University Of Texas At Austin

NSF Awards · FY 2024 · 2024-09

Integration of different types of electronic chips using 3D packaging has recently attracted significant interest due to the ability of these systems to incorporate multiple functionalities into a single electronic package. However, current manufacturing processes for interconnect fabrication are not well suited for this type of integration because of inflexible processes, poor resolution, and low throughput. Microscale selective laser-sintering (μ-SLS) is a microscale additive manufacturing process that offers the potential to overcome these limitations by enabling the rapid production of sub-5 µm parts with 3D structure and good electrical conductivities. Unfortunately, the parts produced using μ-SLS often suffer from poor dimensional accuracy, which causes their performance to fall outside of the required tolerances for electronic packaging applications. These poor tolerances are caused by uncontrolled heat spread from the projected laser mask image to the surrounding areas, preventing the final part from closely matching the desired mask image. New uncertainty-aware model-based control strategies, that aim to account for the sources of variability in the μ-SLS process, will be investigated in this Grant Opportunity for Academic Liaison with Industry (GOALI) project to bring μ-SLS to commercial viability. The performance of the μ-SLS process control strategies will be tested in one of GOALI partner NXP Semiconductor’s prototype packaging lines. This project will also enhance workforce development through the creation and implementation of educational programs focused on the role of computational modeling in manufacturing for students from underrepresented backgrounds. The ability to accurately model and quantify uncertainty in the µ-SLS process is critical to be able to generate optimal control inputs to achieve desired part geometries within specified tolerance limits for µ-SLS parts. Quantification of the sources of uncertainty and the ways in which those uncertainties propagate through the model-based control will be done through: (1) physics-based modeling with uncertainty propagation to accurately model the μ-SLS process and produce part estimates with known tolerances, (2) uncertainty aware data-driven modeling to speed up part predictions without sacrificing accuracy so that these models can be used in model-based control, and (3) model-based control to solve the inverse design problem of finding the optimal mask sets to produce μ-SLS parts with minimal dimensional errors, and to quantify these errors through rigorous uncertainty propagation. Expected outcomes of this work include improved understanding of: (1) the physical causes of uncertainty and tolerance errors in the μ-SLS process, (2) how inaccuracy/uncertainty propagate through the modeling of the μ-SLS process, and (3) the fundamental limits in model-based control of reducing part errors and improving tolerances. These new understandings will enable the μ-SLS process to produce parts with sufficient accuracy and repeatability to meet the requirements for advanced electronics packaging applications and thus provide an additive manufacturing method capable of replacing the dozens of process steps usually required in electronics packaging. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Although ambulatory blood pressure monitoring (ABPM) has been used to detect sources of cardiovascular mortality (e.g., hypertension and abnormal BP rhythms) for over six decades, current ABPM devices are still not yet widely adopted by the general population. Cuffless ABPM approaches are clearly more user-friendly, but have yet to achieve acceptable accuracy. The objective of this SCH project is to create a paradigm-shifting cuffless ABPM device based on biomechanics-guided wireless ultrasound e-tattoo sensors. The specific aims include (1) designing an ultra-low-power and millimeter-size application-specific integrated circuit (ASIC) capable of duplex mode ultrasound to be used in a soft, wrist-laminated, wireless ultrasound e-tattoo (WUET) for continuous monitoring of absolute anatomic and fluidic features. (2) establishing a fundamental biomechanics model to extract BP using the aforementioned hemodynamic measurables. (3) pilot clinical validation of the WUET on patients with arterial catheter insertions in Dell Children's Medical Center using SickbayTM virtual patient monitors for simultaneous data acquisition. The four PIs bring together well-established expertise in analog/mixed ASIC design (Jia), low-power wireless e-tattoos (Lu), cardiovascular biomechanics (Han), and pediatric cardiac intensive care (Mery) to synergistically tackle this grand challenge. This approach fundamentally differs from previous cuffless continuous noninvasive BP (cNIBP) sensing methods that rely on traditional wearables modalities only capable of measuring relative and indirect hemodynamic features. Such modalities then require empirical (e.g., machine learning) models for cNIBP despite inherent inaccuracies and constant calibration requirements caused by poor understanding of the underlying mechanisms and bias to training data. While ultrasound can capture the absolute metrics required for biomechanics-based models, state-of-the-art wearable ultrasound sensors are still constrained by bulky back-end control and data acquisition systems. The proposed analog-edge-computing of hemodynamic feature extraction enables true wireless implementation by dramatically reducing power consumption and sensor size. Our preliminary results demonstrate the feasibility of anatomic and Doppler-based feature extraction purely using analog circuitry. The proposed ABPM WUET can ultimately replace invasive arterial catheters in clinical spaces while also enabling ambulatory monitoring of the broader outpatient populations. RELEVANCE (See instructions): Cardiovascular disease is most effectively treated through early diagnosis and preventative measures, and this project provides a new method of cuffless blood pressure monitoring that is still accessible and patient-friendly. A greater fundamental understanding of vascular biomechanics and a new wearable vascular measurement modality will be established. This project is committed to NHLBI's mission for advancing translational research, with focuses on real patient usability in clinical and outpatient settings.

