University Of California Berkeley

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Congenital heart defects are the most prevalent birth defects observed in humans, several of which result from abnormalities in an embryonic stem cell population called the cardiac neural crest (CdNC). These cells, which originate in the vertebrate hindbrain, are characterized by their extensive migratory ability and capacity to differentiate into numerous components of the heart, including cardiac ganglia and the smooth-muscle septum between the aortic and pulmonary vessels. The mechanisms that govern CdNC derivation and spatiotemporal differentiation remain largely a black box. However, observations from preliminary and historical work suggest that the Bone Morphogenetic Protein (BMP) signaling pathway and cell-cell interactions between the ectoderm and mesoderm play critical roles in modulating CdNC development and contributions to heart organogenesis. The present study will leverage the unique strengths of multiple in vivo (chick and mouse embryos) and in vitro (human embryonic stem cell) models to elucidate how BMP signaling is regulated intrinsically and extrinsically to govern the derivation of the CdNC and its contributions to the heart. Aim 1 will define the role of the BMP signaling antagonist, Noggin, which is specifically expressed in the CdNC, in establishing CdNC identity by assessing the specification and behavior of chick and mouse neural crest cells in vivo following genomic perturbation of Noggin, as well as by performing transcriptomic analysis of Noggin loss-of-function mutant cells to identify downstream BMP target genes with altered expression. Aim 2 will uncover the gene regulatory mechanism underlying the CdNC-specific expression of Noggin by using a two-pronged approach of knocking out CdNC-specific transcription factors co-expressed with Noggin by CRISPR-Cas9, and site-directed mutagenesis of a putative Noggin enhancer which perfectly mimics endogenous Noggin expression in the CdNC. Additionally, Aim 2 will also explore a potential BMP signaling feedback loop and dose-dependent autoregulation of Noggin expression within the CdNC. Aim 3 will unravel the molecular crosstalk between ectoderm-derived CdNC and the mesodermal cells of surrounding tissues, particularly the somitic mesoderm, and unmask the resulting impacts on CdNC cell fate, by employing novel in vitro cultures of human CdNC cells derived from human embryonic stem cells. Taken together, these experiments will resolve how intrinsic genetic circuitry and extrinsic molecular cues synergistically coordinate spatiotemporal patterning of CdNC cells within the hindbrain, and their contributions to cardiovascular development. The completion of this project and the associated career development plan will support Dr. Gandhi in his goal to become a principal investigator studying the etiology of cardiac-crest-derived congenital defects in a manner that maximizes relevance to future therapeutic applications.

NSF Awards · FY 2025 · 2025-08

This project investigates the mathematical foundations of new phases of matter in condensed matter physics, such as stacked and twisted two-dimensional semiconductors/graphene, which are at the forefront of modern quantum science. Although experimental observations have been very fruitful, understanding their novel electronic and physical properties still requires new mathematical methods. This project develops new tools to analyze these materials in the semiclassical regime: the limit where quantum and classical physics meet. This project supports the design of future electronic and quantum devices and the prediction of new quantum phenomena, with potential applications in quantum information technology and materials science. This project also contributes to emerging technologies that rely on two dimensional materials such as superconductive or quantum computing devices. The project serves the national interest by advancing foundational science in applied mathematics, quantum science and materials science. The project also supports undergraduate and graduate education, as well as promotes interdisciplinary collaboration and dissemination of scientific knowledge via scientific workshops and seminars. The investigator studies the semiclassical and spectral analysis of matrix-valued Schrödinger operators arising in twisted two-dimensional semiconductors and twisted bilayer graphene. These systems exhibit rich spectral and topological properties, especially at small twisting angles, where moiré patterns produce new low-energy effective theories. While semiclassical analysis is well-developed for scalar operators, this project develops new methods for matrix-valued Hamiltonians, essential for describing multi-band and spin-orbit coupled systems in these materials. Another goal is to extend semiclassical methods to problems in quantum information theory and quantum computing, where understanding spectral gaps and asymptotic behavior of Hamiltonians is critical for error correction and algorithm design. The project also aims to study problems in the spectral theory of quasi-periodic operators, particularly continuous Schrödinger operators with incommensurable electromagnetic fields. Current understanding is largely based on discrete models such as the Almost Mathieu operator, while this research seeks to overcome these restrictions by directly analyzing the spectrum of continuous Schrödinger operators with such electromagnetic fields in the semiclassical limit. The project integrates techniques from partial differential equations, semiclassical analysis, representation theory, Bloch-Floquet theory, algebraic topology, and differential geometry. The outcomes will provide rigorous mathematical foundations for understanding band structure and band topology in moiré materials, enable interdisciplinary collaboration, and help guide experimental discoveries in physics and material science, such as magic angles or new phases of matter in twisted semiconductors and twisted multilayer graphene. 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 I-Corps project focuses on the development of a new biosensor for rapid, highly sensitive, and specific detection of proteins and nucleic acids. Biosensors aim to identify, characterize, and quantify biological compounds to understand their structure, function, and interactions within living systems. However, biosensors face challenges in detecting and quantifying these compounds in complex biological samples. This solution introduces a biosensor capable of detecting very small biological compounds such as proteins or nucleic acids. This sensor is able to detect multiple compounds simultaneously, representing an advance for rapid diagnostics and drug discovery. The solution is particularly relevant for sepsis detection. Sepsis is a critical health issue in the United States and worldwide, affecting about 50 million people annually, costing more than $38 billion per year in the United States alone, and causing 19% of all global deaths. The growing issue of antibiotic resistance further exacerbates this problem, creating an urgent need for novel diagnostic techniques to replace inefficient and costly blood culturing methods currently used in hospitals. This biosensor has the potential to address that need, improving patient outcomes and advancing national health and welfare. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an extremely sensitive and multiplexed optical biosensor by incorporating singularities in open optical systems, known as exceptional points. The nanophotonic device consists of a periodic array of coupled gold nanobars arranged on a glass substrate, forming a plasmonic metasurface engineered to operate at an exceptional point. This structure is highly sensitive to small changes in its environment and enables the detection of proteins and nucleic acids at very low concentrations through functionalization with specific molecular probes. Each individual metasurface is a 30 micron × 30 micron square, allowing more than 10,000 devices to be integrated into a single 1 cm × 1 cm chip. This dense arrangement supports high throughput, multiplexed biosensing by targeting different biomarkers on the same chip. Operating at an exceptional point enhances sensitivity by approximately 300 times compared to conventional biosensors. Due to its unique sensitivity, scalability, and multiplexing capabilities, this technology offers strong potential for commercialization across a wide range of diagnostic applications. 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

