Grants

NSF

VR (Virtual Reality)-based immersive and interactive environments can be a great resource for learning and training, especially for concepts that involve safety aspects by interacting with inflammable or breakable objects. The tedious nature of developing these VR experiences continues to be a limiting factor for VR becoming mainstream. The main goal of this project is the design and development of an innovative infrastructure, SMILE (Scan to Multi-sensorial Interactive Learning Environment), focusing on: (1) nearly automated construction of VR environments that mimic real-world indoor scenes; (2) interactions with virtual objects involving multiple senses such as touch, visual, aural, and smell. The SMILE infrastructure will undergo rigorous software testing to ensure safe interactions before being deployed to support Internet-scale collaboration among users. In the exploratory phase of this project, the team will address the following: (1) Democratize the creation of VR environments for subject-matter experts that are realistic in reflecting the ambiance of a real-world scene while providing the needed interactions with objects in the scene. (2) Apply sensorial information directly to 3D objects, enabling multiple sensations during interactions with the virtual objects. (3) Internet-scale collaborations in SMILE through multi-modal inter-sender and inter-receiver skew synchronization, based on the temporal requirements of multi-sensorial interactions. This will be tested by deploying SMILE for use in the sites of the project partners: the University of Texas at Dallas, the University of Illinois at Urbana-Champaign, the New Jersey Institute of Technology, and the Dallas College. (4) A coverage-based fuzz testing module where numerous inputs can be generated and executed efficiently to test corner cases in the SMILE components, and configurable static analysis where algorithms can be flexibly tuned to check the most critical safety properties in SMILE. Virtual chemistry laboratory experiments for assessing the performance of SMILE will be designed and developed through research collaboration with the Dallas College. Through this collaboration, the SMILE project will have a significant impact on the broad undergraduate student community. SMILE could be used for different learning environments, apart from the Chemistry lab case study, promoting education involving K-16. The project will involve doctoral students, undergraduate students, and K-12 students. Open-source resources produced through the SMILE project include a multi-sensory database of material properties for sensorial displays, Unity-based VR authoring tools, and automated software testing tools for VR-based applications. These will be made available for a period of five years through the project website at: https://labs.utdallas.edu/multimedialab/. 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

This project aims to enhance the NSF Aerial Experimentation and Research Platform on Advanced Wireless (AERPAW) at North Carolina State University. AERPAW is one of the projects under the NSF Platforms for Advanced Wireless Research (PAWR) program, supporting wireless experiments with autonomous unmanned aerial vehicles (UAVs). The proposed activities will: 1) develop and release new sample experiments with UAVs and software defined radios (SDRs); 2) release new wireless and UAV datasets; 3) enhance platform user support and add new portal features; and 4) conduct outreach activities to bring new users to the platform. The proposed work will release new sample experiments, post-processing scripts, and datasets, to support research in technologies such as O-RAN, spectrum sharing, trajectory optimization, wireless localization, and artificial intelligence (AI) aided spectrum sharing. The project will create publicly accessible short video tutorials integrated into the user manual, will improve AERPAW user portal, and will continue providing operational support, such as weekly office hours, to AERPAW users. Moreover, the AERPAW web portal and digital twin (DT) will be upgraded by incorporating key features requested by users, such as improved user key management and automated feedback mechanisms based on DT system activity. The broader impacts of the project include continued support for experimentation needs of the CISE research community in the area of advanced wireless systems and autonomous UAVs. The datasets to be released will serve as invaluable resources for developing and testing new AI tools based on real world UAV measurements that are otherwise difficult to capture. Outreach activities will bring new users to the platform and accelerate the transition of fundamental research concepts to real world implementation. The student competition to be hosted will train new users and bring fundamental research ideas to practice. AERPAW website, where project updates will be posted, can be accessed at https://aerpaw.org/. AERPAW User Manual includes instructions on how to use AERPAW and lists AERPAW sample experiments, available at https://sites.google.com/ncsu.edu/aerpaw-user-manual/. AERPAW Experiment Web Portal is available at https://user-web-portal.aerpaw.ncsu.edu/. AERPAW Datasets and software scripts are released through https://aerpaw.org/experiments/datasets/. These pages and repositories will be kept available at least five years following the end of the project term. 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

An emerging form of data processing named cryptographic computing enables computation on encrypted data, providing unprecedented levels of privacy and security. However, these techniques are not widely used today as they run very slowly on existing processors and hardware. This project will implement the infrastructure needed to enable researchers to develop new hardware designs that can overcome these performance limitations and enable cryptographic computing. Throughout this project, named Cryptolets, the team will implement the hardware and software needed to accelerate core cryptographic computing functions in hardware, the infrastructure to connect these pieces, and software to validate it. Cryptolets will focus on three intertwined efforts. First, it will implement and open source a hardware library of key operators and kernels used in cryptographic computing (e.g., modular multiplication, number theoretic transform, and multi-scalar multiplication), and prototypes of existing full protocol accelerators (e.g., homomorphic encryption). Second, it will develop infrastructure needed to connect and build chips. This includes automated scripts to run necessary hardware tools and chiplet interfaces. Since cryptographic computing kernels are complex and large, Cryptolets provides users the ability to break designs into smaller chips and connect them. Third, it will provide tooling support for formal verification, testing for side-channels, and test vectors for validation. Cryptolets will develop suites of educational resources and conduct numerous community building events to advance cryptographic computing. First, the Cryptolets team will develop, record, and release lectures on the fundamentals of cryptographic computing, hardware for cryptographic computing, chiplets, formal verification, and benchmarking. Community building exercises will be done via workshops and online events for experts and newcomers across disciplines to connect. Finally, by releasing Cryptolets infrastructure, the barrier to entry is significantly lowered. Coupled with education and community building activities, the research community is expected to grow markedly. All aspects of the project (e.g., code, lectures, slides, and papers) will be made publicly available for all to access and use. All Cryptolet material and code can be found on the website: cryptolets.org. Websites and repositories are expected to be hosted indefinitely. In addition, archival records of the Cryptolets project (e.g., code, data, reports) will be taken and uploaded at the midway (end of year 2) and final (end of year 4) phases of the project. This will provide a permanent and open backup on the internet for all to access. 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

