University Of Wisconsin-Madison

NSF Awards · FY 2024 · 2024-08

Any software developer should have a realistic understanding of all potential security threats the software may face, a concept known as threat perception (TP). Unlike secure coding skills or a security mindset, TP provides the foundational knowledge necessary to teach and learn about these related concepts effectively. Current studies indicate that undergraduate computer science (CS) students in the U.S. often graduate without formal software security education and struggle to develop a security mindset while developing and managing software applications. Even when basic security attacks are taught, students tend to view them as a checklist rather than understanding their real-world implications. Research in the learning sciences shows that merely working through a checklist does not lead to a deeper understanding of how different threats emerge in the real world. This project addresses this gap by formalizing TP based on learning theory, developing methods to measure and assess TP, and designing educational interventions to improve TP learning among undergraduate CS students. By advancing TP education, this project supports the national interest by promoting the progress of science and securing national defense through better-prepared software developers. The project's primary goal is to formalize the concept of threat perception (TP) in software development, measure and assess students' TP, and design interventions to improve TP among undergraduate computer science (CS) students. The scope includes developing a mechanistic model of TP based on constructivist learning theory, analyzing how different code analysis tasks, such as "Build It, Break It, Fix It" (BiBiFi), affect students' TP, and creating a curricular unit that teaches TP through BiBiFi-style projects. The methods involve systematic analysis of students' learning processes and designing educational tools that can be integrated seamlessly into existing courses. This project aims to produce a comprehensive understanding of how students learn and apply TP in software development, providing valuable insights for educators and contributing to the development of more secure software systems. By demystifying the process of identifying and addressing security threats, this project will broaden participation in computer security education to all undergraduate CS students rather than a self-selected group of security-focused students. This effort will help foster a more inclusive and comprehensive understanding of computer security among future software developers, including those from underrepresented backgrounds. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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 · 2024-08

The use of enzymes in synthesis has had an enormous impact on the development of bioactive compounds, as they can perform transformations with unparalleled selectivity at a low cost without using toxic solvents. However, there is a dearth of enzymes that catalyze C-C bond formation on preparative scales. Many common enzymes from central metabolism have exquisite substrate selectivity or rely on coupling to downstream reactions as a thermodynamic driving force, limiting their utility. We have identified a suit of pyridoxal-phosphate (PLP) dependent enzymes that catalyze stand-alone C-C bond forming reactions that are mechanistically distinct from their counterparts in central metabolism. We propose mechanistic analysis of these enzymes, encompassing structural, kinetic, and spectroscopic studies, that will reveal how these enzymes form high-energy intermediates that are shielded from destructive interactions with solvent. This information will enable hypothesis-driven strategies to alter and improve enzyme function. In an allied effort, we are exploring new strategies to increase the efficiency of screening-based directed evolution. Assaying mixtures of substrates in direct competition, followed by resolution and quantitation of the products contain a wealth of un-tapped information. We will explore how to maximize the information present in substrate mixtures and how to use multiplexed data to guide evolutionary steps that are driven by either changes in total activity or by changes in specificity. These advances in engineering will synergize with our practical efforts to evolve C-C bond forming enzymes to perform new catalytic reactions. This research will have immediate impacts because the enzymes will produce non-canonical amino acids (ncAAs). Nature often uses ncAAs bearing side chain stereocenters to tune bioactivity, but the structural complexity of these molecules makes many out of reach for standard organic chemistry. The ncAAs made here will add new and valuable diversity to the medicinal chemistry repertoire.

NSF Awards · FY 2024 · 2024-08

The University of Connecticut, the University of Wisconsin-Madison, and the University of Illinois at Urbana-Champaign are conducting a research study of the barriers and solutions that physics graduate students and faculty experience in non-traditional post-secondary training and workplace settings. The lack of full inclusion of people with disabilities in the STEM workforce is a missed opportunity to realize the full potential and talent of the entire U.S. population. Opportunities to advance knowledge about physics postsecondary training setting and workplace barriers and solutions for faculty and graduate students with disabilities will lead to increasing the engagement, academic career retention, and career advancement of faculty and students with disabilities in STEM. Such success is essential for building and advancing a robust U.S. STEM workforce. The research team is engaging with an expert advisory board, an objective evaluator, a postdoctoral research scholar, and graduate students to contribute to the project work. The research includes the collection, analyses, and interpretation of qualitative and quantitative data that are informed by robust theoretical frameworks and conceptual models. Findings will be share with the general public as well as researchers, educators, and administrators. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). The project is funded by the Directorate for Social, Behavioral and Economic Sciences’ Office of Multidisciplinary Activities, the Division of Equity for Excellence in STEM’s Education Core Research (ECR), the Division of Equity for Excellence in STEM’s Alliances for Graduate Education and the Professoriate (AGEP), the Division of Equity for Excellence in STEM’s Louis Stokes Alliances for Minority Participation program (LSAMP), the Division of Undergraduate Education’s Improving Undergraduate STEM Education (IUSE), and the Division of Equity for Excellence in STEM’s Eddie Bernice Johnson Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (INCLUDES). 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 · 2024-08

PROJECT SUMMARY/ABSTRACT The older incarcerated population in the United States (US) has increased by more than 4,000% since 1981. By 2030, there will be more than 400,000 adults ≥50 years in US prisons. These individuals experience frequent hospitalizations, putting them at risk for delirium, a condition frequently seen in older adults and associated with high morbidity and mortality. Incarcerated individuals face higher rates of both known delirium risk factors and incarceration-specific risk factors. Similarly, incarcerated patients may not receive standard evidence-based delirium prevention measures during hospitalizations due to incarceration-specific barriers (e.g. routine shackling). Despite the increased risk of delirium in hospitalized older incarcerated adults, there are virtually no data on delirium prevalence or incidence in this population or on the frequency with which they receive evidence-based delirium screening and prevention. The goal of the proposed research is to establish foundational data on delirium epidemiology (including prevalence, incidence, and risk factors), on the frequency of delivery of evidence-based delirium prevention measures (e.g. cognitive stimulation, ambulation), and on potential barriers and facilitators to delivery of those measures in this high-risk population. These goals will be achieved through qualitative interviews with all relevant stakeholders, including correctional officers, a group that has not previously been included in discussions around this issue, through a prospective observational trial, and through a pilot study that establishes a novel multidisciplinary team to address delirium prevention among older incarcerated patients. My long-term career goal is to improve the hospital care of the rapidly aging population of older incarcerated adults, with a specific focus on delirium screening and prevention. Through this award, I aim to strengthen my geriatric and delirium-specific knowledge, solidify my research design skills, particularly in acute care environments and with marginalized populations, and begin training in behavioral intervention science. I will access a wealth of resources through the University of Wisconsin School of Medicine and Public Health, including the opportunity to receive mentorship from world leaders in geriatrics and health disparities and from researchers with established track records working with incarcerated populations and correctional systems. Building on the training, mentorship, and data that I will obtain through this career development award, I will submit an R01 to undertake a multi-site study that will further increase knowledge surrounding delirium, delirium screening, and delirium prevention in older incarcerated patients. My definitive goal is to design, test, and implement an incarceration-specific approach to evidence-based delirium measures that will reduce the risk of delirium in the marginalized and high-risk population of older incarcerated adults.

