University Of Wisconsin-Madison

NIH Research Projects · FY 2025 · 2024-09

Project Summary: The laboratory of Dr. Joshua Lang is focused on targeting treatment resistant PCa through developing new biomarkers for precision medical strategies using liquid biopsies; discovering novel vulnerabilities in PCa; and translating these discoveries and biomarkers into clinical trials. Dr. Jamie Sperger has been an integral part of Dr. Lang’s laboratory for the past 11 years and has contributed to 3 NCI funded RO1 projects and co-authored 24 publications. Together, Drs. Lang and Sperger have developed novel liquid biopsy assays which helped identify persistent Androgen Receptor (AR) signaling in men treated with AR signaling inhibitors (ARSIs) due to complex genomic alterations, including AR gene rearrangements, amplifications, and mutations that can be detected in liquid biopsies. Currently Dr. Sperger leads two NCI funded projects in the Lang laboratory. The first project “Molecular regulation of Trop-2 in advanced prostate cancer: Biomarkers and therapeutic niches” (R01CA276269) aims to leverage liquid biopsies as a tool to better understand which patients might benefit from Trop-2 targeted therapies. The second project titled “Enhancing epigenetic analysis of rare cells with multi-phase microfluidics” aims to develop novel methods to detect epigenetic changes by developing new methods using technology co-developed with the laboratory of Dr. David Beebe. Dr. Sperger is uniquely qualified to lead these projects, with over a decade of experience with assay development for liquid biopsies using microfluidics.