Plants obtain all water and nutrients through roots. Across grasslands, plants store most of the carbon that they consume from the atmosphere in their roots and the substances that roots secrete. Despite their critical role, plant roots are understudied. This NSF-CAREER grant will explore the effect of extreme precipitation and nutrient deposition on root growth, and the consequences of root expansion in the context of a changing environment. The project will involve an interdisciplinary team that includes graduate students and postdoctoral researchers, in addition to the principal investigator and his collaborators. The team will test the novel hypothesis that the response of root productivity to precipitation extremes depends on the nature of the response of root productivity to water availability. Mechanisms to be tested include root-system expansion through partnership with fungi, and root-system contraction when eaten by soil nematode worms. The team will use multiple approaches including greenhouse and lab experiments, as well as large-scale field experiments. This research will be of broad societal value because it will enable a better understanding of grassland ecosystems and their role in the carbon cycle. This CAREER grant builds upon previous work on aboveground responses to precipitation and nutrient availability manipulations by turning previous concepts, hypotheses, and mechanisms upside down and centering empirical tests on belowground processes in three hierarchical steps. First, it explores mechanisms behind the effect of precipitation variability on root productivity related to the shape of the relationship between root growth and water availability as stated by the Jensen inequality. Second, it incorporates nitrogen availability and tests for interactions that change the shape of such relationships altering the net root productivity response to precipitation variability. And third, it considers the effect of symbiotic associations between root and soil fungi that may enhance root fitness and nutrient uptake in contrast to root herbivory by nematodes that reduces the capacity of roots to uptake water and nutrients. In addition, the project considers structural differences among grassland ecosystems by testing the applicability of experimental findings across grassland types globally. This work will integrate a comprehensive education plan using multiple approaches from undergraduate courses to student mentoring at all levels (high school through graduate students) and dissemination of results to land managers and scientists. 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

Artificial Intelligence (AI) systems are rapidly advancing in capability, driven by growth in complexity, model size, and training datasets. However, methods to understand the behavior of these models have not scaled commensurately. Furthermore, there remains limited understanding about how important individual data points are to the model behavior. In order to ensure that these models can be trusted and safely deployed in sensitive applications like autonomous navigation, national defense, and medicine, it is critical to develop a more profound understanding of how they work. Greater transparency into the decision-making process of AI models can be used to accelerate scientific discovery in fields like protein design and drug discovery by revealing novel patterns in complex data. Clearer explanations also enable direct model improvement with the goal of building more robust and trustworthy AI systems. This project will develop a powerful method for making AI predictions understandable and explainable using token masking techniques that combine coding theory and signal processing. This project aims to advance the understanding of Artificial Intelligence (AI) models by leveraging the discovery that AI models exhibit sparsity under spectral representations such as the Fourier transform. By employing ideas from channel coding theory for masking, fast spectral methods, and tools from signal processing and error correction coding, the project will develop scalable algorithms to identify significant input interactions and features providing faithful explanations of AI model behavior. The project will also aim to understand the influence of different training data samples on model predictions, as well as the impact of data imbalances on the dynamics of AI applications in order to create well-designed data acquisition mechanisms. In critical applications such as healthcare, these methods will allow one to validate an AI-generated diagnosis by efficiently identifying the most salient features in a patient's medical record. Furthermore, this framework will help identify when a model relies on irrelevant or harmful data features, providing a path to align the model toward more robust and trustworthy outcomes. 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