NSF Awards · FY 2024 · 2024-08

The Gaussian process is a mathematical tool that can use incomplete data to fill in gaps, for example to interpolate the temperature at a person’s house given a network of nearby weather stations. Gaussian processes are used in many application areas, such as geospatial analysis, machine learning, and the analysis of computer experiments. Gaussian processes are flexible, interpretable, and provide natural quantification of uncertainty. However, direct application of Gaussian processes is too computationally expensive for large datasets. This project addresses the computational challenges with novel algorithms and bridges the gap between statistical and machine learning approaches. As big data now appear in almost every field of science and society, providing powerful, scalable, and free software to analyze such datasets can have a transformative effect. This work will replace current practices and approximations for massive spatial data that are often simplistic due to computational limitations. This project can lead to improved accuracy and uncertainty quantification in countless applications with direct impact on society, including carbon monitoring, renewable energy, rainfall prediction, calibration of robotic arms, and modeling and prediction of insurgent activities. The developed methods and software will thus be an important tool for computational and data-enabled science and engineering. The investigators will mentor and train student researchers, and share the project findings via journal publications and conference presentations. The goal of this project is to develop a nearly universal toolbox for scalable Gaussian process (GP) modeling. The toolbox is based on the ordered conditional approximation (OCA), a simple but very powerful idea that exploits the screening effect (i.e., conditional independence) exhibited by many popular covariance functions. The OCA framework unifies many state-of-the-art GP approximations from statistics, machine learning, and numerical linear algebra. This project will result in new, highly accurate OCA methods with guaranteed scalability and broad applicability for modeling and analysis of nonstationary, multivariate, multi-scale, and other processes. Also, extensions will be developed that allow these new spatial-statistics methods to be used in a variety of machine-learning applications, where OCA-type approaches have not received much attention so far. For the new methods, the computational cost is guaranteed to be linear in the data size, with further speed-ups possible through parallelization. All approaches will be implemented in easy-to-use open-source software. This will allow users to bring the power of GPs to bear on modern datasets, enabling spatial prediction, calibration, parameter learning, and nonparametric regression with big 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 · 2024-08

Glaciers move in response to gravity pulling them downhill and much of the resistance to this motion is supplied by the bedrock that they sit on. For fast moving glaciers this motion is largely the result of basal ice sliding over and around bedrock bumps, and the specific processes at the ice-bed interface that facilitate this sliding play a dominant role in setting the glacier speed. Sliding atop the ice-bed interface is known to create cavities (pockets of water) downstream of bedrock bumps. These cavities facilitate water flow, control areas of ice-bed contact, regulate basal drag, dictate subglacial erosion, and affect ice mechanics in general. Thus, the length and shape of cavities (geometry) as they separate from the bed is of fundamental importance in glaciology. This project will determine the fundamental processes that set the shapes of those cavities. This work will benefit the scientific community by producing improved estimates to basal sliding and subglacial hydrology which are two of the main uncertainties in glacier-flow modeling. It will also lead to a better understanding of subglacial erosion which effectively controls the basal bump geometries. This in turn will lead to improved understanding of the fundamentals of glacier and ice-sheet dynamics. Therefore, the outcome of the project could ultimately improve future projections of sea-level rise, benefitting society at large. In addition, this project will train a postdoctoral researcher and undergraduate students from tribal institutions. This project will: 1) Use a novel experimental device to generate a cavity geometry data set for a range of independent controls; and 2) Use the results from part one to constrain numerical models that will allow for the exploration of a greater range of parameter space than is possible in the physical experiments alone. Using a novel cryogenic ring-shear device, this project will systematically assess three likely controls on cavity geometry: effective stress, sliding speed, and bump geometry, while simultaneously tracking strain indicators within the ice and the geometry of the cavity through the transparent walls of the device. These experiments will be conducted with the University of Wisconsin-Madison, state-of-the-art ring-shear device and represent the first instance where all three parameters’ effects on the resultant cavity geometry can be measured simultaneously. The lab experiment findings of cavity geometry and strain rates within the ice will be used to help constrain the process-based numerical modeling of cavity formation. The numerical simulations of ice flow around obstacles will provide information about the stress and strain distribution within the ice, and from this data we can explore the ability of existing theories to predict cavity geometry for fast-flowing ice. The physics within the numerical model will be updated as needed to incorporate processes such as a stress dependent ice rheology or changes in the ice-bed contact physics that are currently unaccounted for. Outcomes will be 1) a detailed understanding of the physics that govern cavity geometry and 2) a simple parameterization of the lab and modeling results that can be easily incorporated into glaciological models for improved estimates of subglacial sliding, hydrology, and erosion. 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 · 2024-08

Self-assembly and structural ordering of particles during the slurry drying process are ubiquitous, intricate, and functionally critical. This process significantly influences important applications such as genotyping, biosensing, 3D printing, and the production of thin films for various purposes. Monitoring, understanding, and predicting the multiscale structural dynamics under different drying conditions poses a major challenge in studying particulate and multiphase processes, which involve fundamental phenomena like wetting, evaporation, surface tension, and multiphase flow. This project aims to develop a comprehensive fundamental understanding of the dynamic structural evolution in slurries and to create a predictive machine learning model for guiding the optimization of the drying process. This knowledge and methodology will offer new insights into the dynamics of particle self-assembly, aiding in the design of drying processes to control the microstructure of particulate systems to achieve desired mechanical and electrical properties. The collaboration between University of Texas at Austin and University of Wisconsin-Madison presents unique opportunities for recruiting under-represented students and for engaging with the science-technology-entrepreneurship training programs. This award aims to develop a comprehensive fundamental understanding of the dynamic drying process of a particle-laden slurry. The mechanistic insights will be integrated into a predictive machine learning model to guide the optimization of the drying process for various composite systems. The following research tasks will be conducted. (i) Establishing the correlation between the drying condition and multi-scale structure ordering. (ii) Imaging and predicting the spatiotemporal evolution of the microstructure. (iii) Model-guided optimization of the mechanical and electrical properties of particulate composites. Specifically, 3D in-situ imaging will be applied to model slurry systems consists of thousands of oxide particles with controlled morphology suspended in a liquid solvent with controlled viscosity. A computing module will be developed to identify, recognize, and track all of them in the 4D imaging data (space and time), which will then serve as inputs for the graph-based machine learning effort. Overall, the project will reveal how the system’s non-equilibrium behaviors would affect its final structural ordering and, thus, its mechanical and electrical properties. 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 · 2024-08

