University Of California Berkeley

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Several neurophysiological disorders are linked to inappropriate levels of serotonin (5-HT) and dopamine (DA), and treatments for these disorders are typically designed around re-adjusting the levels of their respective neurotransmitter. 5-HT and DA play complex roles in the nervous system, yet the precise functions that they serve across various brain regions and within specific neural circuits is still poorly understood. A deeper understanding of how these two monoamines modulate circuit function will likely improve the development of safer and more effective therapies for 5-HT- and DA-related disorders. This proposal seeks to understand the modulatory roles that both 5-HT and DA serve in a very well-defined and tractable neural circuit, the head direction (HD) network of the fruit fly Drosophila melanogaster. In the fly HD network, heading representation is stored in the activity of E-PG neurons, whose dendrites tile a donut-shaped neuropil region of the brain called the ellipsoid body (EB); this arrangement remarkably mimics a compass needle, wherein a ‘bump’ of E-PG activity swings around the EB as the fly turns. E-PG bump position is updated in part by sensory information transmitted through different classes of ring neurons. Each ring neuron sends projections uniformly throughout the EB, contacting E-PGs at every position of the “compass,” strongly indicating that ring–E-PG connections are plastic: without different synaptic weights between ring and E-PG neurons, sensory inputs would lose spatial information. Prior work by my sponsor demonstrated that ring–E-PG connections are indeed plastic, and that dopaminergic input to the EB, via the ExR2 neurons, enhances plasticity during fly turns, when spatial information is presumably rich. Another set of neurons, called ExR3, are serotonergic and have strong projections to the EB, and RNA sequencing data suggests that E-PGs and at least one class of ring neuron express 5-HT receptors. However, the function of 5-HT in this network remains unexplored. In Aim 1, I will test the hypothesis that ExR3 release of 5-HT is important for ring–E-PG plasticity. I will measure the likelihood that a new angular offset between the E-PG bump position and the visual scene can be established (i.e., if ‘re- mapping’ can occur), using an optogenetic ‘write-in’ protocol, and I will ask whether gain or loss of 5-HT can enhance or inhibit successful re-mapping. Then, I will measure calcium activity of ExR3 neurons while flies fictively navigate in VR to determine the behavioral correlates of ExR3 activity, when 5-HT-mediated plasticity is likely to be evoked. In Aim 2, I will test the hypothesis that DA and 5-HT, via ExR2 and ExR3, modulate the activities of distinct classes of ring neurons and thereby transiently adjust relative sensory cue importance in updating the compass at behaviorally relevant moments. To test this, I will first survey the distribution of both 5- HT and DA receptors among the different classes of ring neurons using in situ hybridization. I will then ask whether activation or inhibition of ExR2 and ExR3 can bias the HD network’s preference toward one of two discordant sensory cues, revealing a 5-HT- or DA-mediated shift in cue preference.

NSF Awards · FY 2025 · 2025-09

Genome engineering holds great promise for producing new plant varieties better able to feed the increasing global population. In many species, this approach requires selectively coaxing small clumps of cells carrying DNA sequences of interest to regenerate into whole plants with well-organized shoots and roots. Although regeneration is readily achieved and allows for successful genome engineering in some plant species, regeneration is still too low-throughput or prohibitively difficult in many crops. A critical challenge then is defining and overcoming the barriers that frustrate regeneration in these species. The proposed research will address this problem by investigating why whole plant regeneration is so hard to achieve in sunflower, a globally significant oil and confectionary seed crop. Specifically, the responses of cultivated sunflower cells and tissues to various experimental regeneration conditions will be characterized, and these observations will be compared to the responses of more easily regenerated relatives in the sunflower family. These results will then be leveraged to design experimental interventions with the goal of overcoming sunflower’s barriers to regeneration. Thus, the proposed research will deliver a toolkit of solutions for a major crop that currently lacks modern biotechnology tools to validate gene functions and accelerate breeding for traits essential to sunflower biology and agricultural production. The findings and methods from the proposed research will be communicated to the sunflower research and plant biotechnology communities through a workshop and online materials. Multiple early-career researchers will receive cross-disciplinary training over the course of the project. Recent successes in overcoming barriers to plant regeneration have been achieved by directly manipulating the transcription factors that promote shoot initiation and patterning. These gains have come largely in transformable species, rather than in species recalcitrant to regeneration, in which additional undetermined inhibitory mechanisms may be active. The proposed research seeks to define the gene regulatory programs that inhibit regeneration and test experimental means to overcome them in Helianthus annuus, the common sunflower. Using both tissue-level analyses and single nucleus transcriptomics, the project will characterize the meristematic cell types and gene expression patterns that arise during regeneration in the absence and presence of various regulators of shoot development. A comparative experimental approach will be taken to assess how growth and transcriptional responses to treatments with regeneration-promoting developmental regulators differ between regeneration-recalcitrant sunflower and two readily regenerable relatives in the Asteraceae family. Experiments will also be performed with both wild and domesticated sunflower accessions to test whether changes that evolved in developmental gene networks during domestication are a major contributor to cultivated sunflower’s recalcitrance to regeneration. Insights gained from identifying the molecular mechanisms that inhibit regeneration in sunflower and from additional plans for methods development will contribute to broader efforts in plant biotechnology and support transformation in other recalcitrant species. Together, the proposed research will not only deliver long sought biotechnology solutions for plant breeding in a major crop, but the findings will also support development of new techniques to engineer other species important to the bioeconomy. 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 2025 · 2025-09