NSF Awards · FY 2024 · 2024-09

The first galaxies formed from pristine hydrogen gas within the first billion years of cosmic history, but our theories cannot fully explain how. A team of scientists from the University of Texas, Austin, will tackle the questions of how the first galaxies assembled their stellar mass in the first billion years, what was their cosmological context, and what were the drivers of cosmic reionization. The team will provide mentoring for undergraduate and graduate students, as well as a postdoc. In an effort to promote scientific discussion in Hispanic and Latinx communities, the PI will partner with the McDonald Observatory in West Texas and for the first time develop one of their educational DeepSkyTour videos entirely in Spanish. The next decade will see detailed observations of the 21-cm line of neutral hydrogen, which will revolutionize our understanding of the first galaxies by probing their spatial distribution. This is key to breaking degeneracies in our theoretical models of the first cosmological structures. Extracting this information, however, requires careful and efficient modeling of the 21-cm signal beyond the spatial average. This project will provide this modeling through fast, flexible, and robust theoretical tools that will be made publicly available to the community through the Zeus21 software package. The team will implement stochastic galaxy formation on 21-cm models, study whether the reionization process was dominated by large or small bubbles, and develop new cosmological tests during reionization. This project will reveal the astrophysics sculpting the first cosmic structures, and in addition to scientific publications, talks, and public software releases, it will provide ultra-efficient 21-cm map-making techniques for both educational and scientific purposes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Developing batteryless technology for Internet of Things (IoT) devices offers a promising future, especially for wearable technologies like fitness trackers, smartwatches, and medical devices. The project addresses fundamental issues such as the reliability and functionality of these devices without traditional power sources. By integrating artificial intelligence (AI) and deep learning techniques, this project aims to harness the potential of batteryless sensors for personalized data analytics. This integration requires substantial intellectual innovation, particularly in aligning machine learning (ML) with systems design. Consequently, this project not only aims to transform edge computing, but also has far-reaching implications for healthcare, IoT, and personal augmented reality/virtual reality (AR/VR) applications. Beyond technological advancements, the project has a significant educational impact as it proposes an interdisciplinary, research-based curriculum that combines machine learning (ML) and batteryless system design. This educational approach will foster a new generation of innovators equipped with the skills to advance both ML and batteryless technologies. In terms of new methods, this project seeks to develop new deep learning algorithms tailored for unstructured data generated by batteryless sensors, which differ fundamentally from traditional ML settings. A joint optimization approach is proposed to balance the energy usage strategies of batteryless sensors with the requirements of ML models for data analytics. The project will also develop a novel approach for selecting sensors specifically for batteryless systems. Real prototypes using kinetic energy harvesting will be built to validate simulations through hardware profiling. These prototypes will facilitate the collection of a real-world dataset, providing a comprehensive methodology for integrating batteryless sensors with ML. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Nontechnical description In semiconductors, a photon of sufficient energy can generate an electron-hole pair, which forms a bound excitonic state due to the attractive Coulomb force. The recombination of excitons is critical for light-emitting applications, whereas the dissociation of excitons into free carriers plays a key role for photovoltaics and photodetection. Combining the illumination in the optical regime and detection in the low-frequency microwave regime, the research team performs variable-temperature electrical probing of optically excited quasiparticles in a spatially, temporally, and spectrally resolved manner. This work contributes to the fundamental understanding of low-frequency response of optical excitations and exotic quantum phases in moire superlattices. An integrated research and education program at University of Texas at Austin is established such that students at different levels are trained to master modern nanofabrication techniques, microwave engineering and laser optics, and scanning probe microscopy, which prepares them for future careers in science or engineering. Technical description The interaction between light and semiconductors is dominated by the generation, recombination, and dissociation of excitons. Traditionally, studies of bound excitons and mobile charges are performed in different spectral regimes, with either insufficient spatial or temporal resolution. In this project, the researchers conduct cryogenic laser illuminated microwave impedance microscopy experiments for the study of transport and diffusion of excitons and trions in two-dimensional monolayers and heterostructures, as well as local properties of exciton insulators and topological moire excitons, with high spatial, temporal, and spectral resolutions. As a result, the team can extract the effective permittivity of excitons in monolayer semiconductors, visualize the transport and diffusion of excitons and trions with high spatial resolution, evaluate the electrical properties of intralayer and interlayer excitons, and probe the correlation and topology of moire excitons. The work is important for optoelectronic applications of two-dimensional materials and heterostructures under intense illumination. The research is at the cutting edge of nano-optoelectronic materials systems and aligned with the mission of the NSF Electronic and Photonic Materials Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Preeclampsia (PE) is a relatively common pregnancy disorder that originates from the placenta. It affects up to 8% of all human pregnancies and causes various maternal and fetal health problems, including maternal vascular dysfunction, proteinuria, and hypertension as well as growth restriction and preterm birth of the baby. It has no cure other than the delivery of the baby and can lead to eclampsia, which may result in death via stroke. It is generally accepted that defects in trophoblast lineage development, critical for proper implantation and placentation, are the leading cause of PE. However, the underlying molecular mechanisms of PE have not been well understood. While many genes upregulated or downregulated in PE have been identified by global gene expression analysis, upstream key transcription factors (TFs) responsible for the PE-specific gene expression programs (PE-driver TFs) and their action mechanisms have not yet been described. Furthermore, how such PE-driver TFs disrupt normal trophoblast lineage- specific gene regulatory networks remains unknown. The objectives of the proposed studies are to identify transcriptional and epigenetic regulators modulating PE-specific gene expression programs and to understand their action mechanisms by utilizing recently established human trophoblast stem cells (TSC) and their in vitro differentiation into syncytiotrophoblast (ST) and extravillous trophoblast (EVT) as a model system. Our preliminary studies suggested that the untimely or sustained upregulation of sequence- specific TFs may trigger abnormal expression of target genes known to be PE-specific, which may lead to impaired ST/EVT differentiation and, ultimately, PE phenotypes. To understand the molecular basis of PE, we will 1) identify and functionally validate putative PE-driver TFs which can induce PE-specific gene expression programs by utilizing human TSC differentiation as a model and 2) understand the regulatory mechanisms PE-driver TFs by mapping their downstream target genes and interaction partner proteins. The successful completion of this proposal will provide us with new models for studying the pathophysiology of human PE, illumination of molecular regulatory mechanisms underlying PE, and thus enhance our ability to generate novel diagnostic tools and therapeutics to improve healthcare for both the women with PE and their babies in the near future.