NSF Awards · FY 2024 · 2024-09

This project studies some nonlinear partial differential equations (PDE) that appear naturally in chemistry, physics, and engineering and which arise, for example, in the study of crystal growth, combustion, coagulation-fragmentation processes, game theory, and optimal control theory. These equations have connections with a host of other areas of mathematics, including the calculus of variations, differential games, dynamical systems, geometry, homogenization theory, and probability. The main goal of the project is to discover new underlying principles and general methods to understand the properties of solutions of the PDE under investigation. A key object of the research is a crystal growth model in which the crystal grows in both the horizontal direction, by adatoms, and the vertical direction, by dislocations or nucleation in a supersaturated media. To make practical use of the model, it is important to understand the qualitative and quantitative aspects of the growth speed and the shape of the crystal. The mentoring of graduate students in research is an important educational component of the project. The work of the project involves two themes. The first is about critical Coagulation-Fragmentation equations and their connections with Hamilton-Jacobi equations. The Principal Investigator (PI) is interested in regularity and large-time behavior results for Hamilton-Jacobi equations which give implications on the existence of mass-conserving solutions of Coagulation-Fragmentation equations and their behavior. The second involves level-set mean curvature flow equations with driving and source terms and applications in crystal growths and turbulent combustions. The focus is on the regularity, the large-time average, and the large-time behavior of the solutions. The PI and his collaborators have recently developed new approaches which led to solutions to several open problems in these and related areas. The new approaches are expected to be developed further in this project, thereby bringing fresh perspectives on and insights into the study of nonlinear PDE and viscosity solutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract To meet national needs of the future, the United States must increase the production of biomedical college graduates and draw on a broader range of talent, particularly among historically excluded communities (HECs). Yet an alarming proportion of college students who declare STEM majors switch to other majors before graduating. Moreover, students who belong to certain HECs enter STEM at the same rates as their white peers but leave far more frequently: 44% of white students leave STEM before graduation, whereas 58% of Latine and 66% of Black students leave. A seminal study of the reasons students leave STEM found that students frequently cite a fear of failure as important, but failure is rarely included as a topic in STEM coursework or as a focus for interventions. Therefore, a better understanding of the impact of failure and approaches that de-stigmatize and normalize failure is needed to assure greater success and retention of diverse students in the biomedical sciences. Scientists are affected by two types of failure that are natural parts of a career in science—personal setbacks and scientific failures—which influence advancement in academic and professional paths. But many students interpret a failure in college due to academic or personal struggles or due to failed experiments as an indication that they lack the ability to succeed in the biomedical sciences. In reality, when students encounter learning challenges, personal roadblocks, or wrong hypotheses and failed experiments, they must tap into productive failure responses to identify support structures, figure out what went wrong, adjust their approach, and try again. Productive responses to failures can be personal (e.g., a growth mindset; scientific self-efficacy, fear mitigation tools) or actionable, scientific approaches such as troubleshooting an experiment. By learning productive failure responses, students develop problem-solving skills, reasoning, and resilience, which strengthen a sense of belonging and lead to persistence in science. This study hypothesizes that if students are taught about failures experienced by successful scientists or engage in a structured research experience, they will be less discouraged when they experience difficulties or failures. This research will study the effects of an intervention on student failure responses and STEM persistence. The first experiment will test the effect of videos about personal and scientific failures on students’ behaviors and attitudes about failure and STEM persistence. The second will test these videos in two educational contexts, one containing a course-based undergraduate research experience (CURE), which may have synergistic effects with the intervention. To assess their failure responses, students will complete a survey and attempt an impossible scientific task—a biology video game. The analysis will seek to understand the interactions between the video intervention and participation in a CURE, student demographics, and course characteristics such as class size and placement in the curriculum.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Commercial cigarette smoking is a modifiable risk factor implicated in one-third of all cancer deaths. Nicotine is the chief addictive agent causing sustained smoking, but diverse tobacco products (e.g., e-cigarettes, FDA- approved nicotine replacement) allow nicotine self-administration on a continuum of harm. Public health gains could be substantial if people who use the most harmful products (i.e., combusted cigarettes) switch to a less harmful nicotine product. Nicotine pouches – microfiber sachets containing powered nicotine but no tobacco leaf – are a new class of oral tobacco products rapidly growing in popularity. Switching from cigarettes to nicotine pouches is likely to be health-promoting because pouches are not combusted and contain fewer harmful and potentially harmful chemicals than other tobacco products. However, we currently know very little about how readily people who smoke will adopt nicotine pouches, how effectively pouches can substitute for cigarettes when smokers are trying to avoid smoking, the importance of nicotine dose in effective cigarette substitution, and the mechanisms that may promote or hinder product transition. To address these key gaps, we will enroll 284 adults who smoke daily and are not planning to quit in the next 30 days in a randomized controlled trial (RCT). Participants will be randomly assigned to receive: 1) 3-mg nicotine pouches; 2) 6-mg nicotine pouches; 3) nicotine mini-lozenges (2- or 4-mg); or 4) no study product. Participants receiving a study product (nicotine pouches or nicotine mini lozenges) will be asked to use them for 4 weeks, an initial experimentation week, and then for a 3-week switching trial where they will be asked not to smoke their usual cigarettes and instructed instead to use their study product (if assigned one). Before and after the switching trial, participants will come to the clinic following overnight abstinence and will use their assigned product (if any) during a 30-minute sampling test to assess the duration of product use, subjective evaluations of study products, and suppression of craving and withdrawal symptoms under controlled conditions. During the 4 weeks of the study, participants will use a smartphone app to record, in real-time, each time they use cigarettes (primary outcome) or a study product. For a random daily subset of use events, participants will answer additional questions about the context of their use (e.g., affect, any restrictions on smoking) and potential mechanisms driving use (e.g., withdrawal alleviation, satisfaction). Participants will also complete 3 daily prompted random assessments to characterize non-use contexts. This innovative, rigorous, and timely research will provide critical information regarding: (a) the potential impact of providing nicotine pouches on smokers’ use of combusted cigarettes, (b) whether nicotine dose influences the ability of pouches to replace cigarette use, (c) whether pouches substitute for cigarettes more effectively than FDA-approved nicotine mini lozenges, and (d) product usage patterns and effects that may promote or hinder cigarette substitution These data could inform regulatory policy decisions regarding the potential public health impact of nicotine pouches.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Advancing age is associated with an increased risk of type 2 diabetes, which is exacerbated by the high prevalence of obesity in the aged. These disorders are risk factors for age-related diseases associated with significant morbidity and mortality, including cancer, cardiovascular disease, and Alzheimer’s disease. New approaches to maintain glucose homeostasis in the aged are therefore urgently needed. The objective of the proposed study is to test the ability of diets with reduced levels of isoleucine, valine, or histidine to reverse diet induced obesity and insulin resistance. We have shown that specifically reducing levels of either isoleucine, valine, or histidine can rapidly reduce adiposity in diet-induced obese mice, restoring normal body composition and enhancing insulin sensitivity. Further, we have shown that diets with lower levels of isoleucine or histidine are associated with reduced body mass index in humans. Here, we will determine the degree of restriction of these three amino acids that optimally promotes metabolic health in diet-induced obese mice and test an optimized amino acid restricted diet for the ability to reduce adiposity and restore glycemic control in a non-human primate. We will also gain insight into the molecular mechanisms induced by reduced levels of these dietary amino acids through integrative transcriptomic and metabolomic analyses of blood, adipose, and skeletal muscle samples from nonhuman primates and from young and aged mice. Additionally, we will collect samples for future analysis of the role of the epigenome and microbiome in the response to dietary amino acids. These studies are urgently needed to understand if manipulation of specific dietary amino acids is a translatable intervention to promote metabolic health and increase healthspan.