Earthquakes are known to occur at plate boundaries, yet these natural hazards can also occur within stable continental regions beyond the plate boundary, such as in eastern North America. The hazard and risk posed by earthquakes in eastern North America is great because the region includes half of the top ten most populous metropolitan areas in the United States and generates 25% of its GDP. Though earthquakes are rare in the region, the recent April 2024 Mw4.8 earthquake in New Jersey highlights the importance of studying earthquake seismicity within stable continental regions. However, the earthquake rate in eastern North America is low, and sparse seismic networks have hampered progress in understanding the nature of faults and the earthquakes they produce. In this project, scientists will develop new machine-learning and cross-correlation methods to detect previously undetected earthquake events at a significantly lower magnitude detection threshold and higher location precision compared to existing catalogs, providing fundamental new data to study seismogenesis and seismotectonics in eastern North America at a broad range of spatial scales. This project aims to significantly improve on and expand currently available catalogs of earthquake parameters (including location, magnitude, focal mechanism) for eastern North America by applying advanced machine-learning and cross-correlation based earthquake detection and characterization methods to decades of continuous waveforms recorded in the region. The instrumentally recorded seismicity in eastern North America is sparse and typically only complete down to ~M2.5 and often higher in regions of sparse instrumentation. The new high-resolution, deep-magnitude earthquake catalog will include many previously undetected events that are expected to illuminate active faults at depths, providing new data and new insight into seismotectonics, fault mechanics, earthquake generation, and the stress conditions under which faults fail. The project harnesses the availability of long seismic archives, recent game-changing developments in event detection and characterization, and a recent Mw4.8 event in New Jersey that serves for ground-truthing both methods and new knowledge gained from the data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The world's oceans are changing rapidly, resulting in still poorly understood impacts on ocean ecosystems, fisheries yields, and carbon storage in the deep ocean. One obvious place to start investigating these impacts is on the most abundant vertebrates on earth, yet these taxa are so obscure that even most biologists are unaware of them. Lanternfishes and bristlemouths dominate the twilight zone in oceans around the world and make up nearly half of all vertebrate biomass on earth. They number in the quadrillions or higher and form a crucial link in the marine food web as primary consumers of zooplankton and food for commercial fisheries. They also connect surface waters where they feed at night with carbon export to the deep ocean during the day through the sinking of their fecal pellets and dead bodies. This organic carbon that reaches the deep ocean then remains stored for hundreds to thousands of years. Therefore, it is vital to understand any impacts on these crucial links in the marine food web. This research will provide the first investigation of how our rapidly changing oceans are affecting foundational vertebrate taxa in global marine food webs. This will create fundamental knowledge about how the most abundant vertebrates are adapting, changing population sizes, shifting their diets, and contributing to carbon storage in response to changing oceans around the world. In addition, the outcomes of this research will be shared through the Berkeley Center for Ocean Futures and K-12 outreach. Research-based courses will provide experimental design and statistical training to undergraduates. All specimens and associated data will be catalogued in the Museum of Vertebrate Zoology and Scripps Institute of Oceanography Ichthyology collections for use by the global scientific community. This research will create genomic and phylogenetic resources for lanternfishes and bristlemouths, determine how they are adapting to changing oceans, and predict the resulting impact on marine food webs and carbon sequestration in the deep ocean. The investigators will use large-scale genomic sequencing of samples from around the world to determine if populations are adapting to warming or acidification from shared signatures of selection on related genetic pathways, such as calcification, thermal tolerance, or heat shock proteins. This research will also produce a genome-scale tree of life for these groups, new de novo genome assemblies, and reconstruct recent changes in their population sizes. The investigators will additionally use µCT scans of specimens from museum collections to detect changes in bone density through time associated with ocean acidification and predict how it will affect their feeding ability. Stomach contents from modern and historical museum specimens will be compared to detect changes in marine food webs over time. Finally, the investigators will develop a new approach to understand how much carbon these taxa are exporting to the deep ocean by DNA-barcoding their gut microbiomes and comparing the unique signatures of lanternfishes and bristlemouths to the composition of free-floating particulate organic matter, known as marine snow, at different depths. 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 2025 · 2025-09

Elementary particles interact, which means they impart forces on each other that can attract or repel. These forces determine how matter is built up from the fundamental particles, from atomic nuclei and atoms to molecules and solids. The fine-structure constant is a number that specifies the strength of electrical forces. Precision measurements of the fine-structure constant are important for many areas of physics, as they provide one of the most precise tests of the standard model of particle physics. These measurements can also be used to search for new particles. In 2018, with NSF support, the PI and coworkers published one of the most accurate measurements of the fine-structure constant to date. Subsequently, they have built a new apparatus with the goal of making an even more accurate measurement. In this new project they will complete this measurement by introducing three innovations that are intended to speed up progress and increase precision. As a broader benefit, this work will train undergraduate and graduate students in the technologies that enable quantum information science. This experiment functions by using photons to impart momentum kicks on atoms, and measuring the speed of the resulting atomic motion. From these measurements the fine-structure constant can be deduced. In this updated version of the experiment, the PI and coworkers will introduce three innovations. First, they will introduce a small temporal offset between two measurements that are usually done simultaneously. This will enable them to direct all of the laser power to just one of these measurements at a time, allowing them to use a larger laser-beam diameter. Second, they will make the paths taken by the atoms more symmetric, which will increase precision. Third, they will perform detailed modeling of the atoms’ paths to understand possible sources of measurement errors. 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 2025 · 2025-09