NIH Research Projects · FY 2025 · 2024-09

Conditional Animal Models of mtDNA Disease: Abstract Mitochondria are critical for metabolism, organ homeostasis, apoptosis, and aging. This wide range of impact is manifested by the enormous biological variation and diverse disorders in patients with mitochondrial disease affecting organs such as the eye, ear, brain, muscle, and kidney. Understanding how mitochondria function in normal biology - and how human mitochondrial DNA (mtDNA) variations contribute to health and disease - has been hampered by a lack of animal models due to limited approaches to manipulate this powerhouse of the cell. This project aims to resolve this major technical bottleneck in the field to enable us to deploy a full complement of gene editors to introduce precise edits in the mitochondrial genome in a manner capable of temporal and spatial control and to use these to make the first conditional animal model of mitochondrial disease using zebrafish. CRISPR gene editing tools have enabled the rapid manipulation of the nuclear genome. CRISPR systems are dual molecular, with Cas protein and gRNA components. They are not currently operational in the mitochondrial compartment however because of a lack of effective methods to deliver exogenous guide RNAs. We have identified an endogenously trafficked RNA that can carry targeted nucleotide changes from the nucleus into mitochondria, representing a potential novel method for the introduction of programmed RNAs suitable for use as guide molecules for CRISPR Cas proteins in mitochondria. When combined with a Cre-regulated mitochondrial Cas12a base editor, we will generate a conditional zebrafish disease model for multi-systemic mtDNA-encoded mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS). The establishment of this toolkit will not only shed mechanistic insights but will also facilitate the genomic nosology of a large class of mitochondrial disorders. If successful, the versatile mtRNA chimeric gRNA delivery system will enable us to model different mitochondrial pathogenic variations. This application harnesses the unique environment of mitochondria to generate a new toolbox to expand the repertoire of tools to edit the mitochondrial genome.

NSF Awards · FY 2024 · 2024-09

Life as we know it relies on a common set of foundational elements, including four standard bases in the genetic alphabet of DNA (G, A, T, and C), and a highly conserved genetic code for the translation of proteins. Synthetic biology seeks to determine the uniqueness and fungibility of these constraints, and one promise of synthetic cell engineering is to transcend the evolutionary constraints that have been handed down to us and instead create life-like systems with expanded chemistries. The Ellington and Adamala labs seek to leverage the flexibility, evolvability, adaptability, and safety of purely in vitro (test tube-based) systems, to engineer synthetic cells with expanded genetic alphabets and genetic codes. In particular, they aim to employ non-canonical nucleotides to broaden the scope of codons (triplet and ultimately new quadruplet codons) in the genetic code, ultimately leading to the incorporation of over 24 amino acids (that have distinctive and biotechnologically useful chemistries) into proteins. Additionally, the project will focus on the biosafety and biosecurity impacts of expanded genetic alphabets. The development of non-canonical genetic alphabets and codes is rapidly advancing, with various groups exploring novel genetic alphabets that are becoming more accessible both in vitro and in vivo. Successful generation of an 8-letter code, and enzymatic incorporation in vitro, demonstrated utility of this technology for engineering novel genetic systems. However, adapting non-canonical genetic alphabets to non-canonical genetic codes presents challenges, mainly due to interdependencies within biological systems. Attempts to modify genetic alphabets and codes have faced systemic disruptions and fitness impacts in natural cells. In response, the focus is shifting towards synthetic cells, which offer greater control over systems biology, free from the evolutionary constraints of natural cells. This proposal aims to utilize synthetic cells for engineering efforts, particularly exploring the implementation of quadruplet codons for genetic code expansion, a task challenging to address in living cells. The synthetic cell approach allows for rationally designed, bottom-up experimentation and the concomitant resolution of complexities related to codon instantiation, contributing insights to both living and synthetic systems. In this work, the researchers will investigate the incorporation of non-canonical nucleotides into translation (Aim 1), followed by the incorporation of non-canonical amino acids via non-canonical genetic alphabets (Aim 2). Finally, they will use artificial evolution to optimize translation systems with non-canonical nucleotides and amino acids (Aim 3). Collectively, this project will explore biological diversity beyond that which currently exists in nature and is supported by the Systems and Synthetic Biology Cluster of the Division of Molecular and Cellular Biosciences. 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 2026 · 2024-09

PROJECT SUMMARY The pancreas is smaller in individuals with diabetes and individuals at increased risk for developing diabetes, suggesting that small pancreas size may convey risk for developing the disease. However, it is not known whether individuals at risk for developing diabetes are born with a smaller pancreas or whether their pancreas shrinks as part of the pathogenesis of the disease. Establishing the link between pancreas size and development of diabetes is difficult, as the time course of diabetes progression is not well established and time to progression can be long. However, pregnancy is a period of physiological beta cell proliferation that presents a diabetogenic state with known and rapid onset. MRI can safely and noninvasively assay multiple aspects of the maternal and fetal pancreas, including pancreas size as well as other markers of pancreas structure and composition. Image acquisition and analysis will leverage our expertise assessing human pancreas size, shape, fat content, and inflammation using multimodal quantitative MRI. We propose to perform longitudinal MRI of the maternal pancreas over the course of pregnancy and postpartum and correlate imaging metrics with diabetes development and metabolic phenotyping. We will also assess the capability of MRI to measure pancreas growth in the fetus. Study participants will include mothers who not develop diabetes, mothers with pregestational type 2 diabetes, and mothers who develop gestational diabetes during pregnancy. These studies will establish the first model of maternal pancreas growth and multimodal imaging signature and their interactions with diabetes. Our central hypothesis is that maternal pancreas growth will be altered by diabetes and can be used to predict diabetes incidence in the mother. While the focus of this study is on the pancreas, the images generated will encompass the maternal abdomen and entire fetus. Thus, the data generated will be valuable datasets for secondary analysis of the interaction of diabetes with fetal development and maternal liver, fat, and placenta dynamics over the course of pregnancy.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY / ABSTRACT In the modern era of genomics and proteomics, the vast amounts of biological data generated present both a challenge and an opportunity. Central to this proposal is the innovative use of kmers, short nucleic or peptide sequences, as a tool to navigate and interpret this data. Kmers are used in a variety of genomics and proteomics applications, including genome assembly and alignment, genomic variant detection and metagenomics. With the continued advancement of sequencing technology, kmers are poised to play an important role in research. Quasi-primes are kmers found in only one single species. We have recently developed algorithms to efficiently identify quasi-primes across every available genome and proteome. In humans, quasi-prime loci are primarily found in brain-expressed genes associated with cognition and are enriched for quantitative trait loci, indicating their significance in the development of species-specific traits. Over the next five years, we will examine quasi- primes in populations of diverse ancestries, in archaic hominins and in primate and mammalian evolution to improve our understanding of their functional and evolutionary significance. Additionally, we will leverage our expertise in performing large scale analyses to expand upon our findings and characterize the functions of these kmers across every sequenced organism and taxonomic group. This will allow us to investigate the underlying mechanisms that enable species to develop new traits and adapt to their environment. The composition of organismal genomes depends on a variety of factors, including genome size, genomic instability, and biological processes, such as transcription and translation. We aim to investigate how these factors shape the composition of genomes in every species and across all taxonomic groups. We will integrate different types of genomic and proteomic data, including kmer frequency profiles, codon usage tables, and transcription and translation annotations. Our goal is to deconvolute the relative contributions of different factors shaping the composition and evolution of organismal genomes. Building on this, we plan to incorporate these findings into generative artificial intelligence models to create improved simulated genomes that will have significant applications as synthetic controls for bioinformatics analyses. Finally, we will provide well-documented, open-source software tools and integrate the data from our projects into accessible databases, aligned to the FAIR principles. In doing so, we aim to not only advance research in our specific areas of focus but also equip other researchers with tools and datasets they can utilize in their distinct domains of expertise. In summary, our multifaceted approach seeks to harness the power of kmers in genomics and proteomics, delve into the intricacies of evolutionary processes, and provide the scientific community with computational resources, fostering collaboration and innovation in basic and biomedical research areas.