Plasma, one of four fundamental states of matter, can be found everywhere in the cosmos, from the laboratory to the solar atmosphere, to supernova remnants and distant galaxies. This award supports a computational study of how energy in plasmas is distributed and dissipated in systems where collisions between charged plasma particles are infrequent. The project will develop reduced models that leverage novel machine learning techniques to make simulations more efficient, and will integrate them into an open-source simulation code applicable to problems in astrophysics, space weather modeling, and fusion energy development. This multidisciplinary project straddling the fields of plasma physics, computational physics, and machine learning will offer a valuable experience to undergraduate and graduate students. Project's outreach activities will involve presentations at universities and local high schools in coordination with the Wonders of Physics program at the University of Wisconsin-Madison. The primary methodology used by this project will be based on the recently developed moment-hierarchy model for magnetized plasmas at arbitrary collisionality. The model will be integrated into an open-source, machine-learning-ready simulation code that incorporates both traditional neural networks and more advanced physics-informed neural networks. The methodology will enable a transition from simplified models with ad-hoc closures and reduced collision operators to a first-principles framework. This approach will alleviate the computationally intensive nature of micro-scale kinetic simulations and enhance the ability to simulate and understand weakly collisional astrophysical and laboratory plasmas. The work will be performed in collaboration with École Polytechnique Fédérale de Lausanne in Switzerland, the University of California, Los Angeles, the Massachusetts Institute of Technology, and Princeton University. The numerical results will be validated using experimental facilities at the Wisconsin Plasma Physics Laboratory (WiPPL). 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 · 2024-08

Abstract Brain injuries affect millions of infants each year and may cause irreversible cell death. Visualizing cell death in their brains is challenging. There is a need for non-invasive and easy-to-implement bedside imaging methods which can be performed safely, and repeatedly and have the ability to detect cell death in the brain. Cell death in infant brains can occur after hypoxia/ischemia, stroke, trauma, or exposure to sedative/anesthetic or antiseizure medications and presents in different forms (necrosis, apoptosis, autophagy). Structural features of cell death modify the acoustic scattering properties of tissue, therefore reflecting ultrasound differently from viable cells. Quantitative ultrasound (QUS) has been used to detect the unique scattering properties of apoptotic cells in cancer and necrotic cells in cultures. Low cost, portability, lack of need for contrast agents, and rapid image acquisition and processing make QUS appealing for the in vivo detection of cell death in infants. Our group has applied these techniques to study cell death in the brains of newborn non-human primates (NHPs) exposed to sevoflurane anesthesia. Within the thalamus, a region that undergoes apoptosis after prolonged sevoflurane administration in infancy, we detected changes in the “effective scatterer size” (ESS) and confirmed histologically that apoptosis was present in this brain region. Notably, a strong correlation between changes in ESS and the severity of histologically detected apoptosis was confirmed. Furthermore, we performed pilot studies in four typically developing human neonates, whose fontanels are excellent sonographic windows, and produced high-quality ultrasound brain images and QUS measurements with consistent values. Here we want to apply knowledge gained from the NHP work and develop QUS technology that will enable capturing cell death in neonatal human brains. First, we will optimize QUS acquisition and analysis of echo data in typically developing human neonatal brains and obtain normative data for key QUS features in selected brain regions in the basal ganglia. These regions are the caudate nucleus (CN), globus pallidus (GP), putamen (Put), and thalamus (Th). Then, we will apply QUS in neonates with brain injury caused by perinatal asphyxia. In these brains, hypoxic/ischemic cell death can occur in the CN, GP, Put, and Th, and is accompanied by diffusion restriction on magnetic resonance imaging (MRI). We expect that QUS features obtained from the CN, GP, Put, and Th in neonates with perinatal asphyxia, who demonstrate diffusion restriction and altered apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps in the basal ganglia on MRIs, will differ from those in typically developing human neonates. The translational significance of this research is immense. QUS may enable the study of when and where cell death occurs in infants’ brains and follow its time evolution. It may provide invaluable means of neuromonitoring in neurocritical care, help address fundamental research questions and assist in optimizing clinical treatments.

NSF Awards · FY 2024 · 2024-08

This project develops an integrated research, education, and outreach program to provide a foundation for understanding and advancing the generalization capabilities of fake (AI-generated) image detectors. With the rise and maturity of artificial intelligence (AI) generative models, fake images are proliferating in the digital world at an unprecedented rate. Fake images can cause harm in a variety of ways by deceiving, manipulating, and misinforming individuals and society at large. For example, they can be used for blackmail, disinformation, or financial fraud, etc. To make matters worse, there is no longer a single source of fake images. Synthesized images could take the form of realistic human faces generated using one type of fake image generator or they could take the form of complex scenes generated using another type of fake image generator. One can be almost certain that there will be more methods developed for generating fake images coming in the future. To combat the potential harm caused by fake images, this project aims to advance technology in image forensics, and build a deep understanding as to how AI-generated images differ from real images. In addition to scientific impact, this project performs complementary educational and outreach activities that engage students in research and STEM. This research is to provide a foundation for gaining a deeper understanding of, and for advancing fundamental research in, detecting AI-generated images. In particular, it will focus on the generalization properties of fake image detectors. Specifically, it will investigate two main thrusts: (Thrust I) Understanding what makes fake AI-generated images fake, including the role of generative models vs. data, a novel benchmark and toolbox for universal fake image detection, and understanding the features that a fake detector focuses on. In Thrust II, the project will advance universal fake AI-generated image detection, including a novel frequency masking strategy, a few-shot adaptation approach for learning with only a few training examples, and extensions to video to account for spatio-temporal aspects of realism. Both thrusts will be studied in the context of creating generalizable (universal) fake image detection algorithms. The investigators' wealth of experience in generative models and understanding model robustness, as well as initial work in this space, make them well-positioned to formulate and solve the relevant challenges, and lays the groundwork for the project. 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 · 2024-08