This award funds the research activities of Professors Hitoshi Murayama and Lawrence J. Hall at University of California, Berkeley. Major advances are expected in understanding the laws of physics never probed before, with new data coming from the Large Hadron Collider in Europe (which is currently the highest-energy colliding beam facility in the world), from underground experiments in South Dakota, and from other laboratories around the world. Additional data is also expected from precision experiments at Fermilab (the Fermi National Accelerator Laboratory in Illinois), with additional insights coming from deepening connections to experiments in astrophysics and cosmology. Research in theory is needed to tie all of these results together into a coherent framework. In this project, Professors Murayama and Hall aim to uncover deep secrets that span the range from the smallest scales of particles and strings to the largest scales of the Universe. The scope of their research will include collider physics, dark matter, neutrinos, quark flavor, the phenomenology and theory of supersymmetry, observational cosmology, gravitational waves, strongly coupled quantum field theories, and the multiverse. It is also expected that new collaborations will emerge on these topics. Professors Murayama and Hall will also be active in public outreach and training the next generation of researchers as well as members of the general scientific workforce, thereby situating this work within the national interest. More technically, Professor Murayama will study non-perturbative dynamics of strongly coupled gauge theories, exploiting their exact solutions in the supersymmetric limit together with anomaly-mediated supersymmetry breaking. This method has allowed people in the community to test many ideas concerning non-perturbative physics, including axions, phase transitions, oblique confinement, etc. Professor Murayama will also build models of dark matter and axions covering different parameter sets relevant for experiments. Professor Hall will work on developing new models of flavor, both at the grand-unification and multi-TeV scales. These models will lead to new results for precision flavor physics experiments. Such results can also potentially address the strong CP problem without an axion. In addition, Professor Hall will incorporate the multiverse paradigm together with symmetries in order to understand the origins of flavor and neutrino mass. 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 2025 · 2025-09

Liquid crystal elastomers are soft, rubber-like materials that contain special molecules called mesogens. Mesogens have unique properties that allow them to be manipulated with external forces, independently from the host polymer. Being able to directly manipulate mesogens gives rise to materials that are supersoft, can dissipate large amounts of energy, and can even function as actuators. Potential applications include biomedical implants with programmable dissipation, architected vibration isolators, helmet liners for football players, motorcycle riders and war fighters, and actuators for soft robotics. To date, most work on liquid crystal elastomers has been performed on material systems whose manufacturing is difficult to scale to the industrial setting. This project proposes to experimentally probe the mesogen scale processes that occur in liquid crystal elastomers, which will be made via economical and scalable batch mixing and cross-linking processes. The plan includes testing materials in several complex states of deformation and developing testable mathematical models for the material’s behavior. All data and numerical implementations of these models will be made findable, shareable, and publicly available through data repositories and code hosting platforms, allowing for the effective design of engineering products that leverage the unique properties of liquid crystal elastomers. Lastly, the project will integrate undergraduate and high-school students in various aspects of the research to promote the development of the American STEM workforce, as well as engage in outreach to improve public scientific literacy and engagement. Liquid crystal elastomers have been widely promoted as possible materials for soft actuators and high damping materials. Past work on materials and models for engineering design with liquid crystal elastomers has been heavily concentrated on mono-domain materials that require special fabrication steps, which are difficult to scale to industrial settings. More promising from an economic perspective are poly-domain materials that can be made in simple batch processes. In this project, a panel of mono and poly-domain materials will be made using the same chemistry. The materials will be subjected to uniaxial tension, compression, biaxial extension, and plane strain compression, all while monitoring their director fields. The resulting data will be used to calibrate a viscoelastic mono-domain model, which will subsequently be utilized in a representative volume element (RVE) model of the polydomain materials. The RVE model will then be exercised as a tool to develop a continuum level polydomain model based on a variational modeling structure that employs a spatially averaged free energy function and dissipation function, together with a generalized Biot evolution law. 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 2025 · 2025-09

In this project, artificial intelligence (AI) will be used to design new sustainable polymeric materials with a range of properties and that can be recycled without the need for costly and inefficient separation from mixed waste streams. Today’s plastic waste challenge exists at a scale of megatons per day across tens of thousands of applications and products. The researchers will create new types of depolymerizable plastics derived from simple feedstocks, and they will develop physics-informed AI models to aid design of these plastics such that they meet a variety of product specifications across a wide range of properties. Ultimately, this approach enables products of all different types, functions, and lifetimes to be integrated into a single recycling stream and accelerates their discovery-to-use timeline. The results and methods developed by this research will be publicly accessible for industrial benchmarking and include code and tutorials for users to perform AI-guided design on their own materials. Through this research, a new generation of scientists will be trained to work at the emerging intersection of polymer materials design and AI model development and use. With this award, the project will develop physics-informed AI and synthesize architecturally varied and deconstructable (ADD) polymers by cationic ring-opening polymerization (CROP) with controlled chain length, branching, and dynamic bond incorporation. This work will create new synthetic strategies to control chain end and side chain functionality, branch type and frequency, and dynamic bond incorporation for polymers produced by CROP. Using polyacetals and polyethers synthesized from a select few monomers, these complex molecular architectures will be linked to key properties using physics-informed AI, which both describes polymer architecture through sets of probability distributions and incorporates theoretical estimates of structure-property relationships. This physics-informed AI will be iteratively improved through active learning approaches and subsequently used to perform inverse design for the creation of new ADD polymers with targeted properties within specified tolerances that will be experimentally validated against industry benchmarks. This Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics (MFS-SPEED) award is co-funded by the NSF through the Division of Chemistry (CHE), the Directorate for Mathematical and Physical Sciences (MPS), and the Division of Innovation and Technology Ecosystems (ITE) in the Directorate for Technology, Innovation, and Partnerships (TIP). Additional MFS-SPEED funding is provided by Procter & Gamble, PepsiCo, Dow, BASF, and IBM. 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 2025 · 2025-09