The COSMOS^3 project is to enhance and expand the COSMOS platform, a city-scale testbed for advanced wireless, networking, and sensing technologies, deployed in New York City (NYC). COSMOS is one of the projects under the NSF Platforms for Advanced Wireless Research (PAWR) program. The project aims to enable studying smarter and safer urban environments by deploying new wireless nodes and sensors—such as cameras, lidars, and radars—across key intersections in New York City. These tools will enable inventing and testing new technologies that could improve everyday life. The COSMOS^3 platform is designed to be open and accessible for a wide range of users to learn, experiment, and create technology that shapes the future of cities. The project is organized into several thrusts: (i) design and deployment of advanced hardware nodes that support a wider range of wireless frequencies (FR1/FR2/FR3) and multi-modal sensing; (ii) development of open-source software tools for managing experiments and processing real-time data; and (iii) development of educational materials and hands-on tutorials to support learning and experimentation. The COSMOS^3 infrastructure enables research on a wide range of next-generation wireless network scenarios including joint communication and sensing (JCAS), dynamic spectrum sharing, edge artificial intelligence (Edge AI), and smart intersections. By integrating programmable radios, multi-modal sensors, and high-performance edge computing, COSMOS^3 provides a flexible platform for experimentation and development that goes beyond what existing urban testbeds can offer, driving the advancement of next-generation wireless technologies. In terms of broader impacts, the COSMOS^3 testbed will accelerate wireless technology innovation, benefiting both the industry and end-users of mobile services. The project will also help to foster a growing community of research users and to encourage the use of the platform at local, regional, national, and global scales. The COSMOS^3 team hosts workshops, internships, and training programs to expand access to the testbed’s resources. By sharing all data, code, and training materials, the project is designed to accelerate discovery, foster collaboration, and enable new research and educational opportunities across many fields. Project information, software, datasets, and experimenter resources for COSMOS^3 are made available at https://cosmos-lab.org/. The project team is committed to maintaining the repository and website for the duration of the award and for at least two years beyond its completion. Long-term sustainability will be pursued through partnerships, community engagement, and integration with other research and education programs, ensuring COSMOS^3 remains a valuable resource into the future. 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

Data is critical to the advances of AI, especially computer vision. Large-scale labeled data, in particular, has been a critical driver of progress in computer vision, the task of making computers see like humans. At the same time, data has also been a major challenge, as many important vision tasks remain starved of high-quality data. This is especially true for 3D vision, which seeks to make computers see 3D shapes from 2D images. To teach computers to see 3D shapes, many images annotated with the 3D shapes are needed, but such annotated images are difficult to obtain in the real world. This project proposes to address the data challenge by using synthetic images, images created by a computer instead of being captured from the real world. In particular, the project proposes to build Infinigen, a free and open-source software program that uses mathematical rules and physics simulation to create realistic-looking 3D scenes covering a wide range of objects in the real world. Infinigen can create an infinite number of annotated images that look like real images and are useful for training AI systems. Compared to existing sources of synthetic data, Infinigen is unique for a number of reasons. It is not a finite collection of 3D assets or synthetic images; instead, it is a generator that can create infinitely many distinct shapes, textures, materials, and scene compositions. Every asset, from shape to texture, from macro structures to micro details, is entirely procedural, generated from scratch via randomized mathematical rules that allow infinite variation and composition. When completed, Infinigen will offer a broad coverage of objects and scenes in the real world from natural objects to artificial ones, including plants, animals, terrains, cities, indoors, and natural phenomena such as fire, cloud, rain, and snow. This project is awarded as a planning project. The project team will take initial steps to engage the community and make preparations for developing Infinigen into large-scale community infrastructure. The project team will build upon an existing prototype of Infinigen and make improvements in terms of capabilities and user services. Infinigen is expected to benefit many CISE disciplines including computer vision, computer graphics, machine learning, and robotics. It can create training data for a wide range of computer vision tasks, including object detection, semantic segmentation, pose estimation, 3D reconstruction, view synthesis, and video generation. Work in this project will be integrated with K12, undergraduate, and graduate education through research training, course development and outreach events. 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

Currently it is costly and difficult to simulate complicated physical scenarios such as tsunamis, earthquakes, or explosions. The mathematical and physical models that describe these effects are highly complex, and solving these models to produce realistic simulations can often require tremendous computing power. Yet physical simulations and digital twins are becoming increasingly crucial tools for researchers in the NSF Directorate for Computer and Information Science and Engineering (CISE), especially as artificial intelligence (AI) models are starting to interact with the real world. Whether considering models that power self-driving cars, household robots, or industrial design tools, a software platform for "physical intelligence" could allow researchers to create a new generation of innovations guided by mathematics and physics. In light of this, this collaborative project brings together investigators from Vanderbilt University, Georgia Institute of Technology, University of California Davis, and Stanford University to create a new, sustainable, community-driven software platform for physical intelligence. The project consists of several main thrusts to build the proposed platform, COSTA (a Community Open Simulation, Training, and Applications framework). The first thrust focuses on developing highly-optimized graphics processing unit (GPU) implementations of common data structures and algorithms used in physical simulations, such as uniform grids, particles, octrees, and linear solvers. The second thrust consists of developing a novel differentiable physics system on top of this GPU infrastructure, based on a partial differential equation (PDE) adjoint framework. This physics system includes algorithms for fluids, solids, and their two-way coupled interactions. Given these capabilities, the project's third thrust involves building a node-based graphical user interface for building physical simulations, as well as integrations with popular AI frameworks like PyTorch. The final thrust of the project involves designing COSTA with a novel cloud-native architecture that will facilitate efficient, large-scale physics simulations on the cloud. Throughout the project, a central aim is broad community involvement, outreach, and impact. For instance, the project involves hosting public workshops on COSTA, building a community governance organization to manage COSTA development, and offering cloud computing credits and training to help onboard the greatest number of users onto the platform. Curricular materials on physics simulation, machine learning, and their combination are another result of the project, and these materials will see integration into undergraduate and graduate courses at the investigators' institutions. Further information about COSTA and its development is available at https://costaproject.github.io/. 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