NSF Awards · FY 2024 · 2024-09

Ancient environmental DNA (aeDNA) is transforming the scientific study of past biodiversity dynamics and the effects of past climate change and intensifying human land use. aeDNA methods permit the detection of past species by recovering ancient fragments of DNA from sediments and matching these fragments to genetic libraries of known species. Because many species preserve poorly and are thus invisible to traditional paleontological approaches, aeDNA is enabling the study of past biodiversity dynamics at an unprecedented combination of taxonomic extent and resolution; in principle the entire tree of life can be studied. aeDNA so far has been at an early stage of research, focusing on methodological refinements and discoveries at individual sites. As the number of aeDNA research teams and records rapidly grows worldwide, the next-stage scientific opportunity is to integrate these many records and thereby study biodiversity responses to past environmental change at regional to global scales. Achieving this global synthesis requires building both the advanced data platforms that can support aeDNA data sharing and the community of experts who will provide and curate this data. This award will enable the next generation of global-scale biodiversity research at unprecedented taxonomic resolution, coverage, and temporal extent, powered by 1) the integration of aeDNA data into a linked open ecosystem of paleoecological and bioinformatic resources and 2) building a closely interlinked social infrastructure that ensures high data quality, social trust, and alignment of informatics development with scientific priorities. To achieve this data integration, the data schema, curatorial systems, and data-sharing systems of the Neotoma Paleoecology Database will be extended to support aeDNA data and metadata. Linking services will be built between Neotoma and standard bioinformatic resources (NCBI/EMBL, GBIF, ORCID), so that aeDNA-based taxonomic inferences are provenanced back to standard authorities and can be regenerated as reference databases improve. To build social infrastructure and a data governance system, the project establishes a Council of aeDNA Stewards and holds annual workshops to advance data governance and metadata norms. Multiple training workshops are held for early career aeDNA researchers and virtual workshops employ a platform-agnostic Docker-based system to minimize barriers to access. Protocols and recommendations will be published in Protocols.io and peer-reviewed journals. The project develops multiple venues for engaging with high-school, college-level, and graduate-level students interested in learning more about how aeDNA can be used to study life’s responses to past environmental change. 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