This award will support research towards the development of millimeter-scale flying robots capable of controlled operation without a physical connection for power or data. The robots will be powered and controlled remotely by a single-axis alternating magnetic field. Careful design of the flying structures provides passive aerodynamic and gyroscopic stability. This project will focus on providing extended range and enhanced maneuverability toward successful deployment in various applications. This project draws upon disciplines including aerodynamics, control, microelectromechanical systems, and 3D printing to advance the state of the art in remote power transmission and flight control for small rotorcraft. Results from this research will benefit the US economy and society by significantly reducing the size and weight of untethered flying robots. The multi-disciplinary approach will help increase participation and positively impact engineering education. This project plans two key technical innovations for millimeter-scale, untethered, remotely powered flying rotorcraft. The first innovation is utilizing the gradient of magnetic fields to regulate lateral flight motions. The second innovation is beamforming of the magnetic field for extending the range of operations. Flying insects have been the inspiration of miniature flying robots toward innovations and breakthroughs that are potentially applicable beyond the field of robotics. However, the tradeoff between mass and power becomes problematic for engineered systems at these scales. Specifically, the low energy densities of current options such as batteries or supercapacitors make them impractical for on-board power storage. Oscillating magnetic fields have been demonstrated to remotely power millimeter-scale rotorcraft. Because magnetic field strength attenuates with distance following an inverse square law, a simple point source will not deliver sufficient power to a flying robot at a practical operating distance. The purpose of the beamforming task is to use an array of emitters with controllable phases and amplitudes to project the field only to points where it is needed. To carry out useful functions the robots must be steerable. To this end, the research team will investigate flight control and maneuverability through control of the magnetic field gradient. 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 · 2025-09

Understanding the signaling between neurons, glial cells, and blood vessels during development is crucial for designing treatments for vasculature defects, such as the over-vascularization associated with retinopathy of prematurity. The proposal aims to determine the role of neural-glial-vascular signaling in vasculature development through two main objectives: Aim 1: Use quantitative approaches to characterize the vasculature development and to assess how this process is altered in mice with altered retinal waves. We also propose to use live imaging of vasculature growth to assess the nature of dividing vessel maturation. Aim 2: Establish the maturation of the glial-vascular unit using two approaches. First, we will use immunofluorescence to assess when the glial-vascular interface arises during development. Second, we will use two-photon calcium imaging to monitor the spontaneous calcium transients in glial processes associated with vasculature and establish their correlation with retinal waves. This research will enable the lab to test novel hypotheses regarding the role of signaling in vasculature development, which is critical for understanding how neural activity influences normal and pathological vasculature development.

NSF Awards · FY 2025 · 2025-09

Wildfire is happening more often near cities and towns, putting people, homes, and communities at greater risk. Since wildfires are growing larger and more intense it is even more important to take steps to protect these communities. One helpful way to prepare and respond to wildfires is by using computer modeling and simulation. This powerful tool helps predict how fires might spread in areas where forests and natural areas meet cities and towns. These areas are called the wildland-urban interface (WUI). However, creating accurate models is challenging because how a fire spreads in an urban area is affected by many complex processes that occur in both small areas (like a building) and large areas (like a whole neighborhood). This project aims to understand these processes better and build more reliable models that can predict how fires will act in WUI areas, whether at small or large scales. The team also plans to create an easy-to-use computer program that will help emergency planners and local leaders use these tools to make better decisions about evacuations, managing fires, and keeping communities safer. The technical aspects of the proposed research are organized around four primary objectives identified as: (i) to develop a fundamental physical understanding of how fire interacts with individual structures and materials in urban environments at the local scale; (ii) to investigate how these localized interactions influence fire dynamics at intermediate scales—such as neighborhoods and communities—thereby bridging the gap between structure-level physics and community-scale outcomes; (iii) use insights from items (i) and (ii) to construct a computationally efficient, large-scale reduced-order model that accurately predicts fire spread in wildland-urban interface (WUI) scenarios, while capturing the essential underlying physics; (iv) to integrate models developed into a user-friendly, operational platform designed to enable real-time prediction and support decision-making for fire preparedness, response, and mitigation in WUI regions. The project outcome is expected to have a significant societal impact, addressing the increasing wildfire risks driven by shifting hydro-meteorological patterns, drought, and urban sprawl. It will produce predictive tools and decision-support platforms to aid real-time evacuation and firefighting strategies. Additionally, it will inform land-use planning, building codes, and zoning regulations to reduce future risk. Notably, the project promotes broad applicability by guiding policies that ensure all populations receive adequate support during disaster preparedness and recovery efforts. 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 2025 · 2025-09

Microbes are being used to produce a range of products. Some of those products are proteins, such as insulin or blood-clotting factors. In many cases, fungal strains are used because they have protein secretion systems. However, the secretion systems are not optimized for throughput, so strain improvement is needed. To facilitate this process, AI methods will be employed to develop a digital model that emulates most of the behaviors of the original fungal cells. This is commonly referred to as a digital twin. The digital twin will be used to identify genetic edits that will improve protein synthesis and secretion. This will be accomplished for many fungal strains and behaviors. The models will be made publicly available using open-source software. A hierarchical genome-to-phenotype model (G2PM) for fungal systems will be developed. The focus will be on Pichia strains. This model will link DNA sequence to gene expression and, ultimately, to strain-level protein secretion performance. More than 8,000 diverse fungal genomes will be curated to train genomic language models (gLMs). These are deep neural networks that learn complex probability distributions over nucleotide sequences. These pretrained fungal gLMs and their learned embeddings will then be integrated into a sequence-to-expression model that predicts high-resolution RNA-seq profiles directly from genomic sequence, across multiple fungal species and environmental contexts. In parallel, the team will generate and publicly release a comprehensive multimodal dataset of engineered Pichia strains. Each will be annotated with whole-genome sequence, transcriptome profiles, and single-cell secretion measurements. Leveraging this resource, the G2PM will combine the pretrained sequence-to-expression module with protein representations in a phenotype-prediction module to predict secretion titers for target proteins from genomic sequence alone. Together, these efforts could yield an AI framework capable of recommending genomic edits that enhance protein secretion, thereby accelerating fungal strain engineering and enabling more efficient, lower cost biomanufacturing. This project is being jointly supported by ENG/CBET/CBE, BIO/MCB/SSB, and the BioMADE Manufacturing Innovation Institute. 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 · 2025-09