Researchers have deployed 32-bit Internet address-based (IPv4) network telescopes and honeypots in academic networks and public cloud infrastructure to capture and react to unsolicited Internet traffic that often carries threat intelligence signals. However, the transition to 128-bit Internet addressing (IPv6) presents a fundamental challenge: its vast address space renders traditional (brute force) IPv4 scanning techniques ineffective, requiring new approaches for traffic collection and threat detection. This project introduces iVoyager, a cyberinfrastructure (CI) designed to empower researchers to effectively explore the evolving landscape of Internet threats by gathering cyber threat intelligence across both IPv4 and IPv6 network deployments. This project plans to design and implement iVoyager that provide three capabilities: (1) a flexible virtualized environment to facilitate development and deployment of distributed dual-stack (IPv4 and IPv6) telescopes and honeypots; (2) a proactive telescope that applies novel active techniques to attract malicious IPv6 traffic; (3) deployment of lightweight telescope and honeypot vantage points in public clouds and collaborating networks. The project plans to operationalize the reference design of iVoyager to collect longitudinal datasets that facilitate use of machine learning/artificial intelligence (ML/AI) for cyber threat hunting, anomaly detection, and malware analysis. iVoyager will complement existing CIs by providing additional datasets for more comprehensive cyber threat analysis. The data collected by IPv4 telescopes has enabled hundreds of network researchers to study Internet-wide cybersecurity incidents, such as denial-of-service attacks, malware propagation, and malicious scanning activities. The datasets produced by iVoyager will expand this capability to a dual-stack IPv4 and IPv6 world, with a focus on advanced applications of machine learning and artificial intelligence (ML/AI) for cyber threat hunting, anomaly detection, and malware analysis. The infrastructure will also support deployment of novel experiments and ML/AI-driven methodologies for characterizing IP spoofing, scanning strategies, and other malicious behaviors. These capabilities will inform and enhance cybersecurity practices and policy development. A link to the project website will be provided from https://www.caida.org/funding/circ-ivoyager/ 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

Microbial biofilms are composed of diverse microorganisms and are widespread in natural, human, and engineered environments. Biofilm-related problems exist across major economic and societal sectors, costing billions of dollars each year in energy losses, equipment damage, product contamination, agricultural losses, and medical infections. For over 30 years, scientists have sought to understand biofilms with much of the focus being on single species bacterial biofilms. While much has been learned from single organism studies, biofilms exist as multi-species, multi-domain consortia. Understanding biofilms is crucial, particularly in how their heterogeneous structures and community organization affect their resilience, nutrient access, and susceptibility to antimicrobial therapies. Digital imaging is essential for biological studies and is a backbone of biofilm analyses because of the non-invasive nature and compatibility with live samples. More specifically, imaging has become an essential instrument in biofilm research because of the ability to provide 3D information of hydrated biofilms in real-time. However, a major barrier in biofilm research has been the absence of robust image analysis methods that are easily accessible, centralized, computationally efficient, sensitive, and reproducible. This has hindered the meaningful and statistically robust integration of image analysis into biofilm studies. The Biofilm Imaging Library (BIL) planning project will lay the groundwork for an open-access, community-driven repository of standardized, high-quality biofilm images and the development of open-source easy-to-use computationally efficient tools aided by machine learning. This award will focus on planning the development of the BIL, ensuring that it meets the needs of the research community by establishing key collaborations, identifying data collection strategies, and designing the foundational platform architecture. Indicative of the interest, nine research groups have already begun contributing images following discussions at the 2024 GRC on Streptococcal Biology. Outreach to the biofilm and computer science/engineering communities is planned at the American Society of Microbiology Biofilms Meeting, the IEEE Signal Processing in Medicine and Biology Symposium and the Center for Biofilm Engineering’s Montana Biofilm Meeting. The BIL framework will be established for building a collection of images and metadata, open-source tools, and associated computational models. 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

Large language models (LLMs) have revolutionized various fields, including education, healthcare, media, and national security, enabling powerful AI-driven applications to enhance productivity and decision-making. However, their widespread deployment has raised significant safety concerns about potential misuse, as they can be manipulated to generate harmful or misleading content. As AI safety threats and countermeasures continue to evolve in a dynamic arms race, a critical gap remains: the lack of a standardized evaluation framework to systematically assess the true safety risks of LLMs. This project aims to close this gap by developing an open, community-driven evaluation infrastructure that engages researchers, practitioners, and policymakers. By advancing AI safety research, fostering public awareness, and strengthening workforce training in responsible AI practices, this initiative will support national interests in trustworthy AI, ultimately ensuring that LLMs benefit society in a safer manner. Building on the research and outreach expertise of the project team, this project focuses on four primary objectives to advance community efforts in LLM safety evaluation: (i) conducting structured surveys and interviews with experts across computing disciplines to identify critical safety concerns, assess existing evaluation gaps, and gather insights on infrastructure requirements; (ii) organizing a workshop to facilitate discussions and advancements on evaluation strategies, and refine the design for a shared evaluation infrastructure; (iii) developing a prototype evaluation infrastructure based on gathered insights and iterative feedback, featuring a comprehensive suite that includes a data library, an LLM model zoo, an attack/defense repository, and an evaluator hub, ensuring both usability and scalability; (iv) hosting an LLM safety challenge to engage the community in testing and improving the evaluation infrastructure. Collectively, these efforts will establish a foundational framework that evolves with the community needs, fostering a deeper understanding, comprehensive evaluation, and continuous enhancement of LLM safety. The outcomes of this project not only advance LLM research but also drive progress in the broader community of machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIDA - National Institute on Drug Abuse