NSF Awards · FY 2024 · 2024-09

Many agricultural, farming, and industrial processes release excess nitrate into the environment, making it the most pervasive groundwater pollutant in the world. This poses a serious threat to human and ecosystem health. Capturing and converting low nitrate concentrations from groundwater and surface waters is exceptionally challenging. To address this pressing need for nitrate management across food and water systems, this project will bring together experts from various complementary disciplines to develop an integrated nitrate capture and conversion device that is efficient, low-cost, and powered by renewable resources. The device will use light energy to concentrate nitrate from waste streams (photocapacitive concentration) and electrically-driven chemical reactions (electrocatalytic conversion) to produce nitrogen and valuable chemicals such as ammonia. This approach will provide insights into the chemical, physical, and catalytic processes involved in nitrate concentration and conversion, as well as the socioeconomic factors that limit the adoption of nitrogen management technologies. The project outcomes will advance the design of sustainable resource recovery systems to manage the nitrogen cycle and may reduce the cost of nitrate treatment. Further, this research will empower resource-limited communities and industrial point source treatment operators to better address their nitrate water treatment needs. Graduate and undergraduate students at the University of Michigan, the University of Iowa, and the University of Texas at Austin will receive interdisciplinary technical training. The planned outreach activities will also provide opportunities to broaden the participation of underserved groups in STEM. This project aims to develop an integrated photocapacitive concentration and electrocatalytic conversion technology for nitrate treatment. The project includes four research thrusts focused on developing and understanding this nitrate treatment technology. The first thrust advances the discovery and design of selective photocapacitive systems to capture and concentrate nitrate. In the second thrust, the team will develop and test electrocatalysts made from inexpensive and earth-abundant elements that are durable and thermodynamically and kinetically compatible for nitrate capture and conversion to ammonia or nitrogen. The third thrust involves physics-based modeling and testing of the transport processes needed to optimize the photocapacitive capture and electrocatalytic conversion system. The fourth thrust assesses process sustainability using technoeconomic and life cycle analyses to promote technology adoption by impacted communities. By integrating photocapacitive and electrocatalytic tools, this project will create a technology platform that sustainably captures and transforms nitrate, a regulated human health risk, into useful products. This convergent research advances knowledge by simultaneously considering nitrate concentration and conversion, unlike existing studies that separate these steps. The project’s outreach activities include (1) creating an exchange program for interdisciplinary summer undergraduate research experiences to prepare students from underrepresented groups for graduate research; (2) engaging water treatment professionals and communities in Iowa and Texas who are working to address nitrate pollution; and (3) integrating best practices from NSF Research Traineeship programs focused on innovations at the nexus of food-energy-water systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

In this project, funded by the Chemical Structure, Dynamics & Mechanisms B (CSDM-B), Chemistry of Life Processes (CLP) and Chemical Measurement and Imaging (CMI) Programs of the Chemistry Division, Professor Hsin-Chih Yeh of the Department of Biomedical Engineering and Professor Devleena Samanta of the Department of Chemistry at The University of Texas at Austin will develop new types of molecules for detecting and imaging chemicals under various settings. These molecules are comprised of short snippets of DNA, RNA, and/or peptides, and are termed as functional nucleic acids (FNAs). The research aims to discover new FNAs with catalytic properties that can be transformed into sensors that are sensitive, selective, and maintain their function in complex environments. The ultimate goal is to develop sensors for diverse chemical and biological applications that are as good as, or better than, conventional protein enzyme-based sensors. The PIs will leverage the institution’s outreach activities to promote STEM awareness among young talents and inspire them to pursue careers in STEM. The PIs will also make a strong effort to utilize web-based and social media outlets such as YouTube to feature their scientific discoveries and make science more appealing to K-12 students, teachers, and the general public. In this project, Professor Hsin-Chih Yeh and Professor Devleena Samanta will develop catalytic functional nucleic acids (FNAs) for chemical measurements and imaging. Specifically, the proposed research aims to use state-of-the-art selection, synthesis, and signal transduction methods to characterize, enhance, and diversify the catalytic properties of a wide variety of FNAs including aptazymes and DNA-peptide chimeras. The ultimate goal is to discover new FNAs that can be used to detect, measure, and image target analytes under various chemical and biological settings with sensitivities and specificities rivaling or even surpassing those of their protein enzyme counterparts. As such, this research will enhance our fundamental knowledge of how targets and FNAs interact, the development of nucleic acid sensors, and the application of nanomaterials in fluorescence imaging. Additionally, it will introduce innovative techniques for monitoring chemical dynamics within live human cells and tissues. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