Models of the physical and chemical behavior of partially molten rocks form a key component of scientists' ability to study and understand volcanic systems. Together with field-based studies, experimental analyses, and investigations of prior eruptions, these models are important for advancing our knowledge of potentially hazardous volcanoes in the United States and globally. This project continues development of a flexible, powerful, and easy-to-use suite of modeling software tools used by thousands of Earth scientists: alphaMELTS. This project will expand the scenarios that alphaMELTS can model, increase integration with igneous rock and experimental databases, link to other geochemical modeling tools written in Python, and support users with workflows for increased reproducibility. Online resources and outreach workshops will extend applications of the software in teaching and training. These workshops, both virtual and in-person, will equip scientists from various career stages and experience levels with quantitative tools for modern Earth science research. This project will implement a framework to align alphaMELTS petrologic modeling software and workflows with FAIR principles (findable, accessible, interoperable, and reusable). Planned developments will make alphaMELTS fully open source, easy to install with standard tools, interoperable with associated modeling and visualization tools and databases, callable from many programming and data visualization environments, and fully versioned, logged, and documented. The work will focus on Python-based tools – in particular, via continued development of the PetThermoTools package for beginner-to-intermediate MELTS users, and machine-learning assisted acceleration for high-end users with high-volume throughputs – but also support those who prefer a simpler graphical user interface. A systematic assessment of model performance will guide users towards applications where the software is verifiably accurate. Expanded functionality will include modeling of reverse crystallization and post-entrapment crystallization of melt inclusions, integration with packages like Thermobar and PySulfSat, and a new trace element engine that gives users total control over partition coefficients. This project will increase integration with IEDA2 databases and the VICTOR portal, and engage with users though workshops, online resources, and creation of instructional materials. The core software development team will grow to include two early-career researchers, as well as two graduate student researchers, which will foster the continuity of alphaMELTS services moving forward. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry program in the Division of Chemistry, Professor T. Don Tilley of the University at California at Berkeley will develop synthetic routes for efficient preparation of nanocarbon compounds that exhibit optical and electronic properties like those of graphene. Graphene is a material that consists of a single layer of carbon atoms arranged in a honeycomb planar nanostructure. It is known for its remarkable properties, being about 200 times stronger than steel yet 1,000 times lighter than paper. The targeted molecular systems consisting of multiple aromatic rings fused together will be designed to interact with light in a controlled and unique way and to possess previously unknown and well-defined 3-dimensional structures. The studies outline in this work have the potential to reveal innovative ways to integrate the transport of charge with photon excitations and provide novel strategies to enable development of next-generation electronic devices. The activities involved in this work will contribute to the training of young scientists and technologists, while generating useful and fundamental chemistry knowledge. The educational aspects of this project will provide opportunities to inspire high school and undergraduate students toward careers across various chemistry and nanotechnology fields. This project will develop synthetic methodologies for the controlled, efficient and scalable introduction of fused aromatic rings into large, tailored polycyclic aromatic hydrocarbons (PAHs). The synthetic methods will utilize high yielding [2+2+n] cycloadditions that simultaneously introduce multiple aromatic rings in a single synthetic step. This methodology should allow access to a wide range of carbon nanostructures, including examples that contain antiaromatic rings or helically chiral graphene-type systems. The elaboration of PAHs with fused antiaromatic rings is to be explored in the context of accessing radicaloid character and mechanisms for spin-state manipulation, of current interest for potential applications in quantum information science. The helical PAHs that will be studied are “expanded” to provide avenues to host-guest chemistry and new chiroptical properties, with the potential to function as spin filters and charge carriers in thin-film devices. Other synthetic studies will explore strategies for spatially orienting nanocarbon units by way of supramolecular assembly and dynamic covalent chemistry (DCvC). The DCvC approach will be used to generate new molecular topologies featuring entangled, knotted nanocarbons and strategies for spatially orienting nanocarbon units. Overall, this research aims to understand the behavior of new nanocarbon materials from a fundamental perspective, and in the long term rationally tune these structures for desired applications in organic electronics and quantum information science (QIS). 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 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.

NSF Awards · FY 2025 · 2025-08

Autonomous agents like warehouse robots, sidewalk-operating delivery robots, drones, and robo-taxi fleets are being increasingly deployed in the real world. These agents incorporate artificial intelligence (AI)-driven sensing and estimation, and planning and control. The real-world performance of these agents, however, is far from satisfactory. It is not uncommon to see a delivery robot being confused by an obstacle in its path on the sidewalk, a drone failing to achieve its objectives, or a robo-car driving irratically. Despite significant advances in AI-based design for such agents, there remain critical challenges in making them robust for performance in the real world. Firstly, autonomous agents must obey operational rules (e.g., traffic rules) and maintain safety at all times. Secondly, these agents rely on various sensors, such as cameras, radar, and proximity sensors, which provide only a partial or imperfect observation of the state of the environment. Thirdly, since such agents rarely operate in isolation, they need to coordinate with other autonomous agents or humans. Finally, to ensure robust and resilient performance upon deployment, it is vital to develop methods for runtime monitoring and adaptation for these agents. This project aims to address these critical challenges for achieving robust performance by autonomous agents in the real world. The research conducted in this project can significantly impact the science of safety-assured autonomy with potential applications in autonomous transportation systems, robotics, and smart manufacturing systems. This project will bring together formal methods, reinforcement learning, and multi-agent control theory to develop a scalable framework for high-assurance design of safety-critical and mission-critical cooperative interacting agents. The research conducted in this project will aim to develop model-based and model-free algorithms for control synthesis using data generated in high-fidelity simulators to optimize a performance criterion subject to the satisfaction of specifications expressed in signal temporal logic. The logic will capture safety requirements, operational constraints, and complex environment behaviors. This project will also develop control algorithms for an autonomous agent operating in a mixed-agent environment where human agents may be present, and for teams of cooperative autonomous agents with system-wide objectives and specifications. It will investigate the design and analysis of robust offline and online monitoring algorithms for high-assurance design under uncertainty. It will also aim to design scalable, hierarchical verification algorithms that leverage data and a new formal model based on graphs of assume-guarantee specifications to reason about system correctness and safety both at design-time and runtime. The research in this project will contribute to the foundations of robust intelligence for multi-agent systems that can operate in complex, real-world environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract The goal of this proposal is to dissect the dynamics, functions, and evolution of TnpB, a transposon-encoded RNA-guided endonuclease, to enhance its genome-editing efficiency and expand its functions beyond DNA cleavage. While RNA-guided enzymes like CRISPR-Cas9 and Cas12 have revolutionized programmable genome engineering, there is a growing demand for smaller and more versatile enzymes. With its compact size (~400 amino acids), TnpB is well-suited for delivery systems with strict cargo limits, such as adeno- associated viruses (AAV). Moreover, its greater abundance and evolutionary diversity present opportunities to develop new RNA-guided tools with broader applications. However, key aspects of TnpB’s mechanism remain unclear, limiting its potential. Specifically, three major knowledge gaps need to be addressed: (1) What molecular determinants drive TnpB’s editing efficiency, and how can they guide the engineering of more efficient variants? (2) How do TnpB and reRNA, its guide RNA, co-evolve, and can this knowledge be used to design novel protein-RNA complexes? (3) How is mature reRNA generated, and can this knowledge inform the design of RNA motifs with improved processing efficiency? This proposal will address these questions through three specific aims: Aim 1 will develop a quantitative kinetic model for TnpB’s targeting efficiency to determine rate-limiting steps, using biochemical and single-molecule biophysical measurements. Aim 2 will explore the co-evolution between TnpB and reRNA, through bioinformatics, structural biology, and functional assays, to understand how their 3D structures have adapted together and to identify key hotspots for structural changes. Aim 3 will investigate the reRNA maturation pathway using RNA biochemistry and structural probing, focusing on how reRNA is processed into a functional guide RNA, and seek to design RNA motifs for more efficient reRNA processing. In the K99 mentored phase mentored by Dr. Jennifer Doudna, I will receive hands-on training in single- molecule biophysics (rotor bead tracking) with Dr. Zev Bryant (Stanford) to complete Aim 1, while also gaining expertise in bioinformatics for evolutionary analysis and structural biology techniques (cryo-EM, RNA structural probing) in preparation for Aims 2 and 3. Additionally, I will focus on developing essential professional skills for my independent career, such as lab management, mentorship and networking. This comprehensive scientific and professional training in the K99 phase will prepare me for the R00 independent phase, where I will lead the completion of Aims 2 and 3 and establish my own research laboratory. These combined efforts will provide more quantitative and detailed mechanistic insights into TnpB’s function and evolution, enabling the development of future RNA-guided genome-editing tools combining compact sizes, robust activities, and expanded capabilities for both research and therapeutic applications.