Sjögren's disease (SjD), a common systemic autoimmune disease characterized by severe oral and ocular dryness and multi-organ involvement, has no disease modifying treatment available. Clinical trials to date have failed due to the clinical and biologic heterogeneity of SjD. As a result, it is important to subset SjD patients into pathobiologically similar groups, called endotypes. SjD patients who have a high number of lymphocytes in their salivary gland (SG) represent one endotype. This biopsy positive (Bx+) endotype has high cytokines that signal via Janus kinases (JAKs) and higher levels of autoantibodies. Another approach to generate biologically unique endotypes is to stratify by hallmark symptoms of dryness, pain, and fatigue. Mesenchymal stromal cells (MSCs) are enriched in SjD compared to control SGs and have divergent signaling pathways preclinically, suggesting that SG-MSCs might drive pathogenesis. The long-term goal is to mechanistically explain and generate therapies that target SG-MSCs in SjD. The overall objective of this application is to define how SjD endotypes modify MSC immunobiology, comparing mouse to human SG-MSCs. The central hypothesis is that MSCs are drivers of SjD pathogenesis through their maladaptive response to the cytokines unique to certain endotypes. The rationale for this project is based on new data that SG-MSCs treated with IFN become maladaptive and promote CD4+CXCR3+ T-cell and B-cell chemotaxis. These new data identify targetable maladaptive MSC behavior; however, the response to other inflammatory cytokines in endotype-specific SjD SG microenvironments is unknown. This knowledge is critical to define and target the pathogenic contributions of SG-MSCs in SjD. The central hypothesis will be tested by pursuing two specific aims: 1) determine the signaling pathways driving maladaptive mouse MSCs and SjD using multiple JAK inhibitors as translational immunopharmacological probes in Bx+ endotype; and 2) define pathologic responses and heterogeneity of human MSCs in SjD by endotype. In aim 1, Bx+ phenotype SjD mice will be treated with JAK inhibitors as immunopharmacological probes. SG-MSC transcript and protein profile, localization, and neighbors in situ will be determined using sequential FISH with hybridization chain reaction (seqFISH-HCR) by each treatment. SG-MSC maladaptive phenotype and function will be verified in vitro and SjD-like disease in mice will be measured. In aim 2, human MSC transcript and protein profiles, localization, and neighbors will be determined, comparing endotypes. Maladaptive MSC behavior will be mechanistically tested in vitro with aim 1 JAK inhibitors, identifying parallels between mice and humans. The proposed research is innovative, because it focuses on SjD endotypes, critical to guiding future pharmacologic MSC targeting and to deconvoluting SjD heterogeneity. Furthermore, it uses seqFISH-HCR technology for high resolution insight into the MSC phenotype in situ. The proposal is significant because it clarifies the pathobiology of a novel immunomodulatory cell, the SG-MSC, by SjD endotype, opening new horizons for MSC- and SjD endotype-targeted therapies. It also performs highly translatable comparisons in vivo between JAK inhibitors.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY/ABSTRACT Biomedical research data sets are increasingly being deposited in public, centralized databases, such as the Sequence Read Archive (SRA), to which researchers submit sequencing-based data. Large centralized databases greatly enable opportunities for training powerful machine learning models, as well as for reanalysis and cross-study meta-analysis of biomedical data. These analyses can be used to answer questions that were not addressed in the papers first describing the data, including those that could only be answered by aggregating data from multiple studies. Unfortunately, researchers have not been able to fully capitalize on databases of biomedical data sets largely because the metadata provided for data sets are often unstructured, unstandardized, and incomplete. For example, the primary metadata for samples with assays deposited in the SRA are provided as a list of key-value pairs, with no standardization of the keys or values and no required fields. Such poor metadata pose challenges for integrating datasets with these databases as well as for querying for specific data sets of interest. To fully enable the opportunities offered by large biomedical databases, we propose to develop automated methods for curating the metadata contained within them. These methods will standardize the metadata of a database by assigning to each record a set of standardized terms for concepts represented within biomedical ontologies and will additionally identify the relationship between each concept and record (e.g., a record’s corresponding biological sample was derived from liver tissue). A complementary set of methods will be developed to identify missing or unstandardized concepts in metadata. The developed methods will use machine learning approaches that can be trained with minimal human effort. To achieve high accuracy with sparse training data, we will take advantage of cutting-edge approaches in deep learning, natural language processing, and active learning. As a specific application of these general methods, we will use them to standardize and enhance the metadata contained within the SRA and the Gene Expression Omnibus (GEO) for the most commonly assayed species using a comprehensive set of ontology concepts and relationships. The resulting standardized metadata for the SRA and GEO will be made freely available and easily accessible via a web interface, bulk downloads, and R and Python interface packages. The developed methods, along with the standardized metadata they produce, will allow biomedical databases to be used to their full potential in advancing our understanding of fundamental biology and human health.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY Identifying modifiable prenatal and early life risk factors is crucial for developing strategies to prevent childhood asthma. While individual birth cohort studies have identified several asthma-related genetic, personal and environmental risk factors, these studies were mainly limited to homogeneous cohorts and modest sample sizes. Pooling data from multiple diverse cohorts could address these limitations. Based on the momentum and accomplishments of collaborative multicenter research in the Children’s Respiratory and Environmental Workgroup (CREW), we propose establishing a children's allergy and asthma data hub to ensure enduring access for the scientific community to allergy and asthma birth cohort data. The proposed Children's Allergy and Asthma Data Repository (CADRE) will leverage hundreds of millions of NIH dollars invested in conducting allergy and asthma cohort studies by pooling and harmonizing these data for future collaborative studies. We propose to initiate CADRE using CREW’s existing data set and data-sharing procedures and then expand this critical resource to invite, facilitate and accommodate broad participation from the scientific community. The CADRE data repository will invite collaboration and ensure ease of use by providing value-added services to facilitate data accessibility, usability, harmonization and interpretation. To accomplish these goals, we propose three specific aims. First, we will expand the CREW repository of birth and infant cohorts’ data focused on identifying risk factors, clinical phenotypes and natural history of childhood allergic diseases and asthma by collecting more data from the original cohorts and by recruiting new collaborators and cohorts to contribute data to the repository. Second, we will develop the CADRE data collaboration platform as a foundation for a national data ecosystem for secure integrative allergic diseases and asthma research. Third, we will establish the administrative infrastructure to develop, refine and establish an ethical and compliant data-sharing and governance process for the acquisition, linkage, usage, and analysis of multidimensional data containing protected health information (PHI) and secure export non-PHI results to investigative teams. Establishing the CADRE data repository will preserve, pool, and harmonize the precious and far-reaching data sets of US childhood asthma and allergy cohorts. CADRE will develop secure procedures to facilitate investigators' access and utilization of these comprehensive and irreplaceable data for collaborative studies while simultaneously protecting the privacy of research participants.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY As an aggressive and invasive type of breast cancer, triple-negative breast cancer (TNBC) has limited effective treatment options in the clinic. Chemotherapy, represented by Taxol and Abraxane, is the first-line therapy for TNBC, while TNBC patients who initially respond to chemotherapy will eventually develop drug resistance following relapse. Immunotherapy, including immune checkpoint inhibitors, has only achieved success in treating a small subset of patients with TNBC characterized by a high burden of somatic mutations. Surgery remains the major treatment option for TNBC; however, the high frequency of TNBC recurrence after surgery could lead to poor patient prognosis and high mortality. These highlight a critical unmet clinical need to develop a new therapeutic modality that can serve as effective monotherapy or synergize with current first-line treatments to prevent post-surgical TNBC recurrence. Intracellular ferritin serves as a key regulator to balance the increasing need for iron to support TNBC growth while mitigating the damage of excess iron through mineralizing and storing the intracellular iron ions. It has been well established that ferritin expression correlates positively with TNBC cell proliferation, with more ferritin concentrations in TNBC tumors than in normal breast tissues. Clinically, the serum ferritin level could serve as an indicator of disease severity in TNBC patients. In our preliminary studies, we have developed a ferritin-degrading proteolysis targeting chimeras molecule (designated DeFer) that can leverage the intracellular ubiquitin-proteasome system to degrade ferritin, leading to TNBC cell pyroptosis. In vivo anti-TNBC efficacy was substantiated on 4T1 TNBC-bearing mice. Our study suggested that ferritin can serve as a novel TNBC drug target, and its degradation could inhibit TNBC growth. In this proposal, we will further optimize the developed DeFer, investigate the underlying ferritin degradation mechanisms, and test its in vitro and in vivo anti-TNBC efficacy. Furthermore, to improve the in vivo pharmacokinetics and biodistribution of DeFer, we will load DeFer into a platelet-based delivery system to improve the circulation time and facilitate the selective accumulation at the post-surgical TNBC site. Finally, we will combine DeFer-loaded platelets with immune checkpoint inhibitors to investigate the synergistic anti-TNBC recurrence efficacy on both murine TNBC and human TNBC PDX models.