There is an immediate need to understand the critical formulation parameters that may affect product performance in vitro and in vivo, with a goal of developing IVIVCs for long-acting injectables. In this proposal, we will develop IVIVCs for a long-acting PLGA-based solid implant using a physiologically based pharmacokinetic (PBPK) modeling approach. PBPK modeling “provides a unique opportunity to understand how the physicochemical properties of drug molecules/polymer, implant specific properties, critical formulation attributes, and physiology, among other things, influence the in vivo release mechanisms of LAI drug products and their disposition characteristics. Successful execution of the project will entail (1) developing a bio-predictive in-vitro release testing method and determining how critical formulation and physicochemical properties impact the in-vitro release of PLGA-based buprenorphine implants; and (2) using a bottom-up PBPK approach to build IVIVCs that predict in-vivo PK profiles of PLGA-based buprenorphine implants from in-vitro data.

NSF Awards · FY 2024 · 2024-09

The overall objective of the principal investigator's (PI) research is to develop accurate mathematical models and computer simulations arising from physical phenomena of fundamental scientific interest. The mathematical problems considered in this project describe non-equilibrium systems endowed with memory effects. Such systems are characterized by internal and external forces that generate breaking of symmetry and exhibit stable states that cannot be captured by simple hydrodynamics within classical fluid and gas dynamics modeling. The elements of these systems arise in many phenomena impacting daily human life: e.g., biosystems and molecular medicine at miniature scale, plasma evolution in fusion models for clean energy, and reacting solid state nano structures for solar generation of hydrogen resources, to name just a few. The broad range of problems requires new computational approaches that are being designed and analyzed within this project to ensure consistency, stability, error estimates control and rates of convergence to equilibrium. The scientific computing component is being developed using the techniques that need to be integrated into novel AI and ML strategies along with the tools from non-linear analysis. The work is interdisciplinary in nature and is being carried out in collaboration with physicists, engineers, and social scientists. The PI’s students and postdoc trainees will be involved in the research. These research goals comprise a broad program in the development of analytical and numerical tools associated with statistical transport equations and systems at the core of applied mathematics in probability, statistics applied to chemistry, physics as well as biological and social dynamics. They concern the modeling of complex interactions systems yielding kinetic frameworks associated to Markovian and non-Markovian processes of birth-death dynamics such as Chapman-Kolmogorov flows of weak turbulence problems arising as dissipative mechanisms in Vlasov-Poisson or Maxwell systems. Such statistical approaches lead to nonlinear integro-differential systems of equations of collisional classical, or quantum Boltzmann of Dirac-Fermi or Bose Einstein type, or aggregation, coalescence, breakage particle systems. The PI will focus on the interplay of this models from analytical and numerical mathematics viewpoint and the scientific computing implementations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Black holes are mysterious and fascinating objects where the gravity is so strong that not even light can escape them. Supermassive black holes are a million to a billion times the mass of our Sun and are ubiquitous at the centers of massive galaxies. Supermassive Black Holes play a critical role in shaping the large-scale structure of the universe and the galaxies around them, as they influence the formation of stars and the distribution of matter through their powerful gravitational pull and energy output. This research team will help explain long-standing questions of black hole formation. Intermediate-mass Black Holes (IMBHs; 100 to 100,000 times the mass of our Sun), are one of the best theories for what seeded the first supermassive BHs, and they are expected to still reside in the smallest-scale galaxies today. A new class of low-mass galaxies has been discovered that might be powered by IMBHs. These so-called "Extreme Emission Line Galaxies" (EELGs) emit inexplicably large amounts of very high-energy light that are well beyond what stars can produce. This research also provides an exciting and natural way to bring diverse astronomers together and prepare students for graduate study. The investigator will develop a BH Summer Bridge Course, provide Postdoc Mentoring opportunities tailored to BH research and establish long-lasting affinity support groups through Writing Retreats and a Beyond Astronomy Conference. This project will address the outstanding challenges that have so far obscured a clear view of IMBHs using a novel, multi-pronged pathway for studying IMBH signatures in EELGs. The investigator will (1) use spectral energy distribution (SED)+line fitting to diagnose the relative contribution of IMBHs to the high-energy ionizing continuum; (2) measure BH masses from photoionization models that incorporate broad (~1000 km/s) Balmer emission lines; and (3) inform the growth history of SMBHs. This primary objectives are (i) learning more about the conditions in which IMBHs thrive and in which they fail to exist and (ii) constraining the source of hard ionizing radiation in EELGs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Autonomous robots hold the potential to revolutionize society in areas such as healthcare, transportation, and manufacturing. These systems frequently employ learning-enabled components in their perception, planning, and control modules, necessitating complex design choices to ensure safe operation. However, design decisions that initially appear sound may lead to unexpected problems during testing or, even worse, post-deployment. For example, an autonomous vehicle once exhibited erratic swerving to localize itself for lane-keeping, a failure mode unforeseen by the system's designers and developers. Such surprises indicate that the agent's norms—what it considers permissible and obligatory—are inappropriate in certain situations. As learning-enabled systems become more complex, operate in open environments, and interact with humans and other robots, these challenges are likely to be exacerbated. This project focuses on safety failures of reinforcement learning (RL) agents, stemming from two primary sources: the misalignment between design intent and the agent’s perceived norms, and the gap between the agent’s required knowledge for safe operation and its actual perception capabilities. The goal is to equip researchers and practitioners with tools to design provably safe autonomous systems, encompassing all major stages of design, verification, and deployment. The project develops a process to iteratively align an RL agent's norms with those of its designers and formally verify the resulting behavior. Key activities include: (1) developing inverse reinforcement learning algorithms to learn a reward function from demonstrations, constrained by deontic logic; (2) systematically exploring the trained agent’s norms to uncover unknowns by generating norms that would surprise the engineer; (3) querying the agent to explain its reward function when it produces undesired behavior; (4) defining a new class of obligations related to knowledge and corresponding formal specification logic; (5) designing run-time monitors to predict action and knowledge safety violations during operation; (6) implementing online metareasoning, coupled with introspective perception modules, to restore safe behavior; (7) iteratively improving system alignment by updating the agent's learning process using verification and run-time monitoring results. The project's outcomes are validated using an industrial simulator of a real-world bipedal robot, scaled-down autonomous race cars, and a campus-wide fleet of delivery robots. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The networked services that our modern society depends on require many types of resources, including computing power, memory and energy from the end user’s device, network resources from the cellular network provider and massive storage and computation facilities in warehouse-sized data centers. These systems rely on many complex algorithms that decide when to allocate which resources to which users and applications. A key unmet challenge in designing these algorithms is evaluation. How can an engineer be certain that their design will make good decisions in the increasingly complicated infrastructure that operates modern networked applications? Testing helps but does not account for phenomena that may be encountered during deployment but were not a part of the tests. This project develops performance verification, a suite of automated reasoning tools that can search vast spaces of possible system behaviors to find any that result in degraded performance. By providing more principled and more automated methods of evaluation, the project enables system engineers to design more reliable and available systems. This, in turn, provides benefits for a wide range of critical services that depend on the reliable performance of networked infrastructure, including utilities, aviation, defense and first responders. The techniques developed in this project are incorporated into university courses and publicly disseminated educational materials. Performance verification entails assumption-constrained worst-case analysis, which makes it easier to model the complex behavior of real-world systems and does not require the precise characterization of the system and workload required by traditional methods (e.g., simulation). Today, performance verification is a nascent field. To create impact at scale, it needs formalization, better automated reasoning methods, and more systematic ways to connect formal models to the underlying implementations. This project develops a formal modeling and specification framework to model networked systems and specify performance properties. Based on a hybrid of network calculus and discrete control, it provides a practical trade-off between expressiveness and amenability to formal reasoning. In contrast to existing approaches, the framework allows the modeling of closed-loop control while achieving a high degree of automation. The project explores techniques to automatically generate proofs and counterexamples, applying methods such as syntax-guided synthesis, counterexample-guided inductive synthesis, and Satisfiability Modulo Theories (SMT) solving. Finally, to bridge the gap between the formally verified model and the actual system implementation, the framework provides support for validating these formal models against actual implementations or real-world network data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The proposed research aims to revolutionize the monitoring of blood oxygenation by developing a noninvasive, wrist-worn soft sensor patch that can capture the arterial and venous oxygenation separately and simultaneously. Traditional pulse oximeters only measure arterial oxygenation and are limited in their ability to detect metabolic abnormalities such as sepsis, which requires venous oxygenation measurements. By leveraging advanced noninvasive optical sensing technology and novel algorithms, this project seeks to create a device that overcomes these limitations. The ultrathin, stretchable, and self-adherable nature of the patch allows an array of sensors to be comfortably worn on human wrist, ensuring accurate long-term monitoring. Advanced algorithms will be created to separate arterial and venous pulses. These innovations could transform patient care by providing accessible and continuous monitoring of venous oxygenation and reducing the need for invasive procedures. Additionally, it promises to make strides in equitable healthcare, as it aims to provide precise oxygenation measurements across diverse patient populations, irrespective of skin pigmentation. This project aims to develop a novel wireless, noninvasive optical e-tattoo, which refers to lightweight, ultrathin, and skin-conformable sensor patch, capable of continuous and simultaneous monitoring of arterial and venous oxygenation. The research addresses the current unmet need for accurate noninvasive venous oxygenation measurement by isolating venous blood signals using a reflective, depth-sensitive photoplethysmography (PPG) array on a wrist-conformable e-tattoo. The proposed system integrates four key innovations: (1) development of an ultrathin, stretchable e-tattoo with an array of PPG sensors placed over major artery-vein pairs such as the radial artery and vein at the wrist; (2) design of algorithms for separating arterial and venous pulses and converting raw absorbance data into oxygenation levels; (3) validation using a dynamic optical phantom with known ground truth to ensure the accuracy and reliability of the measurements; and (4) pilot clinical validation against invasive catheter measurements. This research is expected to significantly enhance the accuracy of noninvasive venous oxygenation readings, facilitating better diagnosis and treatment of cardiometabolic abnormalities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