Per- and polyfluoroalkyl substances (PFAS) are a group of manmade chemicals that are used in many consumer products and industrial processes due to their unique chemical properties. However, their persistence in the environment poses a significant threat to the drinking water supply of roughly one in three people in the U.S. One promising method for PFAS removal from contaminated water is nanofiltration (NF), a technique that removes nanoscale particles from a liquid using membranes as a filter. Yet, several critical challenges must be addressed to make NF a viable part of PFAS cleanup efforts. First, the effectiveness of NF in removing the wide variety of PFAS types, especially (ultra)short-chain PFASs and those found in complex mixtures, remains unknown. Second, a better understanding of how various forms of PFAS interact with NF membranes at a molecular level is needed. Third, the lack of predictive models to identify key factors that affect PFAS passage through NF membranes hinders rational membrane design and selection. This research aims to address these knowledge gaps by combining experiments and computer simulations, integrated with specialized modeling techniques such as machine learning, to investigate how NF removes PFAS from contaminated water resources. The fundamental knowledge gained through this work will advance membrane-based technologies for remediating PFAS-contaminated water. In addition, this project will include public engagement and educational activities such as developing a new educational module, training students from underserved groups, and hosting outreach activities for PreK-12 students to increase PFAS scientific literacy and awareness. The overarching goal of this research is to use an innovative integration of experimental and computational studies to elucidate the performance and mechanisms of (ultra)short-chain PFAS removal by NF. To achieve this goal, the NF removal performance for (ultra)short-chain PFAS of varied structural features will be evaluated, and the structure-property-performance relationship of PFAS removal in NF treatment will be established using machine learning techniques. The investigators will use non-targeted chemical analyses to further assess the NF performance in removing diverse PFAS from complex aqueous film-forming foam-impacted water. The interactions and transport of PFAS at the water-membrane interface and within polyamide NF membranes will be probed theoretically using molecular dynamics simulations to gain mechanistic insights into the experimental results. The findings of this research will generate fundamental knowledge to inform rational design strategies for developing more effective NF membranes tailored to remediating PFAS-contaminated water. 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

Proportionally, Native Americans earn fewer undergraduate and advanced degrees in science and engineering than any American minoritized group, and they have the fewest doctoral scientists and engineers in the workforce. One contributing factor is the way STEM is typically taught, which can create a disconnect between home and school cultures, and a clash between identities and worldviews. This project proposes to build on a longstanding collaboration between Tribal and university partners in Wisconsin. The project addresses a need identified by Tribal partners: improving pathways for Native learners to enter Science, Technology, Engineering and Mathematics (STEM) fields and careers to ultimately support the protection of land and water, as well as sustain Indigenous culture, in their regional communities. The partnership will address this disconnect by developing an intergenerational land-based learning plan that will combine Indigenous culture with STEM knowledge and land and water conservation activity. This plan will have components that engage (1) youth ages 12-18 in year-long experiential STEM learning experiences, (2) adult relatives in reclaiming connections to their culture and building an understanding of how their culture intersects with western STEM, and (3) families in seasonal shared learning experiences, like maple sugar making, wild rice harvesting, ice fishing, and foraging. The research will address the questions (1) What is a model of Tribal-university partnership for community land-based learning, that connects TEK and STEM for land stewardship? (2) How do Indigenous language revitalization and cultural revitalization (and the stewardship instructions that they contain) inform land-based learning? (3) How does Indigenous STEM learning engage communities in land and water stewardship, and what are the roles of Tribal members and university allies in supporting this process? (4) What is the readiness, experience, and capacity of Tribal and university project partners to design and implement Tribally-Driven Participatory Research (TDPR) effectively, and what research methods and culturally specific methods are being developed via TDPR? The Intellectual Merit of this project lies in its use of Tribally-Driven Participatory Research to make contributions to creating and studying a culturally-sustaining STEM pedagogy that honors and upholds Tribal sovereignty across a number of project elements (e.g., building capacity, data sovereignty, and centering Indigenous leadership). This work will add to the body of literature on how to integrate "Western" science and Indigenous perspectives in informal STEM learning experiences, with a goal of both conveying STEM content and upholding and affirming local Traditional Ecological Knowledge (TEK). The Broader Impacts of this project lie in its potential to encourage Native youth to pursue STEM careers that can benefit the resource and environmental management of their own communities, the preparation it gives to adults and families for supporting their youth in pursuing STEM education and careers, the community-based activities that encourage Tribal partners to conserve and restore natural environments in alignment with Tribal priorities, and the way the project can serve as a model for how to establish or repair relationships between universities and Tribes who occupy the same regions. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences. 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 · 2024-09