NIH Research Projects · FY 2026 · 2024-08

Project Summary/Abstract: Efficient access to molecules of importance to human health has long driven the development of innovative synthetic methods. In recent years, greater emphasis has been placed on exploring stereochemically complex molecular space with high Fsp3 that is not well-represented in typical compound screening libraries. In this context, N-heterocycles and aminated carbocycles are attractive targets, as they are widely prevalent in drugs, natural products, biomolecules and ligands. We are developing two modular platforms capable of rapid, flexible transformations of simple precursors (alkenes, dienes, allenes, imines) into azetidines, pyrrolidines, piperidines and other N-heterocycles and amine-bearing carbocycles. Mechanistic insights are used to tune the fate of underexplored reactive intermediates that include methyleneaziridines, aziridinium ylides and 2-amidoallyl cations. The first platform leverages our expertise in catalyst-controlled nitrene/carbene transfers to furnish aziridinium ylides that are shuttled along diverse paths to deliver stereochemically rich amines and enantioenriched N-heterocycles. A second platform explores mild ways to generate 2-amidoallyl cations from allenes and engage these reactive species in enantioselective cycloadditions for rapid syntheses of densely functionalized carbo- and heterocycle synthetic building blocks. In addition to the continued development of synthetic methods to furnish molecules with high Fsp3 linked to higher success in drug screening libraries, the biological testing of chemical space unlocked by our work is important to its long-term significance. Several compounds from our libraries show a range of anti-cancer, anti- TB, antimalarial and other activities, but preparing individual compounds is time-consuming and labor-intensive, limiting the impact and significance of our work. We are adapting our synthetic methods to the synthesis of DNA- encoded libraries that will be made available to screening facilities at UW-Madison and more broadly to the academic and industry communities. Last, our innovative methods for amine synthesis will be applied to natural product analogs of the anti-malarial compound jogyamycin and the anticancer antibiotic nogalamycin to understand structure-activity relationships, with a long-term goal of uncovering new small molecule binders of the ribosome. Jogyamycin and nogalamycin are challenging targets for total synthesis, as molecules that bind the ribosome often do not follow the Lipinski ‘rule of 5’ due to the highly electronegative surface potential and limited buried surface area differ from proteins and often contain several contiguous stereocenters bearing polar hydroxyl and amine groups that are tedious to prepare using current synthetic methods, but are readily accessible using our synthetic methodologies.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Quaovi Sodji, MD, PhD is a Tenure track Assistant Professor in the Department of Human Oncology at the University of Wisconsin School of Medicine and Public Health in Madison. His long-term goal is to use his acquired clinical and research expertise in radiation oncology, drug discovery and cancer immunotherapy to develop treatments that can improve the lives of patients with solid tumors including the pediatric cancer neuroblastoma. He aims to develop a translational research program investigating the use of low-dose targeted radionuclide therapy (TRT) to sensitize tumors to killing by chimeric antigen receptor (CAR) T cells. The purpose of this career development award is to provide Dr. Sodji with the support and mentorship needed to develop a successful translational research program. His proposal will be performed under the primary mentorship of Zachary Morris, MD, PhD, an expert in immuno-radiobiology and Christian Capitini, MD, an expert in CAR T cell therapy. Other members of his mentorship committee include Paul Sondel, MD, PhD, an expert in immunogenetics, Jamey Weichert, PhD, an expert in radionuclide chemistry and Bryan Bednarz PhD, an expert in nuclear medicine. The training plan includes formal coursework, seminars, national conferences, and hands- on training activities to expand expertise in 1) tumor immunology and CAR T cell engineering, 2) radiochemistry and dosimetry, and 3) laboratory management, mentoring, and grantsmanship. CAR T cells are “living drugs”, tailored to each patient that can directly recognize and kill cancer cells. They have been very effective against blood cancers, but not against solid tumors including one of the most common childhood cancers, neuroblastoma, which is very deadly cancer when it relapses. This limitation of CAR T cells in solid tumors has been attributed to their inability to penetrate the tumors and the loss of their killing potential over time. TRT, a form of radiation where a radioactive chemical is linked to a drug that can selectively deliver it to cancer cells, has been shown to increase the infiltration of immune cells into tumors and the susceptibility of tumor cells to immune-mediated destruction. The goal of this proposal is to use TRT to deliver radiation to all tumors throughout the body to overcome the factors that presently limit the success of CAR T cells against solid tumors like neuroblastoma. This proposal aims to 1) identify the amount of TRT-emitted radiation that CAR T cells can safely withstand, 2) determine if and how this form of radiation increases the potential of CAR T cells to kill tumor cells and 3) evaluate whether TRT in combination with CAR T cells is effective in eradicating tumors in murine models of neuroblastoma. This proposal will lay the groundwork to justify the evaluation of this combination therapy in clinical trials for patients with neuroblastoma. Our anticipated findings will have enormous translational potential as a therapeutic approach that could be tested in any cancer for which a tumor-selective TRT agent and a tumor surface antigen-specific CAR T cell could be engineered.