Project Summary The primary objective of this research project is to elucidate how circadian rhythms are integrated into neural circuitry important for reward-seeking behavior. Circadian timekeeping is an evolutionarily conserved system essential for the proper function of reward-seeking behavior. By aligning reward behaviors with optimal times of day, animals can seek food, mates, and safety in a manner that enhances survival. Investigating the circadian modulation of reward neural circuitry is therefore necessary to understand the fundamental properties that comprise the neural encoding of reward behavior. This research also has broad implications for human health, as maladaptive reward behaviors underlie various neuropsychiatric diseases, including addiction, bipolar disorder, and depression. Advancing the neuroscience of reward behavior has significant potential for informing the development of therapeutic and diagnostic tools for disorders affecting the brain's reward system. This study aims to determine how circadian rhythms are integrated within the brain's reward system by investigating the bidirectional relationship between rhythms in the ventral tegmental area (VTA) and nucleus accumbens (NAc), two key regions in the dopamine reward pathway. Aim 1 will examine how circadian clock gene expression in the VTA drives rhythms in the NAc, dopamine release, and reward-seeking behavior by selectively ablating critical circadian clock genes in the VTA and measuring downstream effects using long- term fiber photometry and behavioral assays. Aim 2 will investigate the reciprocal relationship, assessing how circadian clock gene expression in the NAc affects circadian clock gene expression in the VTA, dopamine release, and behavior using a similar approach. If these regions operate through a reinforcing feedback loop, it would suggest that circadian rhythms in reward behavior arise from coordinated communication rather than a unidirectional flow of circadian information. These findings could reshape the understanding of neural timekeeping in reward circuits and provide insight into how circadian disruptions manifest at the neural circuit level. This fellowship would support invaluable training opportunities toward the trainee's growth as a neuroscientist. Specializing in both circadian neuroscience and cutting edge tools like long-term fiber photometry, the Jones lab provides the ideal environment to conduct the proposed methodology. Additionally, this research plan will help develop expertise in the dopamine dynamics of reward through collaboration with Co-Sponsor, Dr. Rachel Smith, therefore preparing the trainee for a career at the intersection of circadian and reward neuroscience.

NHLBI - National Heart Lung and Blood Institute

The incidences of co-morbidities, such as obesity and diabetes, continue to rise, undoubtedly contributing to increased cardiovascular disease. This includes rising cases of heart failure (particularly HFpEF), for which in-depth understanding of the mechanisms involved in disease etiology, as well as treatment strategies, remain unmet needs. With respect to the current application, cardiac processes fluctuate dramatically over the course of the day, at molecular, cellular, and organ levels; these rhythms are mediated in large part by the cardiomyocyte circadian clock. We have recently unveiled the beginning of the sleep phase as a period of accelerated cellular constituent turnover, which is critical for cardiac growth and repair. Significance of this phenomenon is underscored by realization that cardiac growth only occurs when the heart is acutely challenged with prohypertrophic stimuli/stresses at the beginning of the sleep period. Moreover, repetitive challenge of the heart with a prohypertrophic stimulus at the beginning of the sleep period accelerates adverse cardiac remodeling and contractile dysfunction in models of heart disease. These studies highlight the importance of temporally coordinating extra-cardiac (e.g., behaviors) and intra-cardiac (e.g., circadian clocks) influences for the maintenance of normal cardiac function. The precise mechanisms promoting cardiac growth at the beginning of the sleep period remain unknown. Here, we provide evidence in support of two distinct contributing mechanisms. Our studies suggest that the cardiomyocyte circadian clock augments branched chain amino acid (BCAA) catabolism in the middle of the active period, when cardiac protein synthesis is lowest. This has led us to hypothesize that increased BCAA catabolism limits BCAA-mediated activation of mTOR and cardiac protein synthesis during the active period (Aim 1). We also provide evidence that the cardiomyocyte circadian clock activates the integrated stress response (ISR; an established translation inhibitor) in the middle of the active period. These data have led us to hypothesize that ISR activation during the active period attenuates cardiac protein synthesis at this time (Aim 2). Surprisingly, 24hr rhythms in cardiac growth appear to be antiphase during HFpEF, peaking during the active period, leading to the hypothesis that aberrant 24hr rhythms in growth mechanisms contribute to adverse cardiac remodeling and HFpEF development during cardiometabolic diseases (Aim 3). Collectively, these novel observations have led to the overarching hypothesis that temporal governance of pathways orchestrating cardiac growth (BCAA signaling and ISR) are impaired during cardiometabolic disease, leading to adverse remodeling of the myocardium, such that reinstatement of normal rhythms will attenuate HFpEF development. Successful completion of the proposed studies will reveal the mechanisms temporally governing cardiac growth, the extent to which these rhythms are impacted by cardiometabolic disease, and the efficacy of chronopharmacologic strategies for the treatment of HFpEF.