NIH Research Projects · FY 2025 · 2025-08

Abstract Alzheimer’s disease (AD) is a progressive disease and is the most prevalent neurodegenerative disease underlying dementia in the elderly. AD is known to progress following a temporal and spatial pattern where specific brain regions are affected at different stages of the disease. While both β-amyloid plaques and tau neurofibril tangles are hallmarks of AD, tau pathology shows a stronger correlation with cognitive decline. Tau is predominantly produced by neurons, yet different brain regions and neuron types exhibit varying vulnerability to tau pathology. AD-vulnerable neurons exhibit hyperexcitability linked to tau accumulation and secretion, observable even before AD onset. Secreted tau spreads between neurons and astrocytes, promoting tau pathology. Early AD astrocytes show abnormal calcium signaling, potentially impairing tau clearance. Therefore, to better understand the molecular mechanisms and explore new AD therapeutics, it is essential to regulate the activity of specific AD-vulnerable neurons and their associated astrocytes. We propose to develop magnetogenetic techniques with better efficiency and cell specificity enabled by enhancer AAV to manipulate subtypes of brain cells implicated in AD onset. Using these tools, we will test cell-type specific hypotheses underlying the accumulation, spread, and clearance of tau proteins across species. To control the activity of these cells, we will employ FeRIC (Ferritin iron Redistribution to Ion Channels), a magnetogenetic tool that we have pioneered. FeRIC can wirelessly and non-invasively modulate cell activity and achieve cell-type specificity and deep brain access. Enhancer AAV vectors will be developed to drive cell type specific expression of FeRIC. By combining brain cell type-specific enhancer AAV vectors and FeRIC, we will develop cell-type specific non- invasive techniques to manipulate AD-vulnerable neurons and astrocytes and test their roles in AD onset and propagation with cellular, circuit, and temporal precision, in rodent models both in vitro and in vivo, and exploratorily in ex vivo human and non-human primate brain tissues. A successful outcome will elucidate the mechanistic actions of tau pathology in early AD onset through non-invasive, cell-type-specific manipulation of AD-vulnerable neurons and astrocytes, potentially reversing the disease's progression.

NIH Research Projects · FY 2025 · 2025-08

Understanding the motor cortex's role in controlling complex, natural motor behaviors remain a major challenge in neuroscience, and thus limits our ability to elucidate mechanistic causes for many movement disorders. This proposal addresses a significant gap by introducing bat flight as a novel model to study motor control of natural dexterous movement. Traditional models often limit behavioral complexity and frequently require restraining and long training periods. As such, they may not fully explore the computational space of natural dexterous movements. In contrast, bat flight involves intricate 3D maneuvers requiring high precision and control over complex hand-like wings and is also naturally highly reproducible in the lab setting. This makes it an ideal paradigm for exploring motor cortical computations of complex natural behavior. To study this behavior in bats, we have recently made several key technological advancements. First, the use of wireless Neuropixels allows for the simultaneous recording of hundreds of neurons, providing unprecedented insight into population-level activity in freely flying bats. Second, the use of causal neuronal manipulation methods, such as optogenetics and chemogenetics, which allows for precise reversible perturbation at key points during flight. Our preliminary results indicate that motor cortex representations span several behaviorally relevant time scales, from the phase of the wingbeat cycle to the phase of the flight trajectory and to specific activity patterns for preparation / planning at the population-level. Using DREADDs, we find that motor cortex activity is crucial for precise flight control, with disruption leading to significant performance deficits. Thus, the integration of cutting-edge wireless recording technology and targeted neural manipulation offers innovative approaches to dissect motor control in freely flying bats. By characterizing flight behavior in both simple and obstacle-rich environments and mapping neuronal activity to these behaviors, this project aims to elucidate the motor cortical mechanisms underlying natural dexterous movement. This new research program studying motor control in bats has the potential to significantly enhance our understanding of motor cortex function and could uncover fundamental principles leading to translational applications that improve treatments and rehabilitation strategies for individuals with impaired motor function.