NIH Research Projects · FY 2024 · 2024-08

Project Summary/Abstract We are seeking to acquire a Delta Q Isotope Ratio Mass Spectrometer (IRMS) along with its peripherals for the UW Isotope Ratio Laboratory. The need for this instrument arises because our current Delta Plus and Delta V instruments are obsolete and no longer, or soon to be no longer, supported by the manufacturer and pose extreme challenges in terms of operability and repairs. The upgraded Delta Q IRMS is a versatile and high- performance instrument that accurately measures stable isotopic ratios in both gaseous and solid samples. Its enhanced capabilities and efficient sample analysis per isotope will ensure the continuity of our lab's operations. Specifically, this instrument will play a vital role in measuring stable isotopic ratios of hydrogen and oxygen, with a specific focus on doubly labeled water studies. These studies are of utmost importance in the fields of nutritional sciences and obesity research. The precise measurements and advanced technology of the Delta Q IRMS will provide reliable data for investigating energy balance components, thus making a significant impact in the obesity field. Accurately measuring energy intake and expenditure is crucial for comprehending human energy balance, and the doubly labeled water method stands out as the most precise approach for this purpose [1]. Conventional methods that rely on self-reported data through diet records and questionnaires are susceptible to errors and bias[2], while the doubly labeled water method, coupled with changes in body energy stores, has been advocated by Ravelli and Schoeller, 2021 [3]. As a result, the doubly labeled water method, which relies on isotope ratio analyses, is indispensable for understanding energy balance, preventing obesity and chronic diseases, and also addressing malnutrition [4]. Access to isotope ratio analyses is limited to a few expert labs worldwide, and our lab has been at the forefront of pioneering the doubly labeled water method for the past 25 years [5]. We have provided analyses to 28 investigators leading to 107 publications, which have been cited more than 47,000 times. To continue meeting the demands of our NIH-funded users, the acquisition of the Delta Q IRMS is indispensable. It will (1) provide precise and accurate isotopic ratio measurements, enabling in-depth exploration of energy expenditure, energy intake, body composition, and metabolic pathways in humans and animals, (2) enhance the health-related goals of our users' investigations, and (3) foster biomedical research within and beyond our institution. The acquisition of this instrument is vital for cutting-edge research and its availability in the UW Isotope Ratio Lab will have a profound impact on the scientific community. Ensuring the continuous generation of accurate data will enable valuable insights and advancements in diverse research areas that rely on stable isotope analyses. Moreover, it will empower researchers to make significant strides in various health-related fields. This instrument plays a crucial role in advancing scientific knowledge and fostering innovation ultimately contributing to breakthrough discoveries. In light of these considerations, we request the acquisition of a shared Delta Q IRMS to support our services to users and contribute to advancements in obesity and other fields. S10: Isotope Ratio Mass Spectrometer for UW Bioanalytical Isotope Ratio Laboratory

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Lower-income Americans increasingly rely on a range of unsecured debt sources (e.g., credit cards, payday loans) to help manage expenses, likely contributing to frequent fluctuations in debt levels throughout the year. Frequent fluctuations in debt amounts, use of certain debt sources, or for particular reasons (e.g., managing unexpected expenses) may be adaptive, helping individuals to reduce stress and anxiety related to financial management. Yet, in some cases, these fluctuations may be harmful, increasing experiences of stress, anxiety, and depression. Due to existing survey limitations, we know little about experiences of within-year changes in debt and their potential role as health stressors or stress-reducers for lower-income individuals. The R21 project will systematically capture short-term changes in debt amounts and composition and evaluate associations with generalized stress, anxiety, and depression. The project has three specific aims: 1) Field a novel smartphone survey that collects biweekly self- reported health, income, and debt data from 410 lower-income adults for one year. The development of a biweekly survey on debt is supported by prior research that underscores the importance of capturing short-term fluctuations in income to understand economic insecurity of lower-income individuals and families. The proposed survey addresses the lack of short-term data on debt holdings across a range of unsecured and understudied sources. 2) Develop standard measures of within-year fluctuations in debt burdens across debt sources using data collected from the survey. In doing so, we introduce the concept and measurement of within- year debt fluctuations to the multidisciplinary literature on debt and health. 3) Analyze the data to provide the first insights on the scope of lower-income individuals’ experiences with short- term debt fluctuations and consequences for health, with attention to variation by race. The study is the first to collect high-frequency data on debt and health via mobile surveys, with implications for advancing data collection efforts in this area. Findings will inform economic and family policy, with insights on how addressing debt fluctuations in addition to overall debt burdens in policy design may have positive public health impacts. Data created through this project will be made publicly available to advance research across disciplines concerning family functioning, poverty, and health.

NSF Awards · FY 2024 · 2024-08

This award supports the Neotoma Paleoecology Database. Neotoma is one of the most widely used and trusted international data resources for fossil data, growing rapidly in the volume and variety of its data holdings, functionality of its software services, and the size and scope of its user community. This award will allow Neotoma to grow and enhance systems to support higher rates of data additions, more streamlined data curation, and better support solutions for new communities seeking to use Neotoma data. This project provides access to publicly funded data and supports researchers, educators, and the public by providing a high-quality, expert-curated open data resource for paleoecological and paleoenvironmental data. Specific activities for this project include better support for rapid upload of hundreds to thousands of datasets from participating research teams through enhancements to the Data Bulk Uploader System (DataBUS), with newly added ORCID user authentication and support for the popular Linked Paleodata (LiPD) format. Embargo Manager will support early data contributions and better data management practice, in alignment with NSF Division of Earth Sciences (EAR) Data and Sample Policy. The Hierarchical Vocabulary and Taxonomy Manager (HVTM) will improve data quality and interoperability by enabling efficient viewing and curation of controlled vocabularies. Neotoma will freely upload supported data types, with priority for NSF-EAR PI data, and will help on-board major geoscience paleodata communities. Neotoma PIs will develop and provide multiple training support activities for scientists, with focused workshops for early career researchers (ECRs) and scientists from underserved regions, multi-lingual support for workshops and online resources, publicly posted training videos, and model workflows for data handling. Neotoma developers will reduce barriers to access and support artificial intelligence and machine-learning applications by deepening Neotoma’s metadata provisioning to Science-on-Schema and DataCite. Lastly, Neotoma stewards will create custom-tailored training and leadership opportunities for ECRs by designing workshops, videos, and code vignettes to address ECR-identified challenges. 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 · 2024-08