Project Summary This proposal requests funding to purchase a Waters Acquity UltraPerformance Convergence Chromatography (UPC2) system equipped with photodiode array and single-quadruple mass spectrometer detectors. The instrument will be used to characterize the numerous novel molecules synthesized at the University of Wisconsin-Madison, and will be located in the Department of Chemistry Synthesis and Catalysis Center. Projects that will be supported by this instrument are addressing fundamental biological questions and challenges in drug synthesis. The Yoon group is developing new general strategies for the controlled photochemical synthesis of complex organic molecules and small-molecule therapeutics. The Stahl group develops new oxidation and oxidative coupling reactions that form carbon-carbon and carbon-heteroatom bonds with broad impact in medicinal chemistry and drug discovery. Research in the Wickens group combines electro- and photochemical methods to selectively transform simple and inexpensive molecules into compounds of medicinal importance, facilitating the development and large-scale preparation of new pharmaceuticals while simultaneously reducing the cost of life-changing medicines. The Schomaker group uses mechanistic insight to tune the reactivities of unusual intermediates to achieve mild, versatile syntheses of stereochemically rich, densely functionalized N-hetero- and carbocycles, including those in bioactive molecules that bind to specific ribosomal subunits. The synthesis of macrocyclic compounds displaying stable axial chirality that play important roles in biology and medicine is the focus of research in the Gellman lab, where new bifunctional foldamer catalysts are being developed to exploit non-traditional peptide scaffolds that orient reactive groups to achieve high stereoinduction during macrocyclization. The Martell group's growing research program is developing hybrid biological-synthetic catalysts consisting of three-dimensional, self-assembled DNA and protein scaffolds as catalysts for stereo- and regio-selective transformations, coupling reactions, and the tagging of endogenous proteins to locate the specific subcellular regions where they reside. And, the Blackwell group is addressing the urgent global need for new antimicrobial therapies with an integrated research program at the interface of chemistry and biology focused on chemical signaling pathways that allow bacteria to act as a group at high cell densities and activate behaviors that significantly impact human health, including the initiation of deadly infections. The ultimate goal of all these projects is to find ways to improve human health by discovering new therapies and drugs.