NHLBI - National Heart Lung and Blood Institute

Project Summary/Abstract Circadian rhythms of cellular function and physiology are important signals for health, particularly in the lungs and in respiratory diseases. In diseases such as asthma, nocturnal symptoms are a key feature and exacerbations beginning at night from viral infections frequently result in emergency room visits or hospitalizations, but the biology underlying why symptoms and exacerbations occur more often at night is not well understood. Cellular circadian rhythms occur due to intrinsic oscillations of gene expression in a molecular clock that regulates numerous immune genes and pathways implicated in diseases such as asthma. Prior research in mice has indicated that the molecular clock function in airway epithelial cells is important in regulating innate epithelial immune responses to viral infections. However, investigation of circadian regulation in human cells and in clinical diseases such as asthma has been limited. The research proposed in this project will address a current gap in knowledge of circadian regulation in human airway and lung diseases. The project will use an existing biorepository of primary human airway epithelial cells differentiated to an organotypic state at an air-liquid interface from healthy donors and donors with pediatric asthma. Aim 1: Measure viral replication and airway epithelial innate immune responses to human rhinovirus infection in AEC cultures in relation to cellular circadian time. Aim 2: Compare the pattern of circadian cyclic gene expression of innate immune pathways in the airway epithelium of children with asthma and healthy children. Aim 3: Characterize the impact of circadian disruption on epithelial immune responses to infection. The proposed studies will identify epithelial immune pathways under circadian regulation in human airway epithelial cells that can lead to novel therapeutic strategies in the treatment of viral infection and asthma through a circadian biology informed approach. Dr. Powell’s career development plan builds on his background in molecular and cellular biology and clinical expertise in pulmonary and sleep disorders to add training in epithelial innate immunity and viral studies using mechanistic translational approaches, developing expertise in circadian bioinformatics, and methods for molecular manipulation of the core circadian clock in primary human airway epithelia. The K08 award is well- structured to make maximal use of the extensive resources at Seattle Children’s Research Institute and the University of Washington while leveraging the expertise of a mentorship team comprising experts in airway biology, circadian biology, circadian regulation in pulmonary disease, and bioinformatics. The award will lead Dr. Powell to a productive, independent career as a physician-scientist investigating circadian biology in human lung diseases to guide novel therapeutic development.

NIA - National Institute on Aging

PROJECT SUMMARY/ABSTRACT Aging and age-related diseases have become a public health crisis. Circadian rhythms become dysfunctional with age and disrupted circadian rhythms result in elevated risk for diseases that are associated with aging. The proposal aims to improve our understanding of the bi-directional relationship between aging and circadian rhythms. This fellowship application will help train the candidate toward the goal of becoming an independent investigator at an academic institution in the field of circadian aging. A major driver of age-related disease is cellular senescence. There is literature evidence to suggest circadian disruptions result in elevated in cellular senescence. Interestingly, there is also evidence that senescent cells display circadian dysfunction. Mechanisms that govern these processes are incompletely understood and require further investigation to delineate. Our preliminary data suggests expression levels of prominent players in the cellular senescence program are dependent on expression of Bmal1, a core clock component. Further, we have identified that p21, a cell-cycle protein also responsible for driving cellular senescence is highly upregulated at night, but this rhythm is disrupted with circadian misalignment, suggesting the clock is responsible for regulating its expression. We have also identified that both senescence status and treatment with exogenous SASP factors can robustly alter clock protein, Per2 expression rhythms over time. Finally, we have identified that increased markers of cellular senescence and disrupted circadian behaviors coincide with increased age in mice. Taken together, we hypothesize that the molecular clock regulates cellular senescence and that cellular senescence drives circadian dysfunction with age. To test this, we designed these aims: Aim 1: Determine how the molecular clock contributes to cellular senescence. Aim 2: Define the rhythm of senescent cells and determine how they influence the rhythm and circadian functions of neighboring non-senescent cells. We will use both in vivo and in vitro approaches to strategically manipulate the molecular clock and cellular senescence to disentangle their effects on one-another. Using state-of-the-art single-cell imaging techniques and innovative treatment strategies, this proposal will fill in significant gaps in our understanding of mechanisms driving age-associated disease. Further, the candidate will also undertake a rigorous training program at UT Health San Antonio designed to enhance critical thinking and presentation skills, scientific productivity, and successful entrance into a position as an independent researcher at an academic institution. The proposed studies are significant because they will explicate a currently understudied reciprocal relationship between circadian rhythms and cellular senescence. A better understanding of the underlying biological processes that drive aging will undoubtedly provide insight toward development of therapeutic interventions capable of ameliorating an array of diseases. This proposal will also support the scientific endeavors and training of a young scientist with significant potential to run her own state-of-the art research program in the future.

NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases

Project Summary/Abstract Circadian clocks are ubiquitous in tissues, regulate many metabolic functions, and their misalignment or disruption contributes to deleterious health consequences. Although molecular underpinning of the circadian clock is well-studied, the role of the clock in tissue-specific metabolic functions, such as that of tanycytes, is poorly understood. Tanycytes, specialized hypothalamic glial cells, gate the entrance of nutrients and hormones to the brain, signal to hypothalamic neurons, and generate new neurons relevant for feeding and sleep. Circadian rhythms in peripheral signaling to the brain are necessary to maintain whole-body energy homeostasis, but the contribution of cell-autonomous circadian rhythms in tanycytes to this process is completely unexplored. My recent work has identified the presence of these rhythms in tanycytes and found that the core circadian clock gene Bmal1 in tanycytes regulates leptin signaling, diet-induced weight gain, and adult neurogenesis in female mice. This proposal examines the mechanisms of circadian regulation of tanycyte endocrine signaling and neurogenesis, their relevance to feeding behavior, and the contribution of diet and sex differences to these processes. I hypothesize that there is an autonomous circadian rhythm in tanycyte leptin signaling to the arcuate nucleus of the hypothalamus, the brain's feeding control center (Aim 1). Based on published and preliminary data, I propose that tanycyte circadian rhythms influence feeding behavior through endocrine signaling and neurogenesis (Aim 2). I further hypothesize that tanycyte neurogenesis is controlled by circadian rhythms both at the level of initial cell division and differentiation (Aim 3). Finally, I will interrogate the effects of sex hormone signaling on tanycyte circadian-metabolic function (Aim 4). To pursue these aims, I will use a combination of cellular and molecular assays (immunolabeling, single-cell RNA sequencing), functional bioassays (BrdU cell division labeling, stem cell lineage tracing, metabolic phenotyping) and behavioral assays. Successful completion of this project will offer important advances in understanding both circadian rhythms in tanycyte metabolic function and behavior. First, understanding molecular mechanisms of the tanycyte rhythms will further define the role of the clock in gating periphery to brain communication. Second, it will provide new insights into the regulation of food intake. Third, it will identify novel pathways through which diet and sex differences influence the circadian transcriptome. Understanding the temporal gating of tanycyte function under pathophysiologic conditions is important for developing interventions to improve human health. Plan for Career Development: My training plan leverages a highly complementary co-mentorship arrangement between Dr. Amita Sehgal and Dr. Stewart Anderson who possess deep expertise in circadian rhythms/metabolism and stem cell metabolism/neurogenesis respectively. I will receive significant consultative support from Dr. Ron Anafi of the University of Pennsylvania Center for Subcellular Genomics and Drs. Joseph Baur and Corey Holman, directors of the Rodent Metabolic Phenotyping Center at Penn. My research and transition to independence will benefit from this strong mentorship team, state-of-the-art facilities/equipment, and professional development resources offered through the Penn office of Biomedical Postdoctoral Programs.