The goal of this project is to develop technologies that can identify and separate cell type based on their internal chemistry. Cells exhibit different amounts of fluorescence based on the presence of certain molecules. Knowledge of these molecules can be used to confirm cell properties such as metabolic activity. This knowledge can be used to determine that stem cells are healthy and viable for transplantation for example. In this project the approach will be used to study and identify adult brain stem cells, a cell population that is under study for research into cognitive diseases and may be used as a therapeutic strategy for such diseases. This project will create a new cell sorting technique based on measuring thefluorescence from these molecules to enable improvements in isolating stem cells. Significantly, the technique is label-free and rapid, suggesting that it can be adapated in the work flow for stem cell purification. These concepts are shared with K-12 students through high school apprentices in the lab and an immersive rural summer science camp. Preliminary data from multiphoton fluorescence lifetime imaging studies show changes in the fluorescence lifetime of NAD(P)H, a molecular indicator of cell metabolic activity. New NAD(P)H lifetime flow cytometry technologies have also been developed that can accurately profile the same biological changes captured with standard multiphoton approaches. However, real-time cell sorting capabilities based on NAD(P)H lifetimes are not yet available. Therefore, this proposal will develop new sorting capabilities for NAD(P)H lifetime flow cytometry to (1) create a single cell deposition module that provides cell-to-cell correspondence between NAD(P)H lifetimes and self-renewal capacity to enable label-free identification of stem cells within a mixed population, and (2) separate metabolic subpopulations within stem cell lines for improved quality control of these lines. The development of active sorting technologies based on label-free NAD(P)H fluorescence lifetimes, including binary sorting into bins and single cell deposition systems, would provide a radically different approach for cell enrichment and single cell functional assessments. 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 · 2024-08

Farm financial programs are crucial for farmers’ profitability and help them adapt to climate change and market risks. These programs, which include crop insurance, agricultural loans, and conservation cost-share programs, are widely used by farmers and attract fierce discussion and debate in the U.S. Farm Bill and worldwide. They serve not only as financial tools but also as key factors in guiding farmers’ land practices toward either conservation or non-conservation management. These land practices have profound impacts on soil health, water quality, and land sustainability, which in turn influence risks to individual farmers and broader societal and environmental risks over the long term. This research illuminates two areas of farmer behavior related to the Farm Bill: (1) the decision-making processes of farmer households regarding financial programs and land practices and (2) the relationship of these financial program decisions to land practices and, subsequently, the ability to adapt to climate and market risks. This study addresses understudied complex interactions among individual decision making and risk management, social identity theory, and government financial support. The team employs both a farmer survey and in-depth interviews to gather data. The study involves a diverse range of farmers, such as those involved in row crops, forage, confinement livestock, and grass-based livestock operations, enabling comparative analysis. The study aims to deepen understanding that can bring about a reshaping of financial programs that are central to the Farm Bill. By exploring the decision-making processes of a wide variety of farmers, the project enhances knowledge of diverse agricultural systems. The findings inform policy recommendations that may contribute to food, water, and land security as well as improve the well-being of farming communities. 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 · 2024-08