Project Summary The sequence-function landscape of a protein is the connection of genotype and phenotype at a molecular level. Although abstract, this concept has many real-world applications. Mapping and predicting this space would improve our basic science understanding of macromolecular function in the cell, clarify the role of genetic variation in causing disease, and lead to our ability to design biological therapeutics with improved potency and specificity. The goal of our work is to develop experimental methods to systematically explore the mutational landscape of biomedically relevant proteins such as CRISPR-Cas genome editing enzymes. A related aspect of our work is to create new types of assays that yield robust measurements of real biochemical parameters, rather than relative measurements which are typically measured out of convenience. In the long term, our goal is to couple this data, mechanistic studies, and machine learning tools to develop state of the art methods for predicting and engineering protein function.

NIH Research Projects · FY 2025 · 2025-08

Cancer immunotherapies have revolutionized the treatment of cancer but immunoediting of tumor cells sculpts tumor cells that become resistant to CD8 T cell responses by selecting for the loss of MHC I antigen presentation. Here we will elucidate how the local ablation of intra-tumoral regulatory T cells (IT-Tregs) leads to the mobilization of NK and CD4 T cells that control CD8 T cell-escaped tumors by investigating the underlying mechanisms that mediate tumor control and then testing the translational potential of using the discovered mechanisms therapeutically. Preliminary data shows that IT-Treg ablation, using tumor-localized Diphtheria Toxin administration in Foxp3DTR mice, can substantially control MHC I-deficient tumors in a manner that is independent of CD8 T cells. Instead, NK and CD4 T cells mediated tumor control. Induction of the NK response was dependent on CD4 T cells and associated with an increased STAT5 signaling signature in NK cells, indicating that CD4 T cells may help NK cell antitumor function by increasing IL-2 or IL-15 signaling in NK cells. Furthermore, CD4 T cell tumor control was partially independent of NK cells and was associated with an increased expression of granzymes and NK-activating receptors on CD4 T cells. Notably, elimination of tumor cells by CD4 T cells did not require MHC II or MHC I expression by tumor cells, suggesting an alternative mode of T cell recognition of tumor cells, potentially via NK receptors. We propose to address these gaps in our understanding, as well as test whether IT-Treg-depletion, cytokine-based therapeutics, or combinations of both approaches can yield similar activities against evolving primary and metastatic cancers with mixed MHC I- deficiency. In Aim 1 we will determine the mechanism(s) of CD4 T cell help of antitumor NK cell response, focused on the role of IL-2 and IL-15 cytokines and dendritic cells in supporting antitumor NK cell mobilization. In Aim 2, we will determine the mechanism(s) of NK-independent, CD4 T cell tumor control by first addressing whether CD4 T cells can directly kill tumor cells. We will then define the mode of killing and the mechanisms of recognition of MHC I- and MHC II-deficient tumor cells, using genetic deficiencies and antibody blockade experiments. Alternatively, we will test whether CD4 T cells control tumors indirectly by activating macrophages or antibody responses. In Aim 3, we will test the hypothesis that clinically translatable cytokine delivery and Treg-depleting approaches enhance the activity of NK and CD4 T cells against primary and metastatic tumors. Importantly, we will test combinations with checkpoint blockade immunotherapies, which select for MHC I-deficiency in tumor cells, in the setting of mixed MHC I+/- heterogenous tumors. This will test the translational potential of the discoveries made in Aims 1 and 2 using clinically applicable approaches.

NIH Research Projects · FY 2025 · 2025-08

This K01 award will support the career development of Dr. Brittany L Morgan Bustamante, an infectious disease epidemiologist at the UC Berkeley School of Public Health (BPH). Dr. Bustamante seeks to become an independent researcher leading studies on the upstream and contextual factors shaping infectious disease risk and outcomes. The proposed research will investigate three clinically important invasive fungal diseases—aspergillosis, candidiasis, and cryptococcosis. These infections are increasing in incidence and severity but remain underexamined in population-based studies. Although clinical risk factors are well documented, less is known about how broader contextual determinants such as healthcare infrastructure, characteristics of the physical and social environment, and access to services contribute to variation in disease incidence and severity. Fungal infections are especially difficult to diagnose and treat, and delayed detection may be influenced by barriers that occur outside the clinical encounter, including healthcare availability, neighborhood-level conditions, and system capacity. To address these gaps, this project will integrate electronic health record (EHR) and national inpatient data with contextual indicators to identify multilevel drivers (i.e., individual, place-based, policy) of fungal disease risk and severity. Aim 1 will apply a novel multilevel modeling approach to estimate how invasive fungal disease prevalence and in-hospital mortality vary across combinations of individual-level characteristics (e.g., age, sex, geography, insurance type). Aim 2 will use advanced spatial and multilevel methods to quantify the contribution of state-level measures of healthcare access (e.g., provider shortages, insurance coverage), environmental conditions (e.g., dust exposure), and neighborhood indicators (e.g., population density, housing vacancy) to examine geographic variation in infection risk. Aim 3 will estimate the causal effect of Medicaid expansion on fungal disease incidence and outcomes among high-risk individuals (i.e., those with diabetes or chronic obstructive pulmonary disease) using a difference-in-difference approach. The five-year training plan includes coursework, workshops, directed readings, and mentored research activities designed to: 1) Build expertise in developing constructs of the upstream and contextual factors influencing infectious disease risk; 2) Develop skills in the advanced epidemiological, computational, and statistical methods required to study multilevel determinants of disease using disparate data sources; and 3) Strengthen collaborative partnerships and develop leadership and professional skills to design and execute impactful, policy-relevant research projects. The environment fostered by BPH’s commitment to early career scientists, and its strengths in infectious disease epidemiology, biostatistics, and health-systems research provides an exceptional environment to support these goals.