NYC Department of Cultural Affairs

Circle in the Square Theatre School, Inc.

Smithsonian National Museum of the American Indian

Circle of Dance: A Promotional Video

NIEHS - National Institute of Environmental Health Sciences

PROJECT SUMMARY Aging is a major risk factor for numerous diseases and is modulated by both genetic and environmental factors. However, dissecting the interplay between environmental stressors and aging remains challenging. This is partly because traditional stress markers are transient, complicating the retrospective analysis of environmental stimuli and their contribution to the aging process. Consequently, there is a critical need for novel, stable markers that can track both the aging trajectory and the life stress history of an organism, including exposure to toxins and underlying health conditions. Such markers could significantly enhance our ability to predict the propensity to develop age-related disorders. Circular RNAs (circRNAs) are a highly stable and abundant class of RNA generated by circularization of specific exons. Their stability allows them to accumulate with age, particularly in non-dividing cells such as neurons. Indeed, our findings indicate that circRNAs exhibit very long half-lives (exceeding 20 days) in the Drosophila brain, with some showing no degradation over the animal’s lifetime. Moreover, we found that circRNAs can accurately track age, and that their levels are influenced by environmental stimuli such as oxidative stress, temperature, light exposure, and starvation. This combination of exceptional stability, capability to function as age markers, and induction by environmental stimuli implies that circRNAs can serve as life experience markers. Indeed, flies subjected to a ten-day regimen at 29°C exhibit higher levels of specific circRNAs even six weeks after returning to standard conditions, indicating that circRNAs can reveal past exposure to environmental stimuli. This positions circRNAs as unique molecules capable of integrating aging and stress information which could help uncover early-stage stages of disease or organ malfunction and determine if animals have been exposed to dangerous substances in the past. Additionally, these results suggests that circRNAs induced upon exposure to stimuli could be relevant for the responses upon re-exposure to the stressor. In this project, we aim to establish circRNAs as robust markers for tracking the integrated transcriptome and stress history of populations during aging. In this context, we plan to: 1) Identify circRNA subsets for tracking chronological age, aging and environmental stress exposure. and 2) Determine the encoding capacity of circRNAs as life experience and aging markers. By refining circRNA signatures to accurately reveal the stress history of Drosophila populations and their relationship to aging, we aim to develop new predictive markers for age-related diseases. This project is particularly relevant in the context of an aging human population and in the search of new markers that could predict the presence and predisposition to age-related diseases.

NIAAA - National Institute on Alcohol Abuse and Alcoholism

PROJECT SUMMARY: Alcohol acts on the brain’s natural reward circuits and has a multifaceted influence on food reward circuits. Consumption of alcohol primes the release of dopamine and can alter the physiology of the reward system, profoundly affecting circuit homeostasis, plasticity and function. This results in aberrant reward encoding, leading to aberrant value-based choices and maladaptive behavior. However, the heterogeneity of the mammalian reward system makes targeting distinct subpopulations of cells difficult, which has precluded a detailed understanding of how reward is encoded. The reduced numerical complexity of the Drosophila reward/dopaminergic system provides a tractable alternative to study cellular resolution mechanisms of reward memory and reward seeking. Like mammalian mesolimbic dopamine neurons, reward-encoding dopaminergic neurons that innervate the Drosophila mushroom body are diverse in molecular composition, neural connectivity and function. Moreover, we have shown that dopaminergic activation of the mushroom bodies underlies that rewarding effects of alcohol in flies. This provides an ideal functional framework for investigating the cellular and molecular mechanisms through which rewards with different values, such as sugar and alcohol, are encoded and how they interact. This work will reveal mechanisms through which relative reward value is encoded and how alcohol influences reward circuit function to elicit unconstrained reward seeking. Although we all have an intuitive understanding of what is very rewarding, and what is less rewarding, our memories of reward change with experience. Our first goal is to understand how absolute and relative sugar rewards are encoded in a dopaminergic memory circuit. Our second goal is to understand the mechanisms through which alcohol is weighted relative to sugar, and how prior experience with alcohol alters how sugar reward is encoded. Our third goal is to identify the molecular mechanisms through which alcohol can alter sugar motivation by addressing how alcohol-induced alternative splicing impacts sucrose response and encoding. Overall, this research will identify causal physiological and molecular mechanisms through which alcohol influences the formation and expression of reward memory.