NIH Research Projects · FY 2025 · 2025-07

SUMMARY The human fungal pathogen Coccidioides causes Valley Fever, a treatment-refractory and sometimes deadly disease prevalent in arid regions of the western hemisphere. Patients contract the disease by inhaling fungal spores from soil. Understanding Coccidioides behavior in the environment is thus key to public health measures for Valley Fever prediction and prevention. We propose a first-ever molecular study of the environmental life cycle of Coccidioides toward this end. Our preliminary work has shown that Coccidioides preferentially makes infectious spores in dry conditions. We now want to know how. We apply experimental- and computational- genomic approaches to dissect desiccation response in Coccidioides outside the host. The rich data sources we generate will pinpoint candidate genes underlying spore formation and will also position us to discover other environmentally-triggered phenotypes in Coccidioides. Our work will open a new wing of the research literature focused on the molecular basis of Coccidioides disease transmission from the environment.

NSF Awards · FY 2025 · 2025-07

Nonlinear hyperbolic and dispersive partial differential equations (PDE) are fundamental to describing natural phenomena across all scales, from subatomic particle dynamics to electromagnetism, fluid dynamics, and the behavior of astronomical bodies. This project aims to deepen our rigorous understanding of these equations by investigating a strategically chosen array of key problems concerning long-term dynamics, singularity formation, and the stability or instability of special solutions. The insights gained will clarify highly nonlinear phenomena in physics, including gravitational singularities in general relativity, soliton resolution in dispersive models, and small-scale formation in plasma physics. The project will train undergraduate, graduate, and postdoctoral researchers by mentoring research projects and organizing research-focused seminars. The main scientific goals of the project are as follows. First, for the Einstein vacuum equation, the Principal Investigator (PI) plans to make direct progress towards the Strong Cosmic Censorship conjecture for perturbations of the subextremal Kerr black hole spacetimes, based on the recently introduced technique for computing and justifying generic late-time tails. Second, for the Skyrme model, the goal is to prove the asymptotic stability of the equivariant B = 1 Skyrmion, based on the PI's recent work on the asymptotic stability of the 3-dimensional catenoid. Third, for critical semi-linear geometric dispersive equations, the PI plans to attack the Soliton Resolution conjecture along a sequence of times without any symmetry assumptions, based on the PI's work on critical geometric flows. Additionally, the project aims to provide a more precise description of generic dynamics under suitable symmetry assumptions, which goes beyond the Soliton Resolution conjecture. Finally, for the Hall-magnetohydrodynamics equations in plasma physics, the PI plans to utilize the instability mechanism of degenerate dispersion to establish enhanced (or even anomalous) dissipation, based on the PI's work on ill-posedness by the same mechanism in the non-dissipative case. 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 · 2025-07

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry, Professor Richmond Sarpong of the University of California–Berkeley will study methods for the insertion of reactive derivatives of benzene known as benzynes into five-membered organic compounds to prepare seven-membered rings. The methods that will be developed will streamline the preparation of complex organic molecules that have previously required more steps to prepare and introduce a completely new way to make families of compounds. The planned studies will provide a new platform for multi-step organic synthesis and train researchers in the science and art of chemical synthesis, which will prepare them for careers in the agrochemical, materials, and pharmaceutical industries. The goal of the proposed project is to develop the insertion of benzynes into 1,3-diketones, specifically in fused bicycles. The resulting ring-enlarged fused ring systems comprise the core of natural products in a wide range of natural products. Because the fused bicyclic substrates that will be employed are symmetrical, this project will also offer opportunities to develop enantioselective benzyne insertions for the first time. It is anticipated that this latter development will introduce a completely new way to prepare fused polycycles in enantioenriched form. 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 · 2025-07

This project advances NSF’s mission and national interests by increasing our understanding of ecosystem functioning, ensuring continued national prosperity as a society dependent upon healthy natural resources. The research addresses a problem with broad implications: reconstructing ancient ecosystem food webs. Food webs represent ecosystem energy flow and are important to ecosystem functioning and services for human societies. Human influence on ecosystems is expanding rapidly, but data on ecosystems’ responses to rapid or extreme impacts is limited. The fossil record contains many examples. Generally, paleontological reconstruction relies upon modern food web properties, but this correspondence breaks down after mass extinctions. Recovering ecosystems deviate significantly from expectations of how ecosystems function. This project will examine marine ecosystems spanning the Permian-Triassic mass extinction, 251 million years ago, when 80% of species became extinct. The project will reconstruct the ways in which ancient ecosystems adapted to extreme disturbances by using fossils collected in the field and deposited in museum collections combined with high performance computing analyses. The project will support and train numerous students and an early career scientist in analytical and museum curation skills. Reconstructing ancient ecosystems relies on models of interspecific interactions, operating under a uniformitarian assumption that the principles determining interactions remain constant through evolutionary time. The resulting probabilistic food webs provide insights into the robustness of past ecosystems. This project challenges the uniformitarian assumption by proposing a framework within which to test alternative assumptions of species properties following extreme ecological disturbances. This project will examine Permian-Triassic communities, including mass extinction-spanning and recovery communities, questioning whether communities immediately after mass extinctions were structured and functioned differently from those during environmentally quiescent times. The project will mobilize new and existing fossil collections, as well as offer paleontological training to community college and high school students. A post-doctoral researcher will participate to enhance analytical skills, develop museum experience, and hone scientific communication. 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 · 2025-07