NSF Awards · FY 2025 · 2025-08

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Hendrik Utzat of the University of California, Berkeley, is developing a new optical instrument to measure time-dependent fluctuations in the vibrational spectra of single molecules. These spectral fluctuations provide insights into how molecular bonds are distorted, how molecules interact with surfaces, and how polymer chains change conformation. However, especially fast fluctuations—on the nano- to millisecond timescale—have largely eluded quantitative measurement. Professor Utzat and his team will build an apparatus called Spectral Fluctuation Raman Spectroscopy (SFRS), which will enable precise measurement of Raman spectral fluctuations across a broad range of timescales, including those currently inaccessible with existing techniques. The data generated by this instrument will help researchers better understand molecular interactions, potentially leading to improved catalysts, functional materials, and pharmaceuticals. The project will contribute to building a competitive, quantum-literate U.S. workforce through training opportunities for high school and college students in spectroscopy, quantum optics, instrument engineering, and data science. Technically, the project integrates photon-correlation and interferometric methods into surface-enhanced Raman spectroscopy (SERS) to quantify spectral fluctuations with sub-microsecond to millisecond resolution. The technique measures spectral correlation functions of Raman photons, allowing temporal resolution to be decoupled from detector frame rates. To achieve this, the project will harness cutting-edge superconducting nanowire single-photon detectors (SNSPDs) and interferometry. The instrument will be benchmarked using established SERS model systems to assess its ability to detect vibrational dephasing and molecular switching processes. The method is designed as a general platform for measuring time-dependent vibrational spectral diffusion in single-molecule systems. 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 2025 · 2025-08

The health risks of heat exposure depend strongly on relative humidity (RH), as high RH prevents evaporation and thus blocks the evaporative heat loss that gives perspiration its cooling effect. The close connection between RH and heat stress means that changes in RH matter the most in climates that are already hot and humid, including the global tropics and subtropical maritime regions like Florida. These climates are home to much of the world's population, thus RH change in a changing climate is a question of great practical importance. But climate models do not produce consistent results for RH change and the basic science of RH change is not yet adequate to provide much guidance. A case in point is India, where RH has increased by 5-10% since the 1970s, in opposition to the decline in continental RH with warmer temperatures expected from theoretical arguments. Work funded here seeks to develop a theoretical framework for ground-level RH over tropical continents that can account for changes over the past 50 years and provide an estimate for the RH change that is likely to occur as the tropics continue to warm. The work is based on a budget equation in which RH variations are ascribed to changes in atmospheric transport of moisture from adjacent oceans, vertical mixing of moisture by atmospheric convection, and transfer of heat and moisture between the atmosphere and the land below it. The theory is distinctly tropical as it assumes that onshore moisture transport occurs in overturning circulations driven by land-sea temperature contrasts as is the case in monsoons. The theory is developed and tested using a combination of historical observations and model simulations performed in configurations with varying levels of complexity. The work is motivated by concerns over the human impacts of RH change in warm climates, as noted above, and the research team conducts outreach to organizations addressing RH-related health concerns, both in the US and in India. In addition, the project supports a graduate student and works with two undergraduates recruited through the Berkeley’s Undergraduate Research Apprentice 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 2025 · 2025-08

Heatwaves have large societal impacts that are roughly proportional to the hottest surface air temperature (SAT) during the event – the heatwave intensity. Future changes in heatwave intensity depend not only on the long-term, time-mean warming but also on changes in the variance in SAT; in particular, increases in SAT variance at daily timescales would amplify future heatwave intensity. The work will analyze; (i) the physical mechanisms responsible for daily variance of SAT across the globe in observations; (ii) potential biases in SAT variance in global coupled climate models, and the physical mechanism responsible for those biases and; (iii) robust mechanisms of future changes in SAT variance and their impact on heatwave intensity. Specifically, the work develops a framework for decomposing daily SAT variance into contributions from the movement of energy by atmospheric winds, solar radiation reaching the surface, and changes in soil moisture using observational datasets. Preliminary work suggests that SAT variance in the middle latitudes is governed primarily by the import of tropical air into a region and is thus set by the strength and duration of wind patterns. The impact of model wind biases on SAT variance is diagnosed using targeted model simulations in which the winds are nudged to match observed winds. The wind nudging framework is used in conjunction with a surface energy balance model to understand changes in SAT variance in future projections from an ensemble of climate models, and to identify the influence of model bias on the projected SAT variance change. Additionally, this framework is used to make improved projections of future heatwave intensity by correcting for the impact of model biases in SAT variance over the historical period. 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 2025 · 2025-08

This project addresses fundamental challenges in statistical modeling, to develop more accurate and reliable methods for analyzing complex data. As data becomes increasingly central to scientific discovery, economic prosperity, and national security, the need for advanced statistical tools is paramount. This research will create new techniques in nonparametric regression, a field of statistics focused on fitting models to data without pre-supposing the relationship's form. It confronts three recurrent obstacles in analyzing large datasets -- curse of dimensionality, ad-hoc tuning choices, and the tension between flexibility and interpretability -- by developing principled regression and density-estimation tools, thereby improving our ability to interpret complex information. The work forges new links between shape-constrained nonparametric methods and neural networks, adapts ideas from image processing to statistics, and also unites frequentist and Bayesian thinking through simple, intuitive priors. The development of these methods will have wide-ranging benefits in many applied fields. Furthermore, this project will contribute to the education and training of the next generation of statisticians and data scientists, ensuring that the nation remains at the forefront of this critical field. The investigator will develop a suite of novel approaches to nonparametric regression. One area of focus is a new shape-constrained method for multi-index convex regression, which is designed to alleviate the curse of dimensionality and has close connections to single hidden-layer neural networks. Another key component of the research involves the systematic study of Total Generalized Variation (TGV) regularization for regression and density estimation problems that have both smooth and non-smooth components, a common challenge in fields like image processing. The project will also investigate the properties of the log-concave maximum likelihood estimator, with the aim of proving its suboptimality in high dimensions under the total variation distance. Additionally, the research will explore Bayesian approaches with innovative priors, such as those based on Cauchy processes, to model complex regression relationships and address the issue of tuning parameter selection. Finally, the research will develop Bayesian methods for mixed-derivative constrained regression, leading to the creation of an additive regression tree and piecewise linear fits for greater flexibility in multivariate settings. These research thrusts will be pursued through a combination of theoretical analysis and computational experiments, to produce practical and principled solutions to outstanding problems in nonparametric regression. 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 2025 · 2025-08