NIH Research Projects · FY 2025 · 2024-09

Idiopathic pulmonary fibrosis (IPF) is a rapidly progressing disease characterized by relentless extracellular matrix (ECM) deposition and lung stiffening that leads to death 3-4 years after diagnosis. IPF disease monitoring largely relies on high-resolution computed tomography imaging and pulmonary function tests, which are unable to assess ECM deposition or real-time disease activity. Since there is currently no method to evaluate ECM deposition and real-time disease activity, and given the importance of determining disease progression in therapeutic decision-making, there is a critical need for improved methods to assess IPF disease activity in real- time. Dr. Bernau's long-term goal is to establish an independent research career dedicated to developing new molecular imaging methods that facilitate diagnosis and treatment of disorders characterized by aberrant wound healing, especially IPF. Molecular probes optimized for positron emission tomography (PET) imaging enable sensitive assessment of target engagement, making this an attractive modality for non-invasive monitoring of disease activity. In this proposal, Dr. Bernau will leverage her expertise in matrix biology of IPF, small animal imaging with training in PET imaging, probe development, pharmacology, and additional animal models of lung fibrosis to develop a novel PET probe optimized to determine the activity and treatment response of human IPF. Fibronectin (FN) is an abundant glycoprotein that is highly upregulated during IPF, serves as a scaffold for other ECM proteins, including collagens, and is localized to fibroblastic foci, the leading edge of active fibrosis. Due to its essential role in early phases of the fibrotic process, the rationale for this proposal is that identifying regions of nascent FN deposition can serve as a tool for distinguishing active fibrosis and assessing disease progression. Dr. Bernau and her team developed a novel probe (PEG-FUD) that is innovative in its capacity to target early ECM deposition in fibroblastic foci in human IPF and early pro-fibrotic phases of bleomycin-induced pulmonary fibrosis in mice (via in vivo PET imaging). The objective of this proposal is to address the probe's key characteristics for detection of fibrotic disease activity and adapt it for downstream clinical translation, including optimization of its signal to background ratio within 1 h post-injection. To accomplish this, Dr. Bernau Aims to: 1) Optimize FUD's imaging performance while preserving FN binding affinity in vitro and in vivo, and 2) Determine how 64Cu-PEG-FUD probe can monitor disease progression and response to antifibrotic therapies. Dr. Bernau is supported by the rich research infrastructure and resources of her Mentorship and Advisory Committee, the Department of Medicine, and the University of Wisconsin-Madison. These studies will enable Dr. Bernau's future work focused on subsequent pre-clinical developments of PEG-FUD as a probe for active lung fibrosis. Importantly, this project and training period will serve as a foundation for an independent research career focused on molecular probe development for IPF and other disorders characterized by aberrant wound healing, informing the choice of optimal treatment and improving morbidity and mortality for these devastating conditions.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Myocardial Infarction (MI) claims nearly one million lives in the US per year, a number expected to increase by 30% by 2060. Survivors are at increased risk for stroke, heart failure, and recurrent MI. Neutrophils play a critical, yet dual role in regulating cardiac recovery following MI. Depending on their inflammatory state, these innate immune cells can both initiate a healing response or drive further tissue damage. A current gap in understanding how neutrophils integrate chemical and mechanical signals to regulate this inflammatory state has resulted in failure of existing neutrophil-targeted therapies to treat MI. Recent innovations from the Beebe and Huttenlocher labs enable interrogating the role of mechanical and chemical cues on regulating neutrophil inflammatory state. The Beebe lab has introduced a system of liquid- walled microfluidic channels that model the dynamic mechanical environment over the course of MI. The Huttenlocher lab’s introduction of genetically tractable human iPSC-derived neutrophils enables dissection of the signaling mechanisms that regulate response to varying mechanical cues. This work has identified a key signal, myeloid-derived growth factor (MYDGF), a leukocyte-secreted factor that mediates cardiac repair following MI in mice, to also regulate neutrophil response to mechanical cues. This fellowship proposal will further utilize these technologies to investigate the mechanisms behind MYDGF- mediated cardiac healing. We hypothesize that MYDGF paracrine signaling directly counteracts mechanical activation through competition in HIF-1α signaling and regulates cardiomyocyte regeneration within a zebrafish cardiac wound model. Aim 1 will use our organotypic system to first characterize the role of mechanical stress on activation state of primary neutrophils. Within this system we will then use primary and human iPSC-derived neutrophils to test they hypothesis that MYDGF paracrine signaling counteracts mechanical activation through competition in HIF-1α pathway signaling. Within Aim 2, we will employ a zebrafish cardiac injury and regeneration model to investigate the role of MYDGF in regulating cardiac repair through modulation of neutrophil response. The goal of this work is to identify key signals that regulate neutrophil activation state for future therapeutic targeting to treat human MI. The proposal will provide specific training in cell signaling and in vivo imaging. The project will synthesize the engineering expertise of Dr. Beebe and cell biology expertise of Dr. Huttenlocher, with the vast medical, scientific, and translational resources available at the University of Wisconsin – Madison. This pre-doctoral fellowship will drive further development of research, clinical, mentorship, innovation, and communication skills necessary for my career as an independent physician scientist.

