FACED on fast neural imaging into the mesoscale by integrating a FACED module into a two-photon mesoscope, with the resulting 10-30× speed gain enabling the recording of neuronal activity over 20 mm2 in vivo. Finally, to overcome the limitations of 1PWM in voltage imaging, we will build a kilohertz-frame-rate single-photon confocal microscope using FACED to enable voltage imaging in both sparsely and densely labeled brains, while developing new far-red voltage sensors to maximize the imaging depth of this approach. The proposed work will substantially broaden the impact of FACED technology by making it accessible to and capable of answering a wide variety of neuroscience questions.

NIH Research Projects · FY 2024 · 2024-09

Project Summary/Abstract Recognition of peptide antigens presented by major histocompatibility complexes (MHCs) by T cell receptors is a fundamental molecular mechanism by which T cells sense and respond to foreign antigens such as viral proteins and mutant oncoproteins. Yet many cancer-specific antigens are poorly presented by common MHC alleles, greatly limiting the ability of T cells to be mobilized against patient tumors. Methods to increase the MHC-I presentation of cancer specific oncogenic driver mutations could greatly augment anti-cancer immune responses. Conversely, misrecognition of self-antigens by T cells breaks the immune tolerance and leads to the destruction of healthy tissue, a process that underlies multiple autoimmune diseases. Genetic studies have revealed strong linkages between specific MHC alleles and autoimmune disease risk (e.g. HLA- B*27:05/ankylosing spondylitis, HLA-DQ2/celiac disease and type 1 diabetes), providing a strong rationale to therapeutically target these MHC alleles. Although MHC proteins have not been targeted by small molecule drugs, recent studies on idiosyncratic drug hypersensitivity reactions uncovered the propensity of MHC proteins to accommodate drug-like compounds. Here we seek to exploit this understanding to develop small molecule ligands of MHC proteins for the treatment of cancer and autoimmune diseases. By leveraging a new high throughput assay we have established to monitor compound stabilization of MHC proteins as well as their covalent reactivity, we propose to develop small molecules that 1) enhance the presentation of cancer antigens by MHC Class I proteins via a molecule glue mechanism, 2) block the antigen presentation by autoimmune- associated MHC Class I proteins, and 3) allosterically perturb the function of autoimmune-associated MHC Class II proteins. The proposed research program will deliver novel chemical matter to enable new therapeutic mechanisms for cancer and autoimmune diseases. In addition, the methods we develop here will lay the foundation for the systematic discovery of small molecules that modulate of MHC-mediated antigen presentation.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Abstract The vast diversity in lifespans among organisms provides a remarkable natural experiment in which to explore the evolutionary innovations that have shaped the extensive variation of this phenotype. Non-human primates represent a critical taxon for understanding the evolution of human lifespan due to their close phylogenetic proximity and broad range of lifespans. One key hallmark of aging is genomic instability and the accumulation of somatic mutations. Only recent improvements in the accuracy of genome sequencing have enabled the study of these mutations in aging tissues. A subset of these mutations confer growth advantages driving carcinogenesis, however emerging evidence also highlights their role in several other age-associated diseases and potentially aging itself. We recently demonstrated that somatic mutation rates are inversely correlated with lifespan in mammals, consistent with a potential causal role in aging. Furthermore, comparative genomics analyses we and others have performed across species with diverse lifespans have determined that DNA repair genes are key players in adaptations to long life. Here, we propose to characterize somatic mutational landscapes of aging in primate species spanning 70 million years of evolution. These maps will be generated across 10 tissue types from a collection of marmosets, macaques, baboons, chimpanzees and humans of diverse ages and both sexes using ultra-accurate NanoSeq mutation profiling. For a subset of tissues we will also perform PacBio long read sequencing to assess somatic structural variants. These data will be supplemented with NanoSeq profiling of several cell types from 17 additional primate species. Together, these maps will allow us to ascertain how somatic mutational processes, including mutation rates and signatures, vary with age across primate species with diverse life spans and life histories. As a result of these somatic mutations as we age many tissues also become colonized by clonal expansions of positively selected mutant cells. To determine how these clonal dynamics relate to species lifespan we will next perform deep targeted-NanoSeq on 250 genes, known to drive clonal expansions in humans, across individuals of different ages from 8 of the aforementioned species. These analyses will permit identification of the mutations driving clonal expansions, the rate at which these expansions occur during aging, and spatial clonal dynamics across the lifespan in diverse species and tissues, informing our understanding of how they contribute to aging. Finally, we will seek to identify the species-specific genetic determinants of these somatic mutation phenotypes. We will perform a phylo-GWAS analysis to identify the genes associated with differences in mutational patterns among species using both general phenotypes (e.g. lifespan, body size) as well as the molecular mutational phenotypes identified in aims 1 and 2 (e.g. mutation rates, spectra, and clonal dynamics). These comparative genomics approaches will allow us to link both broad primate phenotypes and quantified molecular phenotypes to the evolutionary innovations that have impacted lifespan in the primate clade. Together these multi- scale evolutionary comparisons will reveal factors that contribute to the diversity of life spans across primates and to ultimately identify novel targets for interventions to extend human health span.