Autonomous robots will become pervasive in our society and will solve complex tasks, actively collaborating with each other and with humans. As the recent COVID-19 outbreak has highlighted, autonomous robots can solve a range of time-sensitive problems including logistics, reconnaissance, and disinfection of critical areas. Beyond pandemic, small-scale robots can help humans in complex or dangerous tasks such as search and rescue, security, and surveillance, and, thanks to their lighter weight, they pose only a modest risk to human safety. These time-sensitive tasks require robots to make fast decisions and agile maneuvers in complex and dynamic environments. State-of-the-art autonomous navigation approaches, while mature, are slow and brittle and prevent robust and resilient agile navigation. This Faculty Early Career Development (CAREER) Program studies the fundamental perception-action problem for agile navigation of autonomous robots in complex environments by planning a novel, low-latency, robust, adaptive, safe, and resilient paradigm. This project aims also to educate students on the technical aspects, societal benefits, and ethical use of autonomous systems by establishing a unique multi-disciplinary, and integrated research and educational platform which includes a core curriculum on robot localization and navigation, and a series of online racing hackathons for a post-pandemic customized and integrated research and educational experience. These will contribute to lowering the barrier to participation in research and education for students. This project will generate a new foundational theory, which includes models and algorithms resulting from a principled combination of perception, learning, and control to holistically design visual perception and action to create small-scale agile autonomous robots. The goal is to capture the strict cross–coupling effects between perception and action to jointly and concurrently resolve the perception-action problem to speed up the robots’ decision making process and increase their agility. The project is organized in three thrusts according to a series of objectives, culminating in innovations in robotics autonomy research and education. A compressed and unified representation of the perception and action spaces guarantees to reduce the robot's inference latency and naturally reveals the cross-coupling effects among them. Next, the robot will exploit using this representation its action-predictive information to enhance its inference capabilities and will employ an optimal control/planning approach to maximize its perception accuracy and quality. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). 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 · 2025-07

This project will develop analytical methods to approximate the state of certain dynamical systems to within manageable errors, with an eye towards computational feasibility. Dynamical systems provide mathematical models for the long-term trajectories of objects moving according to physical principles. The subject has its foundations in Newtonian mechanics, and features widely in both pure and applied mathematics (e.g., fluid dynamics, airflow dynamics, Hamiltonian mechanics). Hamiltonian mechanics, which provides an alternative formulation to Newtonian mechanics and is particularly useful in classical and quantum mechanics, expresses the time evolution of a system in terms of partial derivatives of a certain energy. The resulting equations, called Hamilton’s equations, describe how the coordinates and momenta of a system evolve over time. The project will seek to further the understanding of quantitative aspects of the analysis of various dynamical systems, under the mathematical rubric of rigidity. The project also provides opportunities for the training and mentoring of early career researchers, especially graduate students. The PI will contribute to the dissemination of mathematical knowledge through the organization of various conferences, workshops, and long research programs. The project resides at the intersection of dynamical systems, ergodic theory, number theory, and geometry. It seeks both to establish new rigidity results and to advance and refine existing results, in the setting of the dynamics of group actions on structured spaces. A major focus is on an enhanced understanding of finitary analysis and effective conclusions. Rigidity phenomena for the dynamics of group actions on homogeneous spaces and moduli spaces have been studied extensively. However, a quantitative understanding of the behavior of orbits has proven to be challenging. This project seeks results in this direction, with an emphasis on systems with polynomial rates, and will also investigate implications of these findings within the domains of number theory and geometry. In addition, recent results of the PI and collaborators regarding finitary analysis on homogeneous spaces will be studied with an eye towards extensions to other settings, including dynamics on the moduli spaces of Riemann surfaces. 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 · 2025-07

This I-Corps project focuses on the development of a software platform designed to simplify and optimize the adoption of electric vehicle charging infrastructure by businesses such as hotels, retail centers, universities, and similar organizations. These organizations face challenges in planning how many chargers to install, selecting appropriate equipment, identifying qualified installation partners, and managing ongoing operations without specialized expertise. Without an integrated solution, businesses risk overspending, underutilization, or operational downtime that negatively impacts revenue generation and customer satisfaction. This project seeks to deliver a scalable platform that provides tailored recommendations for charger planning, connects users with vetted suppliers and installers, and manages charging operations to reduce electricity costs and ensure charger availability. By enabling more organizations to adopt electric vehicle charging infrastructure efficiently and affordably, this solution has the potential to spur economic growth through increased adoption of electric vehicle technologies. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an integrated software platform combining charging optimization algorithms, supplier and installer recommendation tools and operational monitoring to support the deployment and management of electric vehicle charging systems at commercial properties. The platform leverages mixed-integer optimization to recommend optimal charger quantities and configurations while minimizing capital and energy costs. A supplier database and selection engine assists users in choosing reliable equipment and installation partners suited to their site’s needs and local regulations. Real-time monitoring and predictive maintenance features ensure charging infrastructure uptime and operational efficiency. By combining infrastructure planning, procurement support, operational management, and cost optimization within a single system, the technology addresses key technical and logistical barriers to electric vehicle charging adoption in commercial settings, providing users with a streamlined, cost-effective path to electrification. 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 · 2025-07