NSF Awards · FY 2024 · 2024-09

The project focuses on the study of spectral properties of non-Hermitian families of random matrices, i.e. of particular types of rectangular arrays of randomly chosen numbers. Random matrices naturally appear in probability and statistics, computer science, and many research areas of mathematical physics. Non-Hermitian random matrices have been utilized in such diverse fields as dissipative and stochastic processes, scattering chaotic systems, and nuclear physics, and have applications ranging from neural networks and computational algorithms to communication theory and ecosystem dynamics. The main aim of the project is to achieve a better understanding of global and local (individual) behavior of complex eigenvalues of different kinds of non-Hermitian random matrices as the size of matrices becomes large. The project will also provide research training opportunities for graduate students. Since E. Wigner's and F. Dyson's works, the local behavior of the eigenvalues of random matrices has been expected to be universal, i.e. independent of the distribution of matrix entries, and, to a large extent, of the structure of random matrices. This project will focus on three important classes of non-Hermitian random matrices with two-dimensional spectral distribution: anti-Hermitian deformations of Hermitian matrices, which play an important role in the scattering theory; fully non-Hermitian matrices with independent entries with or without deformation; and sparse non-Hermitian random matrices. Universal spectral properties of these non-Hermitian random matrices will be studied by extending and combining classical random matrix techniques with a new approach based on a rigorous application of supersymmetry techniques for classical (symmetric) random matrices with real eigenvalues. 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

Large, sensitive laser systems are common in research laboratories, but they are traditionally owned and operated by a single investigator and used to support 1 or 2 experiments. In this project, the investigators plan to propel a new regime in the sharing of large, alignment-sensitive instrumentation, including lasers and other equipment. They will acquire, assemble, and ruggedize a state-of-the-art laser system with truly unique capabilities. First, few other systems worldwide will rival its spectral coverage (92% of the 210–4300 nm wave length range), enabling the system to serve comprehensive research spanning fundamental understanding and practical applications in a wide range of disciplines. Second, it will be assembled onto a custom-built wheeled optical table, allowing for widespread sharing across laboratories and buildings: the laser will be transportable to any lab at UW-Madison. The design, integration approach, and test results of the transportable laser system will be disseminated broadly, so that the approach can be adopted by many others. The investigators believe this project will help spark a revolution: institutions worldwide are expected to copy the approach and thereby increase equipment access dramatically. Importantly, many of the users who will gain access to the shared equipment in the envisioned paradigm will be researchers who are disadvantaged in some way (e.g., working on a topic or in a region where funding is lower), thereby promoting a more equitable and inclusive research ecosystem. Furthermore, from the perspective of the national and global research enterprise, fewer pieces of equipment will be needed to satisfy research efforts, ultimately enabling sponsors to fund a more diverse equipment pool to accelerate scientific discovery. The laser will enliven the pursuit of transformative research topics among over 20 faculty at UW-Madison and at least 4 local companies in topics including quantum computing, quantum spectroscopy, biological sensing/imaging, optoelectronic device development, advanced manufacturing, and energy solutions. The ability of the system to produce 2 independent laser outputs will be particularly enabling for many emerging research topics. Beyond advancing academic and industrial research, the proposed work will generate new knowledge and techniques in the assembly, ruggedization, and testing of alignment-sensitive optical equipment. Advanced vibration management strategies will ensure minimal optical misalignment during transport. The laser will also be used for outreach. For example, the investigators will provide educators with access to the laser to enhance their classroom experience by incorporating it in activities in a graduate-level Biophotonics Lab course and the TeachQuantum program for high school teachers. 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