NIH Research Projects · FY 2025 · 2024-09

All eukaryotes relies on calcium ion (Ca2+) as a second messenger to respond to internal and external signals. Defects in Ca2+-based signaling cause numerous human diseases. Despite the importance and broad medical implications, Ca2+ signaling mechanisms remain unclear. The challenging question concerns how Ca2+ encodes specific information coming from different primary signals and translate them into distinct responses at cellular, organ, and organismal levels. Encoding and decoding the specificity of Ca2+ signals have thus remain a long- standing puzzle in the signal transduction field. The PI’s laboratory studies Ca2+ signaling mechanisms using a model plant (Arabidopsis). Discoveries in Arabidopsis have established new conceptual framework applicable to Ca2+ signaling mechanisms in all eukaryotes and set the stage for this application. The breakthrough in Ca2+- encoding mechanism in plant innate immunity has demonstrated that plants and animals both recognize bacterial pathogens by cell surface receptors, followed by Ca2+ second messenger that activates the immune responses, providing a unified paradigm in Ca2+-mediated defense signaling in eukaryotes. The guided pollen tube elongation resembles axon guidance and fungal hyphae growth in that all these polarized cell growth processes involve specific Ca2+ oscillations orchestrated by multiple Ca2+ channels and transporters. The discovery of a new family of Ca2+ channels in male-female recognition prior to fertilization reinforces a general paradigm that Ca2+ signaling is a common language in cell-cell communication during reproduction. In the next five years, PI’s research will expand beyond the current project to include several new directions. In addition to continuing the studies on cyclic nucleotide-gated channels (CNGCs) in the contexts of pollen tube growth and innate immunity in the current project, the new program will include research on the newly discovered Ca2+ channels in reproductive cell death of male and female cells, root mechano-sensing, and plant-fungal interactions. The overall goals are to identify the influx Ca2+ channels and efflux transporters that control the dynamic changes in cytosolic Ca2+ levels, uncover regulatory mechanisms how these Ca2+-transporting proteins are regulated by external cues, and piece together signaling pathways that couple signals to responses in specific physiological contexts. The general approach is to utilize genetic tools to identify genes responsible for each signaling pathway, followed by electrophysiology and cell biology procedures to reconstitute the pathway in a cellular context, completed with biochemical and structural methods to evaluate structure-function relationship of gene products and deduce mechanistic processes. Such combination of tools has been proved to be highly effective and successful in dissecting the molecular mechanisms for Ca2+-encoding in PI’s ongoing research. Completion of the project will reveal new Ca2+ encoding mechanisms, contributing to the conceptual framework of Ca2+ signaling, which is highly relevant to human health.