One of the most prominent and exciting challenges in fundamental physics is understanding the nature of dark matter, which accounts for 85% of all matter throughout the known Universe. Dark matter plays a crucial role in forming galaxies, galaxy clusters, and larger structures of matter through its gravitational effects on ordinary particles. A key question is whether or not dark matter experiences any forces beyond gravity. The PI's research program will tackle the dark matter problem at the intersection of cosmology and particle physics, using cosmological and astrophysical observations to study the microscopic properties of dark matter. Such observations have emerged as a powerful probe of dark matter, due to their increase in precision and sensitivity over recent years. The PI's research will provide foundational work for future dark matter analyses, in preparation for potential new discoveries as next-generation instruments and surveys begin operations in the upcoming decade. In conjunction with research, this program includes the mentoring and support of undergraduate women in physics. This research will study the impact of dark matter interactions with ordinary particles and dark matter self-interactions throughout cosmic time. These interactions can affect the formation and evolution of dark matter structures, thereby enabling the opportunity to search for new dark matter physics using current and future cosmological and astrophysical observations. This research will pursue new avenues to bridge theoretical models and observational techniques to study dark matter. It will involve the development of sophisticated tools to perform joint analyses using multiple datasets. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Metabolic pathways function in response to cellular needs, but they also can direct signaling and even influence how our genes are read. If we can understand the underlying mechanisms of metabolic-dependent signaling, it could broaden the range of interventions when combatting diseases to include modulation of cell metabolism. It is currently challenging to accurately measure concentrations and fluctuations of the metabolic intermediates that participate in these signaling pathways. By understanding the intracellular concentrations and regulation of these signaling metabolites, we can determine how the availability of a specific metabolite is poised to impact enzymatic activity, as well as the extent and timing that their levels may fluctuate over the course of physiology and disease. These metabolites are highly compartmentalized even within individual cells, and an accurate measurement for signaling needs to distinguish its free concentration from bound pools. Moreover, signaling metabolites often have distinct roles in different parts of the cells and their concentrations can be differentially regulated. We are deconvoluting the signaling roles of metabolites by developing small single-fluorescent protein biosensors that are genetically encoded and selective for specific intracellular metabolites. These sensors can be localized subcellularly and measured changes in their fluorescence reflect changes in concentration for specific intermediary metabolites. Together the data will determine how that metabolite regulates signaling. In this proposal, we use mitochondrial NAD+ sensors to elucidate the mechanisms of transport for human mitochondrial carrier family member, SLC25A51, and determine its roles in disease. We also develop new sensors to expand our investigations to additional signaling metabolites, including NAD-derived metabolites.