NEI - National Eye Institute

Project Summary Visual processing transforms sensory inputs into signals used to guide behavior. These transformations occur over multiple stages of the cortical hierarchy to generate representations that are increasingly invariant to nuisance features such as size, angle and lighting. Such invariance is fundamental for scene segmentation and object recognition. Thus, understanding the cellular and circuit mechanisms underlying these transformations will provide key insights into cortical function. In this project, we will dissect invariant coding of motion direction to the specific components of the stimulus. For instance, when multiple drifting gratings are superimposed to form a plaid, the perceived direction is in the pattern direction, rather than that of the individual components. “Pattern cells” that encode this direction have been identified in the visual system of primates, carnivores and most recently rodents, suggesting that these cells are important for the perception of motion direction. The predominant model proposes that pattern cells are constructed through a series of sequential transformations: direction selectivity is established first, followed by spatial invariance and finally motion invariance. Our preliminary data from mice and marmosets argue against this clean hierarchical transformation, revealing the presence of spatially dependent pattern cells in V1 of mice and marmosets. Here we will test the hypothesis that the foundation for pattern motion is first laid by spatial inhomogeneities in the feedforward pathway and that the major computation is to generate a spatially invariant representation in the higher visual areas (HVAs). In Aim 1 we will examine how thalamic drive supports pattern selectivity in V1 of rodents and primates. We will use extracellular recordings to compare how spatial receptive fields create pattern selectivity and then use intracellular recordings in conjunction with optogenetics to determine how thalamic inputs sculpt these representations. These data will be used to build a receptive field model of cortical processing that explains cellular responses to plaids in V1. In Aim 2, we will determine how motion and spatial invariance increase from V1 to the HVAs in marmosets and mice (areas MT and AL). We will interrogate this transformation by using pre- and post-synaptic imaging approaches to test the hypothesis that spatially variant signals in V1 converge onto individual neurons in the higher areas to generate invariance. In Aim 3, we will investigate the contribution of local inhibitory circuits to pattern selectivity. We will use two-photon imaging to assess the plaid direction selectivity of inhibitory interneurons, and whole-cell recordings to determine how inhibitory inputs are integrated. Finally, these data will be used to elaborate our computational model to determine the role of recurrent circuits in the generation of pattern selectivity. Together, these experiments will provide a mechanistic explanation for the construction of an invariant cortical representation, from the level of synaptic inputs onto molecularly defined cell types within and across cortical areas in two species.

NIDCD - National Institute on Deafness and Other Communication Disorders

PROJECT SUMMARY We integrate sensory signals, like smell and taste, to make decisions about the type and quantity of food to eat on a daily basis. Distinct sensory inputs converge in brain regions that regulate food intake, such as the gustatory cortex, but the neural circuit mechanisms that integrate sensory information to control food consumption remain largely unknown. The fruit fly Drosophila melanogaster provides an excellent model system to address this question, as we can genetically target individual neurons in the fly brain and use the whole-brain connectome to identify pathways linking sensory input to motor output. Just as humans use multiple sensory cues to evaluate the quality of food, flies use distinct taste inputs from their legs and feeding organ, the proboscis, to decide whether to initiate feeding. This study will determine where and how neural circuits in the fly brain integrate concurrent taste input from the legs and proboscis to regulate feeding, providing a model to more broadly understand the logic of how neural circuits integrate different food-related signals. The experiments in Aim 1 will determine how sugar inputs on the legs and proboscis are integrated to regulate the likelihood that flies will initiate feeding and the quantity of food they consume. In vivo calcium imaging experiments in Aim 2 will determine how concurrent sugar input on the legs and proboscis influences the activity of individual neurons in a neural circuit known to respond to sugar and promote feeding initiation. These recordings of neural activity, combined with an analysis of neuronal connectivity in the whole-brain connectome, will provide a neural substrate for the behavior identified in Aim 1. Together, the findings from this study will identify the neural circuit mechanisms that integrate distinct taste inputs to promote adaptive feeding behavior. The basic principles of chemosensory processing and its role in feeding regulation are conserved across species. Thus, future work can use our findings to develop hypotheses about the neural circuits that combine sensory cues to regulate food consumption— and investigate whether these circuits contribute to dysregulated eating— in rodents and humans.

NIA - National Institute on Aging

Project Summary Alzheimer’s disease (AD) currently affects ~6.7 million individuals in the U.S. alone and is expected to grow to 13 million by the year 2050. A major component of its devastation is the progressive loss of the patient’s ability to form memories. Treatments for rescuing memory function in AD patients do not exist, due in part to insufficient research performed to characterize the activity of memory-supporting neural circuits compromised by the disease. The effort to develop memory-restorative therapeutics that intervene in neural circuits requires vulnerable neuronal subtypes impacted during pathophysiological progression, and how they relate to memory performance. Neurons in the entorhinal cortex (EC) act as a gateway for sensory inputs feeding into the hippocampus. This EC-hippocampus circuit is critical for memory formation and retrieval. Despite its significance to AD pathophysiology, it remains unclear which circuit components are vulnerable in the EC of AD patients or animal models. Our laboratory’s pioneering previous research has developed exciting preliminary results that suggest a path forward. Our overall hypothesis is that circuit vulnerability of the EC, including the lateral entorhinal cortex and medial entorhinal cortex, to tauopathy causes associative memory impairment in AD. We will use two Tau mouse models to identify EC cell types vulnerable to tauopathy. The project is expected to yield advances toward the development of therapeutics to mitigate associative memory functions impaired in AD.

NSF

Cellular wireless communications are an integral part of modern life, with the demand for high data-rate communications continuously increasing. To support this need, the cellular infrastructure in the United States uses a combination of legacy frequency bands from 4G standards as well as newly allocated spectrum for 5G/6G systems. As a result, a cellular base station must support a variety of frequency ranges and standards, often simultaneously in the case of carrier aggregation. Conventional industry solutions use a single radio-frequency (RF) hardware chain to serve each band, for example using separate dedicated hardware for the two Advanced Wireless Services 1 (AWS-1) bands of 1805-1880 MHz and 2110-2155 MHz even though these frequencies are relatively close to each other. The RF front-end hardware therefore exists in duplicate or triplicate, depending on the range of frequencies to be covered. If a single transmitter could operate instead over the entire frequency range of 1805-2155 MHz, this would cut in half the size, cost, complexity, and capital investment for the cellular base stations. At the same time, a successful broadband solution must not incur any energy efficiency penalty, as a reduction of even five percentage points is prohibitive in terms of operating costs and the consequent need for heat removal. This efficiency requirement precludes the use of existing hardware solutions for bandwidth extension, which are well-known to exhibit a bandwidth-vs-efficiency tradeoff. This project will develop alternative solutions involving both new hardware design and machine learning (ML) algorithms to address these challenges. The project will also train an engineering workforce able to solve challenges from a multidisciplinary perspective. The aim of this project is to address the growing demand for high-power RF transceiver front-ends capable of operating over multiple bands without incurring any energy efficiency penalty through the co-design of RF transceiver hardware and signal processing software. The majority of previous efforts addressing the bandwidth-vs-efficiency tradeoff have approached this problem from a purely circuit design perspective. Similarly, linearization of, and signal generation for, cellular RF transceiver front-ends falls squarely in the domain of signal processing design. The research of this project, on the other hand, approaches the challenge from the basis that a true wideband and efficient solution cannot arise from a narrow technical approach, but instead requires a cross-disciplinary solution incorporating both hardware design and artificial intelligence (AI)/ML-assisted signal processing that can control the data signals to be transmitted in a way that optimizes both spectral efficiency and energy efficiency. The new research approach of this project eliminates the artificial boundary between hardware and software in conventional designs which typically pre-determine the signal generation strategy for a dual-drive power amplifier. By allowing the signal processing and separation to be fully two-dimensional, an additional degree of design freedom is enabled. The project exploits this new 2D design space for both hardware design and signal processing to optimize bandwidth and efficiency. Because of the complexity of the function space, optimization tools are employed. The end goal is a co-design of hardware and signal processing. Beyond these technical goals, the project supports US workforce development for the wireless industry by training RF engineers with critical interdisciplinary skills in both hardware design and AI/ML tools for the next-generation wireless communication 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.