This project focuses on two big open problems in algebraic combinatorics that lie at the intersection of representation theory and algebraic geometry. Both problems have connections to multiple fields of mathematics, and in both cases the PI and his collaborators aim to use combinatorics to understand objects which are currently understood only abstractly in a more concrete way. It is hoped that such understanding will have applications back to the other areas of mathematics, but also to physics and to human-machine collaboration in mathematics. The project will also help develop the STEM workforce by providing research and conference travel support for graduate students. More specifically, the project aims to (1) improve upon recent progress the PI has made on the Combinatorial Invariance Conjecture for Kazhdan-Lusztig polynomials, which asserts that these polynomials of foundational importance in geometric representation theory depend only on the structure of a certain graph, and (2) extend the PI’s recent construction of an SL(4) web basis to produce analogous bases for spaces of invariants of higher rank Lie (or quantum) groups. For (1), the proposed approach takes advantage of the combinatorics of structures called hypercube decompositions of Bruhat intervals, which were discovered with the aid of machine learning. For (2), the PI hopes to deepen the recently discovered connections between webs and the combinatorics of plabic graphs. 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 2025 · 2025-08

Materials made of grains - like sand, agricultural products, and pharmaceutical powders - are not only some of the most common materials in our daily lives but also cover the surfaces of other planets. Predicting how they will flow on Earth has been a long-standing challenge for engineers. These difficulties will only grow as missions are planned at lower gravity to explore asteroids, the Moon, and Mars. In this project, the team will create powder-flow experiments small enough to fly on the International Space Station. These experiments, performed by astronauts at both high and low gravity, will be compared to results from experiments performed by students here on Earth. Using this data, reliable digital twins will be created and tested, which are computer models that mimic the observed flows. Through these efforts, cutting-edge training will also be provided to students. This project will explore granular flow behavior by conducting and modeling experiments under different gravity conditions, both on Earth and aboard the International Space Station (ISS) using the Multi-use Variable-gravity Platform (MVP). This specialized facility employs a centrifuge to simulate a wide spectrum of gravitational forces - from near-weightlessness to conditions exceeding Earth gravity - offering a rare opportunity to examine how granular systems respond outside typical terrestrial environments. This project will investigate two central hypotheses. The first posits that granular flows are strongly influenced by the magnitude of gravity. To test this, rotating drum flows composed of materials ranging from uniform beads to regolith simulants will be analyzed. By examining features like interface geometry and internal velocity fields, it will be determined whether observed behaviors follow theoretical predictions for gravity-dependent scaling laws, or deviate in measurable ways. The second hypothesis suggests that existing continuum modeling frameworks, grounded in fundamental mechanics, can be extended to accurately capture granular dynamics in low-gravity regimes. These conditions may amplify secondary effects such as cohesion or particle softness, which will be incorporated by the team into model refinements. Using data from both ground and ISS experiments, the team will iteratively calibrate and validate the models. Success will be defined by identifying dominant constitutive ingredients across different gravity levels. This will provide crucial evidence that well-constructed continuum models can serve as predictive digital twins for granular processes relevant to planetary exploration, where direct experimentation is limited or impossible. 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 2025 · 2025-08

This project will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI). As DS and AI play an increasingly central role in science, healthcare, technology, and national security, it is essential that the methods used to analyze data are trustworthy and transparent. However, current data analysis tools often rely on the traditional assumption that data come from a specific form of probabilistic model—a practice that often fails to capture the complexity of modern data, leading to misleading conclusions and contributing to a growing crisis of scientific replication. This project studies a new framework called Predictability-Computability-Stability Inference (PCSI) for veridical data science (VDS) to help ensure that conclusions drawn from data are not only accurate but also stable, interpretable, and computationally practical. The research will also help train the next generation of data scientists, promote interdisciplinary collaboration, and support the responsible development of AI. By improving how uncertainty is measured and communicated, the project serves the national interest by strengthening scientific research integrity and public trust in data-driven decisions. The PCSI approach evaluates multiple predictive algorithms and filters out those with insufficient performance, avoiding dependence on any single model and focusing uncertainty assessment on those that are adequately predictive. It uses multiple bootstrap samplings to address uncertainty in an integrated manner with the new form of uncertainty in PCSI: stability over pred-checked algorithms. It also employs a novel multiplicative calibration technique to ensure valid prediction coverage, improving robustness to subgroup structures. The project specifically aims to advance the PCS framework for veridical data science (VDS) by developing PCSI methods for key areas of machine learning, including classification, deep learning, and ensemble learning. The research consists of three thrusts: (1) developing PCSI for classification to improve uncertainty quantification, robustness, and accuracy in both binary and multi-class settings; (2) designing PCSI methods for deep learning and large language models using computationally feasible perturbations and calibrations to enhance stability, interpretability, and performance in modern AI; and (3) establishing theoretical foundations for PCSI and PCS-guided ensemble learning, showing that even under model mis-specification, PCSI can remain valid and outperform existing methods such as conformal inference under reasonable conditions. These developments will result in statistically sound, computationally efficient tools, along with software, publications, and educational materials to broaden participation and ensure broad dissemination. 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 · 2025-08