How repeatable is evolution? Can genetic variation in ancestors repeatedly give rise to new species adapting in parallel to environmental challenges? Studying a highly diverse group of North American ground beetles, the researchers will explore the hypothesis that natural selection acted on the same molecular pathways when species adapted to alpine environments during past glacial-interglacial climate cycles. Additionally, comparative analysis of recently diverged alpine beetle species will shed light on the process of climate-based adaptation, which can improve our understanding of the risks and possible evolutionary outcomes of rapid contemporary climate change. The specific molecular pathways involved in environmental adaptation are highly relevant to ongoing efforts to mitigate the effects of climate change in conservation management, pest management, and even human health. This project will broaden participation and active learning opportunities through mentoring of high school student interns through the Wisconsin Youth Apprenticeship Program, as well as undergraduate and graduate students from underrepresented backgrounds through programs at the University of Wisconsin-Madison. These mentees will receive training in genetics, physiology, data science, and science communication. Alpine environments exhibit strong environmental gradients that are expected to exert selection pressure on stress tolerance traits, including temperature tolerance, desiccation resistance, and hypoxia tolerance. Through comparative genomic statistical analyses of 50 closely related beetle species (Coleoptera: Carabidae: Nebria (Catonebria)), the researchers will test for repeated evolution in protein-coding and non-coding regions, genome structural variation, and gene families. In addition, they will test for repeated gene regulatory evolution in abiotic stress pathways through stress tolerance experiments, using multiple sister-species pairs that vary in their ecology (riparian versus alpine habitat specialists). This combination of analyses will test whether there is genomic evidence for repeated evolution in ground beetles adapted to alpine environments and identify the specific genetic mechanisms that enable these species to survive environmental stress. The results will also identify broadly relevant functional genomic data that can advance comparative research on ecologically important traits in the tree of life. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY/ABSTRACT The American Society of Biomechanics (ASB) is the foremost society for biomechanics research and engagement in the United States. The Society’s annual meeting brings together an interdisciplinary group of researchers interested in the application of mechanical principles to biological problems, both basic and applied. The research portfolio of ASB’s membership is highly interdisciplinary and complements the missions of the National Institute of Child Health and Human Development (NICHD) Center for Medical Rehabilitation Research (NCMRR) and National Institute on Aging (NIA) by fostering discovery and dissemination of scientific knowledge through basic, translational, and clinical research that aims to enhance the health, productivity, independence, and quality of life of people with physical disabilities, musculoskeletal diseases, and injuries, across the lifespan. The 2024 annual meeting of the ASB, which will be held in Madison, Wisconsin, is being hosted by faculty at the University of Wisconsin-Madison. The meeting will feature a variety of activities designed to foster a lively interchange of ideas, including podium presentations, thematic poster sessions, invited symposia, keynote lectures, poster presentations, topical tutorials, laboratory tours and a variety of mentoring and diversity events. Student participation and mentoring are priorities for the Society. Meeting attendance is anticipated to exceed 800 delegates, of which nearly half are expected to be students. Mechanisms have been established to specifically encourage and support the participation of women, persons from underrepresented backgrounds, and persons with disabilities. Under the proposed award, the Society will continue to administer its successful Diversity Travel Award program, add a Faculty Scholars Award program to add breadth in new institutions and geographical areas, and organize four key events: (i.) a Mentorship Lunch focusing on guidance and professional development strategies for early- career scientists; (ii.) a Women in Science event convening the women in the Society to offer strategies for development across all career stages; (iii.) a Diversity and Inclusion Lunch bringing together members of the Society to network across boundaries and discuss strategies to support those who face uphill battles to full inclusion; and (iv.) a STEM Educational Outreach Program engaging underserved youth and persons with disabilities in the local community through a biomechanics activities exposition. This grant application seeks funding to continue supporting Diversity Travel Awards for ~16 attendees of the meeting, five Faculty Scholar Awards, and facilities and transportation costs for the STEM Educational Outreach Program. NIH-R13-supported programs were offered at the last six (2015-2023) in-person annual conferences, including travel awards to a diverse group of individuals with disabilities, from underrepresented backgrounds, or facing economic barriers to attendance. These awards have proven to be successful in increasing diversity amongst young active members who over time will become the future leaders of the Society and the field.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY: 30 lines. The canonical phosphoinositide (PI) 3-kinase (PI3K)/Akt signaling pathway uses free membrane PI lipid to regulate cell growth and is frequently hyperactivated in cancer. Akt is also activated in the nucleus by poorly understood mechanisms. We discovered a nuclear PI3K/Akt pathway composed of PI kinases/phosphatases that modify phosphatidylinositol phosphates (PIPns) linked to p53 (p53-PIPn signalosome). PIPKI synthesizes p53-PIP2 that stabilizes it and regulates MDM2. The PI3K inositol polyphosphate multikinase (IPMK) converts p53-PIP2 to p53-PIP3, which is reversed by PTEN. p53-PIP3 recruits the full Akt pathway leading to nuclear Akt activation that regulates FOXOs, MDM2, genomic stress and cell survival. Hypothesis/Objective: Nuclear Akt is dose activated by p53-PIP3. The p53-PIPn complex is stabilized by MDM2 and MDM2 is also PIPn-linked. IPMK and PTEN control the generation of p53-PIP3 that activates nuclear Akt. Mutant p53-PIP3-Akt signaling, unlike p53wt, is constitutively activated and further stimulated by genotoxic stress, resulting in enhanced cell survival and promoting tumor initiation and promotion. We will test this in 4 aims. Aim 1. Determine the mechanisms of nuclear Akt activation by the p53-PIPn signalosome. Define the roles and mechanisms of IPMK kinase and PTEN activity toward the p53-PIPn complexes by elucidating the structural interaction of IPMK and PTEN with p53. Using this structural information, we will determine how genotoxic stress- induced PTMs on p53, IPMK and PTEN regulate the p53-PIPn signalosome and nuclear Akt activation. Aim 2. The residues that link PIPn to p53 and MDM2 will be defined, and mutants made that block PIPn linkage and nuclear Akt activation. We will determine if there are different PIP linkages for p53wt and p53mt. Aim 3. Define mechanisms for p53 stabilization by MDM2, sHSPs and PIPns. The stability of p53 is key for p53-PIP3 activation of Akt and is regulated by MDM2. MDM2 is also PIPn-linked and the mechanisms by which PIPns and sHSPs regulate the p53-MDM2 pathway will be studied. MDM2 PIPn linkage and binding mutants will be used to investigate the role of PIPns in regulating MDM2 ligase activity, interactions with p53 and p53 stability. The PIP kinases and PTEN that generate the MDM2-PIPn complexes will be defined and studied. Aim 4. Define roles of the p53-PIPn signalosome in regulating tumor growth. Using cellular and murine models of breast cancer, we will determine the functional role of the p53-PIPn signalosome in tumor suppression by p53wt and transformation/tumor progression by p53mt. We will also determine whether the expression of p53- PIPn and p53-PIP3-Akt complexes in clinical breast cancer specimens correlates with clinical outcomes. Significance: We have discovered a novel nuclear PI3K/Akt pathway scaffolded on p53 that is independent of the canonical membrane-localized pathway and insensitive to PI3K inhibitors in the clinic, underscoring its therapeutic relevance. Also, as the p53-PIP3-Akt pathway is differentially activated for wild-type and mutant p53, this may represent a fundamental mechanism for mutant p53 oncogenic activity.

NSF Awards · FY 2024 · 2024-08

Semiconductors are materials with electrical conductivity that can be switched on and off and that have enabled modern electronics. However, most electronics rely on silicon and gallium arsenide, which have been the semiconductor materials of choice for more than fifty years. These semiconductors are being pushed to their limits and cannot meet the demands of next-generation electronic devices. Carbon nanotubes are atomically thin semiconducting wires that conduct more electricity per area and changes conductivity with less voltage, thus outperforming conventional semiconductors and enabling high performance circuits and devices. Aligned arrays of carbon nanotubes are poised to become the semiconductor of choice in logic microprocessors by substantially increasing speed and decreasing power consumption. For nanotubes to be most useful, they must be lined up in the same direction in a dense single layer, so that electricity can rapidly and efficiently travel through them. However, there are fundamental nanomanufacturing problems that need to be solved. This award supports research that aims to develop scientific understanding associated with using chemical and topographic cues on a substrate to create arrays of nanotubes that are not just aligned but have tailored packing density and consistent inter-nanotube spacing – critical for realizing their ideal electrical performance. Integrated with the research goals are outreach activities such as a YouTube tutorial about the basics of nanoelectronics for the public, research and mentoring opportunities for undergraduates, and dissemination through Engineering Open House and science fairs. The overarching goal of this project is to conduct fundamental research into the scalable nanomanufacturing of carbon nanotube arrays using chemical and topographical patterning approaches and achieve nanotube-nanotube alignment to ±1˚, nanotube packing density of 200-250 μm-1, and improved regularity in packing needed to drive transformative advances in future microelectronic technologies. The research uncovers the factors that affect the lateral diffusion and reordering of nanotubes in trenches, through chemical cues in the trenches, in conjunction with design of new polymer wrappers that are programmed to set the nanotube-nanotube spacing and pitch. The project advances the trench-based assembly of nanotubes to their current limits and assembles nanotubes in trenches that are only a single nanotube wide. The project uses high resolution and large-area morphological and electrical metrology tools for quality assurance. The use of MD simulations provides insights on the possible carbon nanotube self-assembly mechanisms and helps screen different polymer chemistries and surface functionalization strategies. High-performance field effect transistors are fabricated from aligned carbon nanotubes and benchmarked against conventional semiconductor materials and previous carbon nanotube-based transistor technologies. 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.