NIH Research Projects · FY 2024 · 2024-09

Abstract The ease of access to quantitative imaging technologies such as laser-scanning confocal microscopy has ushered in a revolution in our understanding of the inner workings of cells. However, despite the subcellular resolution afforded by such microscopes, confocal microscopes cannot image with such resolution over the large volumes of cells that reside in the context of multicellular organisms, large organoids, and tissue explants without significant photobleaching or photodestruction. This grant seeks to acquire a light-sheet microscope that is optimized for fast 3D multi-sample volumetric imaging of delicate live specimens in their native environment, the Bruker Luxendo TruLive3D Imager. The TruLive3D is especially suited for multi-position imaging of mouse preimplantation embryos, fly embryos and zebrafish embryos, enabling rapid time-lapse, high-throughput imaging experiments using live specimens. The TruLive 3D imager is particularly appealing to our User Group comprised of developmental biologists, cell biologists, and molecular biologists—all experts in imaging— who aim to overcome the current imaging limitation to define and characterize novel cellular processes. The optical concept of the TruLive3D, the dual-sided illumination and single-lens detection from below, along with the unique design of the sample holding vessels, and the ability to multiplex, enable easy-to-use, extremely fast high-resolution imaging with minimal phototoxicity. Additionally, the combined improvements in image quality, sensitivity, temporal resolution, and reduced bleaching greatly improve the accuracy of image segmentation, tracking, and measurement of signaling dynamics, cell movements, and cell divisions. TruLive3D’s unique multi-well sample holder makes loading and imaging of multiple live specimens easy and quick, allowing for prolonged imaging experiments of otherwise challenging to image samples. The Trulive3D is one of the most versatile systems that can advance our cellular and subcellular understanding of various developmental, physiological and pathological processes. Labs working on fly, zebrafish and mammalian embryogenesis and human/mouse organoids can benefit from this light sheet microscope system to image readouts of signaling dynamics, subcellular dynamics, and cell behaviors in the context of diverse developmental and physiological processes in live specimen. Our User Group includes Drs. Lin He, Ian Swinburne, Hernan Garcia, Xavier Darzacq, Rebecca Heald, Samantha Lewis, David Bilder, Ellen Robey, Sanjay Kumar, Richard Harland, Teresa Puthussery and Mengmeng Fu, who all require this system in order to continue their important research. Most of these investigators are in the Molecular and Cell Biology department, as well as from Physics, Bioengineering, and the School of Optometry, signaling that this will be a useful instrument across the campus.

NSF Awards · FY 2024 · 2024-09

Rapid habitat loss affects many species including non-human primates. Although some non-human primate species are known for their ability to persist in degraded habitats, little is known about how living in degraded habitats affects their well-being, demography, and ecological role. In response to this knowledge gap, this doctoral dissertation project examines a non-human primate species' ecological context, its responses to environmental disturbances, and the cascading effects that such responses have on its interactions with other species and its ecological role. The researchers study a non-human primate species that lives in diverse environments (protected, moderately disturbed, and highly disturbed). The study collects demographic (population size), individual size (morphometric), body composition, and fecal content data. Additionally, the researchers assess this species contribution as a seed disperser in these environments. Mathematical models and computer simulations are applied to evaluate the impact of the potential extinction of this species in these ecosystems. The study promotes international collaboration and provides training and educational opportunities for students at different levels (elementary school to PhD). The study analyzes the combined impacts of natural and anthropogenic disturbances on the population and body mass of a non-human primate species, using a capture-mark-release technique, morphometric surveys, and fecal analyses. The study assesses the impacts of these changes on this species’ mutualistic interactions with plants, using direct examinations of several aspects of their seed dispersal services. Based on the collected demographic and dietary data, the study applies mathematical models and computer simulations to identify the effects of this species extirpation from these habitats. 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