This project builds an international partnership to advance marine autonomy and to train the next generation of engineers. Undergraduate students from the United States will travel to Norway to collaborate with leading researchers at the Norwegian University of Science and Technology (NTNU). They will participate in hands-on research involving robotic vessels and aerial platforms used to monitor ocean activity. The project is made possible by NTNU’s world-class research infrastructure, including advanced laboratories, testing basins, and a fleet of marine and aerial systems that provide a unique environment for experimentation. Through field experiments, software development, and team-based problem-solving, students will gain valuable skills in robotics, sensing, and data analysis while building international experience. This project also strengthens ties between two key Arctic nations and provides students with mentorship, networking, and career development opportunities in the global marine technology sector. The research focuses on two core challenges in autonomous marine operations. The first area develops control and planning algorithms for robotic ships, ensuring they can navigate safely and efficiently in complex maritime environments, including areas with limited sensor coverage and restricted communication. The second area explores coordination strategies for mixed teams of autonomous platforms—including surface vessels, underwater vehicles, aerial drones, and satellites—to conduct joint monitoring missions. A major use case is the detection and tracking of biological and physical features in coastal waters. The project integrates formal methods, machine learning, and multi-agent control to design robust and adaptable systems. Students test their algorithms in simulation and in experimental facilities, including wave basins and fjord deployments, contributing to real-world advances in autonomous 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.

NIH Research Projects · FY 2025 · 2025-07

Mycobacterium tuberculosis (Mtb) is a highly successful pathogen that infects one-quarter of the world population and causes more deaths annually than any other single pathogen. Mtb can cause infection that lasts for a person’s lifetime. How Mtb can persist in vivo during infection is a question that is central to unlocking new therapeutics for treating this devastating disease. Intracellular pathogens like Mtb must acquire all their nutrients from host cells that have evolved mechanisms to starve pathogens of nutrients. What nutrients Mtb can obtain from host cells, and which are essential for its survival, is poorly understood. Genetic screens are critical for understanding the mechanistic basis of microbial fitness and pathogenesis. Transposon Sequencing screens have been a powerful tool for understanding the biology of Mtb yet are inherently limited in throughput. Thus, it is challenging to screen for genes required to assimilate the myriad of possible nutrients that Mtb could exploit for in vivo growth. We have developed a variant of TnSeq, random barcode transpson site sequencing (RB-TnSeq), in which each transposon inserted into a gene is randomly barcoded. Upon completion of a genetic screen, the presence of specific transposon mutants in a library can be detected by a simple PCR reaction, thus dramatically increasing the possible throughput of screening. To date we have performed a total of 45 RB-TnSeq screens, including on 18 individual carbon sources. From our screening data we propose a hypothesis that we will test in aim 1 of this proposal: that Mtb utilizes both D- and L-lactate as crucial carbon sources for infection of human macrophages. Both enantiomers of lactate are produced abundantly by activated macrophages. Further, we propose that lactate crosses the outer membrane of Mtb through a pore formed by the PPE3 protein. We hypothesize that the type VII alternative secretion system is required for PPE3 export to the cell surface. We show for the first time that Mtb utilizes D-lactate as a carbon source and have identified a possible D-lactate dehydrogenase that we will demonstrate is required for D- lactate assimilation into central metabolism. To broaden our understanding of metabolites used as nutrients by Mtb in human macrophages, in aim 2 we will use RB-TnSeq to identify genes required by Mtb for growth in human primary macrophages. By comparing the results of this screen from the results of our in vitro screens on numerous carbon sources, we will identify a set of candidate nutrients pathways required for Mtb to replicate and persist inside host cells.

NSF Awards · FY 2025 · 2025-07

This research investigates how Americans can make choices that align with their personal values and individual goals—a cornerstone of personal responsibility and self-determination. The project develops innovative methods to measure "choice process awareness," examining how accurately people perceive what influences their decisions. By studying everyday choices in common contexts, we are showing that individuals vary widely in their awareness of choice processes and that increasing this awareness enables people to make choices more consistent with their own stated values. Unlike approaches that impose government or institutional solutions, this work respects individual liberty while addressing costly societal problems where people's actual choices often contradict their conscious intentions. The research employs a novel "Awareness of Choice Processes" (ACP) task that quantifies how aware people are of their decision-making processes and how well these processes align with their personal ideals (a property called “agentic alignment”). The project consists of five studies examining the relationship between awareness and agentic alignment in value-based choices. Studies 1-2 test whether awareness predicts agentic alignment. Studies 3-4 investigate whether increasing people’s awareness helps them make more aligned choices. Study 5 explores whether improving awareness and alignment in one kind of choice helps people build a generalized skill for being agentically aligned in other kinds of choices as well. This research advances a "capability" approach that honors individual autonomy; helping people become more aware of their choice processes enabling them to make decisions consistent with their own values. 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 · 2025-07