NIH Research Projects · FY 2025 · 2024-09

Project Summary People with aphasia struggle to translate their thoughts into language. One potential way to help people with aphasia is using brain-computer interfaces (BCIs) that decode intended speech from brain recordings. Recent studies have shown that continuous language can be decoded from semantic representations that encode the meaning of intended speech. However, semantic decoding has only been demonstrated in neurologically healthy participants, and current approaches do not accommodate the language comprehension impairments that often accompany language production impairments in aphasia. The long-term goal of this proposal is to develop BCIs that can improve communication in people with aphasia. This study has three goals: 1) to adapt existing semantic decoding approaches for people with aphasia, 2) to develop semantic decoding approaches that do not require any language training data from the person being decoded, and 3) to involve people with aphasia in the design of BCIs. To accomplish these goals, ten participants with aphasia and ten neurologically healthy participants will be recruited. Semantic decoders will be trained on functional MRI (fMRI) responses while participants listen to stories and watch movies. Semantic decoders will be tested on fMRI responses to perceived speech, perceived movies, and imagined speech. Collaborative design workshops will be held to assess when and how participants with aphasia envision using BCIs. These findings will evaluate the potential for using semantic decoding to improve communication in people with aphasia. This fellowship will provide the applicant with a unique interdisciplinary training experience, which will include the development of the necessary skills for a) administering language assessments, b) conducting neuroimaging experiments in participants with aphasia, and c) collecting and analyzing qualitative feedback from participants with aphasia. The applicant's sponsors and collaborators will provide mentorship in the areas of participant recruitment, language assessment, experimental design, functional neuroimaging, and thematic analysis. Together, these experiences will prepare the applicant for a successful independent research career.

NIH Research Projects · FY 2024 · 2024-09

ABSTRACT Synapses form trillions of connections between billions of neurons in the brain to establish neural circuits that allow us to sense, think, act, learn, and remember. Our goal is to understand how synapse structure supports learning and memory with a focus on dendritic spines, the tiny protrusions that host most of the excitatory synapses in the brain. While most neuroscientists would agree that synapse growth and retraction are vital for learning and memory, we do not know how these long-term changes in synaptic structure are regulated in the face of ongoing brain plasticity. The synaptic active zone comprises discrete domains where presynaptic vesicles are docked and released. Postsynaptic responses are restricted to regions within ~100 nm of the vesicle release sites. Our three-dimensional reconstruction from serial section electron microscopy (3DEM) reveals three zones across the synapse: (i) strong active zones (AZs) that have tightly docked presynaptic vesicles, (ii) weak AZs that have loose or nondocked presynaptic vesicles, and (iii) nascent zones (NZs) that have a thick postsynaptic density but no presynaptic vesicles. At the onset of long-term potentiation (LTP), presynaptic vesicles are rapidly recruited to the NZs, converting them to AZs. Protein filaments shorten and draw docked presynaptic vesicles closer to the enlarged AZs, and recruit vesicles to dock at weak AZs. This evidence of presynaptic plasticity would increase the area of release and probability of postsynaptic receptor response. The recovery interval following saturation of LTP is 1-4 hours depending on the preparation. During this interval, new NZs form, primarily on spines containing smooth endoplasmic reticulum, a local resource for regulating calcium and trafficking of lipids, proteins, and organelles. Clusters of spines form in the vicinity of these enlarged spines. We hypothesize that synapse-specific expansion of NZs during LTP provides a basis for learning and the advantage of spaced over massed learning to establish long-lasting memories. Furthermore, we hypothesize that LTD is driven by the conversion of weak AZs to NZs and ultimately elimination of spines without AZs. To address these hypotheses, we propose multidisciplinary approaches to investigate NZ and AZ plasticity—including slice physiology, optogenetics, glutamate uncaging, and tomographic 3DEM of synapses along activated axons labeled with APEX. Our Specific Aims are: Aim 1) Determine the specificity of NZ to AZ conversion during synapse enlargement, resource utilization, and spine clustering underlying the saturation, recovery, and enhancement of LTP. Aim 2) Test whether saturating LTP at an isolated dendritic spine is sufficient to fill NZs and determine the role of PSD-MAGUK proteins and their interaction partners in the recovery of LTP from saturation. Aim 3) Test whether saturation of long-term depression (LTD) is accompanied by loss of weak AZs and determine the time-course over which LTD recovers from saturation. Outcomes promise new insights about synaptic mechanisms of learning and memory and new targets for understanding and treating learning disabilities.