Nontechnical description: Terahertz (THz) technology holds great potential for ultrahigh-bandwidth communication, non-intrusive medical and security imaging, and quantum materials spectroscopy. However, its widespread application is hindered by the lack of high-efficiency and high-power terahertz sources. One promising approach is the use of photoconductive THz emitters, which can, in principle, achieve an optical-to-THz power conversion efficiency beyond 100% by harnessing power from a DC electrical bias. Unfortunately, the physical properties of commonly used semiconductor materials for THz generation have low breakdown electrical field, restricting the realization of the THz generation efficiency. The project aims to develop innovative GaN-based terahertz (THz) photoconductive emitters with an optical-to-terahertz energy conversion efficiency approaching or even surpassing 100%. It leverages the unique properties of the wide bandgap GaN material, which exhibits a very large breakdown electrical field and a high saturation electron velocity. The project will also advance the understanding of photophysics, carrier dynamics, and THz coupling in wide bandgap GaN. In addition, it provides an active learning environment for graduate and undergraduate students, who will drive the frontier of science and technology in the future. Technical description: This project aims to enhance our fundamental understanding of the photophysics and ultrafast electron dynamics within GaN. Building upon this knowledge, the PI will design, characterize, and optimize highly efficient GaN photoconductive emitters with integrated THz nanoantennas. The research efforts will focus on three core directions: (I) Development of GaN photoconductive switches with a high breakdown electrical field approaching the intrinsic limit. (2) Investigation of ultrafast carrier dynamics, transient current, and THz generation in on-chip GaN THz devices. (3) Design and demonstration of energy-efficient and high-power free-space THz emitters. The proposed project will pave the way for energy-efficient THz emitters with an optical-to-THz energy conversion efficiency beyond 100%. Such power-efficient THz emitters hold the potential to facilitate the widespread adoption of novel THz technologies. Furthermore, this research will foster an interdisciplinary learning environment, providing graduate and undergraduate students with an opportunity to explore state-of-the-art nanofabrication, materials development, and advanced optical characterization techniques. 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

Over the last decade, K-12 science education has seen the development and surge in comprehensive high-quality instructional materials designed to address standards aligned with the Framework for K-12 Science Education. Meanwhile, localization—organizing instruction around local phenomena and incorporating students’ social, cultural, and linguistic resources—has been proposed as a way to better connect science instruction to students’ interests and the priorities of their local communities. Both localization and high-quality instructional materials have been found to support equitable learning opportunities and outcomes in K-12 science education. However, there are many unresolved questions in science education about how to best leverage the advantages of localization in the context of high-quality instructional materials, which are typically developed at a national scale. This project will support a conference series, including an in-person gathering and virtual follow-up meetings, that will bring together teachers, researchers, education leaders, and instructional material designers to investigate these questions. Participants will come together to build a shared understanding of how to integrate the use of high-quality instructional materials with the benefits of localizing these materials to better address students’ contexts and backgrounds. By fostering dialogue, sharing models, and setting priorities for future research and design, the project seeks to build knowledge about inclusive, effective and culturally responsive approaches to science instruction that will advance equitable science education in K–12 classrooms. This one-year conference project primarily focuses on the promise and challenges involved in integrating localization with comprehensive high-quality instructional materials to enhance equitable learning outcomes in K-12 science education. The project will organize an in-person conference followed by a series of four follow-up virtual meetings involving a diverse group of fifty participants, including teachers, district leaders, state education agency leaders, researchers, and instructional materials designers. Methods will include collaborative discussions, presentations of existing models, and evidence-based analyses to clarify definitions and identify priorities for future research and design efforts. The outcomes will be a proposed research and design agenda for the localization of high-quality instructional materials, along with practical examples and models of current approaches. These outcomes will be disseminated beyond the conference, targeting practitioners, designers, and researchers through co-authored conference presentations and publications, as well as shared via teacher social media, newsletters, and professional learning communities. By addressing the inherent tension between national-scale usability of high-quality instructional materials and the need for culturally and locally relevant instruction, this project aims to spur innovation and contribute to the development of truly equitable science instructional materials, ultimately advancing the field of science education. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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.