NIDA - National Institute on Drug Abuse

Project Summary Human immunodeficiency virus type 1 (HIV-1) currently affects over 38 million people worldwide. With combined antiretroviral therapy (cART), HIV-1 is no longer a death sentence but a chronic disease with an inflammatory component. HIV-1 virus enters the central nervous system (CNS) early in infection, and while cART effectively suppresses viral replication in peripheral tissue, the CNS remains vulnerable. As cART does not cross the blood- brain barrier, the prevalence of mild to moderate cognitive impairments, known as HIV-1-associated neurocognitive disorders (HAND), remain high (50%). Brain regions specifically vulnerable to HIV include the fronto-hippocampal system, involving deficits in learning and memory behaviors. The release of viral proteins (i.e. Tat, Nef, gag) from HIV-1 infected brain resident cells plays a significant role in the underlying HAND neuropathology, including lasting changes in neuronal excitability, inflammatory processes, and synaptic communication. The endocannabinoid (eCB) system has attracted interest as a target for treating neurodegenerative disorders due to its potential neuroprotective, anti-inflammatory, and neurotrophic properties. However, due to psychoactive side effects, the therapeutic potential of cannabinoid type 1 receptor (CB1R) agonists is limited. Therefore, efforts are now focused on alternative targets of the eCB system for its therapeutic potential, including allosteric modulators. Published studies suggest that CB1R positive allosteric modulators (PAMs), including ZCZ011, GAT229, and ABD1236, are promising candidates as they can enhance CB1R activity indirectly without producing psychoactive effects, tolerance, or dependence. Additionally, as disease can alter eCB signaling, the use of PAMs may help to compensate and return the CNS to its normal state. Our preliminary data demonstrate neuroprotective effects of ZCZ011 against HIV-1 Tat-induced excitotoxicity as well as improvement of recognition memory in HIV-1 Tat transgenic mice. GAT229 has been shown to reduce behavioral signs and proinflammatory cytokines in rodent models of disease (e.g. neuropathic pain, Huntington’s disease); and preliminary data for ABD1236 demonstrate enhanced eCB signaling. We will test an innovative hypothesis that HIV-induced alterations in the mPFC→HPC neural circuitry predict deficits in mice performing a working memory task, which can be prevented by positive allosteric modulation of the CB1R. To achieve this goal, we will use well-established mouse models of neuroHIV/HIV and propose three aims. Aim 1 will determine how CB1R PAMs alter neuronal activity in the mPFC→HPC neural circuitry in vivo of HIV-1 Tat transgenic mice performing a working memory task. Aim 2 will determine the effects of HIV on eCB-mediated short-term synaptic plasticity in the mPFC→HPC circuitry of HIV-1 Tat transgenic and EcoHIV-infected mice ex vivo. Aim 3 will determine whether CB1R PAMs protective effects against EcoHIV-induced toxicity in vivo are mediated via actions at CB1R-expressing glia or neurons. This work will provide a foundation for characterizing the role of alternative targets of the eCB system in modulating memory circuitry in the context of HIV.

NINDS - National Institute of Neurological Disorders and Stroke

PROJECT SUMMARY The ability to appropriately respond to threats is crucial for survival. Literature has long suggested that arousal and attention, mediated by the neuromodulators norepinephrine (NE) and acetylcholine (ACh), influence sensory processing. NE and ACh release affect behavioral states and are released in response to novelty, attentional effort, and threat. However, the unique contributions of these neuromodulators in regulating behavioral states, and circuits which might underly the release of these neuromodulators in response to salient threat stimuli, remain unclear. It is possible that salient visual stimuli alter neuromodulator release through direct projections of the superior colliculus, resulting in enhanced sensory processing in response to threat, but this circuit has not been directly tested. Height threat is an evolutionarily conserved, ethological visual threat behavior that can be used to study neural circuits underlying threat processing. The visual cliff test is an optical illusion which generates the appearance of a cliff. Both rodents and human infants avoid crossing the threshold of the visual cliff. In rodents, this behavior depends on visual perception and binocular vision. Humans exposed to virtual height threats exhibit increased physiological arousal which correlates with perceived height level. We developed a virtual visual cliff task to study the relationship between neuromodulators, attention, arousal, sensory processing, and threat-related decision-making. Mice climb down a pole towards a virtual cliff and exit to one of four quadrants, one which appears close and three which appear distant. Threat level is modulated by altering the apparent distance between the close and distant quadrants. Head-mounted eye tracking will be used to measure arousal via pupil diameter and attention via gaze and head direction, which will allow us to differentiate the roles of attention and arousal in visual processing and decision-making. This research will elucidate how neuromodulators influence sensory encoding and behavior in response to visual threats, as well as the role of sensory systems in engaging neuromodulator release to enhance sensory processing in response to salient visual threats.