Abstract Visual input is full of noise and ambiguity, thus the brain needs to actively infer the most likely interpretation from multiple possible causes. Remarkably, even though this process of inference relies on billions of neurons distributed across the entire visual hierarchy, what we consciously perceive is always coherent, e.g., if we see a face in an electrical outlet, then we also see two eyes and a mouth. A powerful idea to explain this coordinated inference process is the “analysis by synthesis” paradigm, which posits that the visual system successfully perceives a scene when it can simulate how it is generated. “Synthesis” refers to the top-down reconstruction of an image from a higher-level proposal, and perception is successful when the reconstructed image matches the incoming sensory input. According to analysis by synthesis, visual perception is always coherent because it is synthesized to be so; specifically, via hierarchical generative feedback, a change in interpretation at the highest level can coherently steer representations across all earlier levels. In this proposal, we seek to understand whether and how analysis by synthesis is implemented by the primate ventral stream. The project constitutes a close collaboration between an expert in macaque ventral steam electrophysiology (Tsao) and an expert in dynamical systems modeling (Engel). We propose to use Neuropixels probes to record simultaneously from multiple nodes of the macaque face patch network, an experimentally tractable model for the primate ventral stream. Furthermore, we will develop novel flexible and interpretable computational models for identifying dynamics of neural representations with millisecond resolution on single trials from high-dimensional neural activity data. We will investigate two scenarios where generative feedback should be especially important: perception of ambiguous images (Aim 1) and perception of degraded images (Aim 2). Finally, in an exploratory Aim 3, we propose to record from multiple face patches while monkeys sleep, to look for signatures of generative visual feedback during dreaming. The project will give unprecedented insight into the dynamic computations underlying hierarchical visual inference.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Bats are reservoir hosts for zoonoses that cause the highest case fatality rates documented in humans, including rabies and related lyssaviruses, Hendra and Nipah henipaviruses, Ebola and Marburg filoviruses, and SARS and MERS coronaviruses. Bats exhibit limited disease upon infection with these viruses that cause extreme pathology in other mammals, likely due to robust and rapid innate and cell-mediated immune defenses, coupled with hyper-efficient mechanisms of DNA damage repair and dampened inflammatory pathways. Recent theoretical work in our lab demonstrates how these unique features of bat immunology and physiology—chiefly, constitutive antiviral immunity and resilience to inflammation that confers tolerance to immunopathology—should select for the evolution of high virus growth rates that, while avirulent to bats, are likely to cause pathology following spillover to non-bat, including human, hosts. Here, we seek to explicitly test the predictions of our theoretical model by carrying out experimental evolution of vesicular stomatitis virus (VSV) in bat cell cultures that span a range of both (Aim 1) constitutive antiviral and (Aim 2) inflammation tolerant phenotypes. Under Aim 1, we examine variation in VSV growth rate evolution and the rate of molecular evolution following serial passage of virus across a suite of Pteropus alecto bat cell lines that exhibit both intact (wildtype) and deficient (CRISPR knock-outs) antiviral immune functions. Under Aim 2, we leverage our lab’s unique system of primary bat fibroblast cell lines derived from related species spanning a range of longevities to evaluate whether cells derived from longer-lived species that demonstrate resilience to aging-related stressors also exhibit heightened tolerance of virus infection. We then compare VSV evolution following serial passage across cell lines that demonstrate variable resilience to aging-related stressors in vitro. We hypothesize that antiinflammatory properties in bat cells which confer resilience to aging stressors may also facilitate virus tolerance by limiting immunopathology and—by extension—drive the evolution of high growth rate viruses likely to generate pathology in non-bat hosts. Ultimately, we offer an explicit empirical test of the hypothesized mechanisms underpinning the extreme virulence of bat virus zoonoses.

NSF Awards · FY 2025 · 2025-08

This project envisions a prosperous and secure Arctic region focusing on Alaska that can build, maintain, and operate resilient and sustainable coastal and interior civil infrastructure and can adapt to the dynamic marine and terrestrial environmental changes. This vision will be achieved by engaging with Alaskan communities, industry, and local-to-federal government entities, thereby building a pipeline for workforce development of future scientists, engineers, and skilled workers with expertise in Arctic environments. The team will collaborate with the North Slope Borough and the communities in Seward Peninsula to co-develop and implement the solutions to emerging challenges, notably coastal and riverine erosion in the Arctic coastal communities, infrastructure failures induced by permafrost degradation, and flooding. The resilience solutions and technologies, from ideation to implementation, will be co-developed through close collaborations with partners of Indigenous communities, industry, local to federal government, and six academic institutions. The impacts include improved well-being and resilience of individuals and communities in the U.S. Arctic, increased economic competitiveness of the U.S., improved national security, and increased public scientific literacy and public engagement with science and technology. The project will generate new understanding of how the Earth system (including the northern and northwestern Alaska region, permafrost, and coast-land interface) changes, and its interactions with the built and sociocultural systems, thus building the foundational knowledge base to develop solutions to emerging problems. At the end of Phase-1, the project will (1) identify and specify the solutions needed to address the U.S. Arctic challenges from permafrost degradation, erosion, and flooding, (2) identify data gaps and devise approaches to collect new data for the technology development, (3) define specific requirements for the technologies and solutions, and (4) identify application sites for the technologies and solutions and collaborating partners. Project costs and feasibility in translation of research to solutions will be demonstrated by conducting techno-economic analysis on enabling technologies and system-level solutions. 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.