Project summary/abstract The correct implementation of developmental programs depends on information encoded in an organism’s DNA. Mutations in these regions that control genes have been shown to be responsible for ailments such as developmental defects and cancer. While great progress has been made in mapping these regulatory regions and uncovering how their target genes interact with each other, it is still not possible to precisely predict patterns of gene expression in space and time from knowledge of the DNA regulatory sequence of multicellular organisms. The overarching goal of the proposed work is to leverage knowledge about these regulatory regions and the transcription factors that bind to them in order to reach a predictive understanding of the developmental program of the early embryo of the fruit fly Drosophila melanogaster. Armed with recent innovations in (1) theoretical models that predict the mean and variability in gene expression as a function of regulatory sequence and input transcription factor concentration dynamics, and (2) technology to visualize and quantify transcriptional initiation in real time in live, single cells in a fruit fly embryo, the proposed investigations will achieve significant progress toward predictive understanding of transcriptional regulation in development. First, through cycles of experiments and modeling, the proposed studies will uncover how pioneer transcription factors dictates transcriptional onset dynamics by regulating chromatin accessibility to activators and repressors. Second, an experiment–theory discourse that leverages synthetic biology will be used to reach a predictive understanding of how the number, placement and affinity of transcription factor binding sites dictates the rate of transcriptional initiation. Finally, we will focus on single-cell transcriptional dynamics and its characteristic transcriptional bursts in order to shed light on the molecular mechanisms underlying transcription and its control. Specifically, we will use our novel compound-state Hidden Markov model to determine whether Dorsal controls burst size, frequency, amplitude, or some combination thereof, in order to generate hypotheses about the mechanisms of action of this activator and determine whether stable clusters of high Dorsal concentration that we recently discovered play an active role in regulating transcriptional dynamics. These investigations will fuel the theory–experiment dialogue necessary for reaching a predictive understanding of developmental decision-making. I envision that, by revealing the dynamic molecular mechanisms underlying transcriptional control, we will be able to write governing equations for gene regulation and, ultimately, engineer cellular decision-making programs for bioengineering and therapeutic purposes.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Soil-transmitted helminth (STH) infections (intestinal worms) affect 1.5 billion individuals globally. Recent evidence from trials and modeling studies suggests that community-wide mass drug administration (cMDA) of deworming medication with sufficient coverage and adherence can eliminate STH transmission. However, in settings with ongoing environmental transmission and low coverage of networked sanitation, persistent environmental reservoirs of STH eggs result in high reinfection rates, and hinder progress towards elimination. STH control programs use human stool-based methods to assess STH prevalence and intensity in endemic settings. However, individual stool sampling is expensive and logistically difficult, particularly when human infection prevalence is low. If collecting and analyzing soil from locations in communities with high human activity (e.g. home entrances, water collection points, schools) were established to be equally or more sensitive than human stool diagnostics, this approach may represent a less invasive and more cost-effective surveillance tool for MDA program monitoring and evaluation. This study will leverage a multi-country cluster-randomized controlled trial delivering three-years of biannual cMDA in Benin and India (DeWorm3). The trial will collect and analyze by qPCR, human stool samples 24 months (n=80,000 in total) after the final round of cMDA, which will yield highly accurate human STH infection prevalence estimates in the study areas. Through extensive laboratory studies and field testing in India and Benin, our team has developed a sensitive and specific molecular method for detecting STH environmental DNA (eDNA) in large volumes of soil. We will nest soil sampling within the trial at the same time point as human stool collection and one year later with the specific aims to: 1) Quantify the effect of a biannual cMDA intervention on the soil STH reservoir; 2) Determine whether soil STH eDNA levels can predict community-level human STH infection prevalence; and 3) Develop the optimal soil sampling strategy and compare costs to human stool-based surveillance. Pairing soil STH eDNA assessments with human infection prevalence data already being collected by the DeWorm3 trial is a unique and time sensitive opportunity to validate and test the utility of environmental STH surveillance. Our findings will also contribute to understanding the conditions under which MDA program integration with improved sanitation interventions is needed for achieving sustained reductions in STH infection prevalence.

NIH Research Projects · FY 2025 · 2025-07

Project Summary Identifying therapeutically efficacious tumor antigens remains a significant challenge in cancer vaccine development, as accurately predicting T cell responses from the bulk tumor immunopeptidome is difficult and costly to validate. Dendritic cells (DCs) play a pivotal role in eliciting anti-tumor T cell immunity, but their unique capacities to cross-present tumor antigens as a main driver of T cell infiltration in tumors have not been thoroughly investigated. This proposal aims to uncover the differences between the tumor immunopeptidome directly presented on cancer cells versus those cross-presented by DCs, and evaluate their contribution to T cell infiltration in tumors. I developed a mass spectrometry-based, antigen discovery platform that empirically identified peptides associated with MHC class I and II molecules on mouse DCs. This proposal will focus on pancreatic cancer, in which cancer-intrinsic and extrinsic mechanisms foster an immunosuppressive microenvironment and lead to T cell exclusion in the tumor. To determine whether immunosuppressive mechanisms impair effector T cell infiltration in tumors by modulating tumor antigen landscape on DCs, I will use in vitro and in vivo models combined with multi-omics approaches to decode DC antigen cross-presentation capacities in mouse and human pancreatic cancer. In Aim 1, I will compare cancer cell- and DC-presenting tumor immunopeptidomes in pancreatic cancer models with high- versus low-T cell infiltration, and test the hypothesis that cancer-intrinsic factors downregulate DC cross-presentation capacities and result in poor T cell infiltration in mouse pancreatic cancer (K99). In Aim 2, I will examine the impact of regulatory T cells (Tregs) on DC’s antigen presentation capacities as extrinsic mechanisms and identify tumor antigens whose presentation on DCs is enhanced following Treg ablation. I will then test the potential of Treg-downregulated antigens as cancer vaccine targets to improve anti-tumor T cell immunity and enhance tumor control (K99-R00). Aim 3 will extend these findings to human pancreatic cancer by examining the correlations between DC tumor antigen landscape and T cell infiltration in patient-derived models. Collectively, these multipronged studies will improve our understanding of cross-presented tumor antigens in driving immunosurveillance in pancreatic cancer and pave the way for novel cancer vaccination strategies. This research will be supported by a multidisciplinary advisory team in tumor immunobiology, including Drs. Michel DuPage (primary mentor, expert in cancer immunology and Treg biology), Joshua Elias (immunopeptidomics and mass spectrometry expert), and Ellen Robey (T cell development and antigen biology expert), and collaborators specializing in pancreatic cancer immunology. The exceptional resources and collaborative environment at UC Berkeley will foster my transition to independence and support my goal of advancing antigen discovery technologies for vaccine development against solid tumors.