NIH Research Projects · FY 2024 · 2024-09

Project Abstract Black children are at risk for misdiagnosis across categories of disability due to inappropriate assessment materials and clinicians’ lack of knowledge regarding Black families’ cultural-linguistic practices. Criteria for identifying children with late language emergence (LLE), otherwise known as late talkers, are based on the communication norms of middle-upper class, monolingual, white families. Because communication practices vary across cultures, typical language acquisition may also vary accordingly. Current assessment protocols, including standardized tests, criterion-referenced measures, and questionnaires, are structured around one specific culture’s expectations for communication. Without criteria that are consistent with the cultural-linguistic practices of Black families, Black children are at risk for being mislabeled as a late talker due to a mismatch between current criteria and their community language practices. The proposed study centers the expertise of Black caregivers regarding their children identified as late talkers through the use of video-cued ethnography to gather qualitative data. In addition, quantitative data will be gathered through direct assessment of the children, which will be compared to the qualitative data. This mixed methods study will (1) characterize Black caregivers’ conceptualization of effective communication and late talking; (2) describe Black caregivers’ experiences and evaluation of the language assessment process; and (3) investigate the relation between Black caregivers’ conceptualization of effective communication and current assessment protocols for identifying late talkers. Black caregivers across socioeconomic status who have children identified as late talkers will respond to a recording of a traditional language assessment of an unknown child and to questions regarding their conceptualization of language and communication in individual interviews; participate collectively in a focus group to discuss their experiences with language assessments; and provide real-time commentary as they observe a language assessment of their own child. Video-cued analysis will be used to identify themes in individual interview and focus group transcripts. Descriptive analysis will be used to compare caregivers’ qualitative responses to numerical data derived from the language assessment of the children. Emerging themes across families will inform our understanding of the cultural practices that transcend SES and bear on Black children’s communication and the evaluation thereof. Caregiver feedback on assessment protocols will improve clinicians’ use of current assessment protocols with Black families and clinicians’ accuracy of their description of Black children’s communication abilities.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Essential tremor (ET) is the most prevalent movement disorder affecting approximately 25 million people worldwide. Up to sixty-two percent of patients with ET develop vocal tremor (VT) involving oscillation within the respiratory, laryngeal, pharyngeal-oral, or velopharyngeal-nasal subsystems. These oscillations produce modulation of the fundamental frequency (pitch) and intensity (loudness) of the voice, resulting in a ‘shaky’ voice and increased effort during speech production. Neurological voice disorders like VT are detrimental to communication, professional productivity, and quality of life. Unfortunately, the current approaches for medical management of VT have inconsistent effects on voice production and can have adverse effects. Furthermore, current methods for behavioral management of VT do not target specific impairments and employ a wide range of therapeutic techniques leading to inefficient therapy. The challenges in managing VT stem from a lack of accessible assessment methods to identify the subsystems affected by tremor and guide targeted treatment. Clinical assessment of VT requires the use of nasolaryngoscopy to identify oscillation within the larynx and vocal tract (i.e., pharyngeal-oral and velopharyngeal-nasal subsystems). However, access to nasolaryngoscopy outside of large voice centers is limited by cost, time, and training demands. Furthermore, nasolaryngoscopy cannot be used to identify oscillation within the respiratory subsystem, necessitating the addition of visual and tactile or respiratory kinematic assessments. Although standard acoustical assessments can be employed in a variety of settings at low cost, current acoustical analyses of microphone signals cannot differentiate the subsystems affected by tremor because a combination of laryngeal source and vocal tract filter features are represented. Our recent study on vocal vibrato (i.e., a singing technique that involves modulation of voice similar to VT) used a vibration sensor applied to the neck surface in singers to capture features of the source prior to vocal tract filtering. Analyses of simultaneously recorded vibration sensor and microphone signals revealed distinct patterns in the extent of intensity modulation thought to be related to differences in singers’ involvement of the respiratory system, larynx, and vocal tract in vibrato. In addition, preliminary studies in computational models and speakers with VT showed distinct patterns of modulation based on the physiological source of tremor. Thus, the proposed study aims to determine if simultaneously recorded neck-skin vibration sensor and head- mounted microphone signals differentiate the subsystems involved in voice modulation in singers producing vibrato as a model of VT and in speakers with VT. Furthermore, the proposed study aims to determine if vibration sensor and microphone signals capture the effects of laryngeal botulinum toxin injections in speakers with VT. The findings of this study may address the critical need to advance assessment of VT, facilitate individualized and targeted treatments for speakers with VT, and measure patients’ responses to treatment.

NSF Awards · FY 2024 · 2024-09

Shape synthesis—creating formal descriptions of novel 3-D shapes—is a foundational area of computer graphics. With the advent of deep learning and generative AI models, computer graphics innovators have adopted machine learning (ML) technology in service of shape synthesis. Current approaches focus on adapting image- and video-generation techniques developed by the computer vision and ML communities. These approaches often produce visually appealing results, but suffer fundamental limitations; they often fail to capture geometry and topology properly leading to unnaturally distorted shapes, or shapes that incorrectly incorporate holes or disconnected pieces. Similarly, existing methods offer no guarantees that shapes synthesized will be physically suited for manufacturing. These limitations place fundamental barriers to applying machine learning methods for shape synthesis in applications such as augmented and virtual reality, embodied AI (such as robotics), and manufacturing (including 3-D printing). This project addresses the deficits in current methods by developing a computational framework that incorporates physical, topological, and geometrical preferences when learning shape synthesis. The overarching goal of this project is to establish shape synthesis as a scientific sub-community that departs from simple applications of 2-D image-generation techniques. Integrated education and outreach activities amplify the broader impacts of this project. The key idea of our framework is to model various geometric, physical, and topological priors as regularization losses in learning shape generators to enhance their generalizability. We focus on the latent diffusion paradigm that has led to state-of-the-art shape generators. The proposed research consists of two thrusts. The first thrust studies principled approaches that enforce geometric, physical, and topological priors to improve the diffusion procedure. The second thrust focuses on improving the shape decoder by modeling regularization losses that enforce these priors. We seek to revolutionize 3D shape generation from the current focus of visual appearance to synthetic shapes that are geometrically feasible, physically stable, and topologically correct. Toward this goal, the project will develop differentiable tools in structural shape analysis, computational topology, and shape analysis that can be easily integrated into learning shape synthesis 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.