This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the SBE Directorate. It has both scientific and societal benefits in addition to integrating research and education. PhDs in psychology and neuroscience are awarded to individuals from underrepresented groups in proportions far lower than is their representation in the overall population. The Psychology Research Experience Program (PREP) at the University of Wisconsin–Madison (UW) seeks to address this problem by adopting a core motivating premise of the REU mechanism, which is that Research Experience is one of the most effective avenues for attracting students to and retaining them in science and engineering, and for preparing them for careers in these fields. Indeed, 79% of PREP’s 99 alumni from the years 2011-2023 are currently working as Professors or postdoctoral researchers, matriculating in a graduate program in a STEM discipline, engaged in postbac research activities in preparation for applying to graduate school, finishing up their undergraduate studies in a STEM field of study, or applying STEM-related skills in a nonacademic job. PREP is held for 10 weeks each the summer, during which students engage in immersive mentored research coupled with hands-on classroom training in data science, and professional development. Each PREP student carries out their research project in a laboratory in, or affiliated with, the Department of Psychology. They receive mentorship from the Principal Investigator of their lab, and day-to-day supervision from a graduate student or postdoctoral fellow in that lab. Weekly data science workshops are led by faculty and trainees of the UW’s NSF Research Traineeship (NRT) program that focuses on learning, understanding, cognition, intelligence, and data-science (LUCID). Weekly professional development sessions are led by a rotating set of faculty from the Department of Psychology. PREP’s focus is on integrating principles and methods of data science into the study of psychology and neuroscience. This was developed as a result of polling faculty at the UW, "What attributes do you seek when you are recruiting graduate students for your own lab?" A response that was common across every faculty member polled was prior experience with technical skills. The specifics varied from lab to lab, but facility with programming, such as in MATLAB, python, and/or the R environment, was the most frequently listed desideratum. More broadly, a trend in almost all domains of psychology and neuroscience is an increased emphasis on data science. In some domains it’s data mining and meta analysis. In some, it’s large-scale data collection, such as internet-based studies with Amazon’s Mechanical Turk. In neuroscience, it’s the fact that individual neural data sets can, in-and-of themselves, constitute a kind of big data to which methods from machine learning and other branches of engineering are increasingly being applied. The program’s mentoring organizational structure pairs each PREP student with three mentors: a faculty mentor, who is a tenured/tenure-track/or PI-status faculty member at the Departments of Psychology or Psychiatry or one of LUCID’s other core departments (Computer Science, Electrical and Computer Engineering, Educational Psychology); a research mentor, who is the graduate student or postdoctoral fellow who is responsible for day-to-day research mentoring of the PREP student; and an alumni mentor, an alumna/us of PREP. 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

Biomembranes play a critical role in the structure and organization of biological cells. Various biomembrane bound phenomena are central to the functioning of cells and play a key role in many disease conditions. Thus, an understanding of the physical interactions active on the surface of biomembranes is important to develop a deeper appreciation of cell functioning, and their implications on human health and disease conditions. This award supports research that will focus on the mechanical deformation aspects of biomembranes. The work aims to develop a novel theoretical formulation that models membrane deformation and its interactions with transport and electrochemical processes. The formulation will then be used to model various membrane bound phenomena. This research brings together several disciplines including mathematics, mechanics, chemistry and electrostatics. Since this project addresses important aspects of the mechanical evolution of cells, the research and learning obtained is of significant interest to researchers in biophysics and mechanics. Further, the formulation will be implemented into a computational framework. This computational framework will be made available as an open-source code to the scientific community at large. Theoretical and computational modeling of complex biomembrane mechanisms requires an integrated multiphysics approach to treat the coupled phenomena of mechanics, surface transport and electrochemistry. This project will develop a novel free-energy description of biomembrane mechanics - coupling viscoelasticity, surface transport of lipids, proteins and ions, and electrochemistry. This description will be implemented into a thin-shell mechanics framework to model mechano-chemical evolution of biomembranes. The mechanics treatment will be finite-strain visco-elasto-dynamics, and the numerical implementation will be a curvilinear-coordinates based coupled multiphysics framework. The numerical model will be used to model three important biomembrane phenomena: endocytosis, mechanoporation and strain-modulated neuronal electrical conduction. The model will be validated using experimental observations of neuronal membrane mechanoporation and electrical conduction. The distinguishing aspect of this work is the strong integration of multiphysics (membrane mechanics, surface transport and surface electrochemistry) to enable a unified treatment of various coupled phenomena in biomembranes. 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

Massive hot stars are the greatest sources of energy and new material in the Galaxy. A collaboration of astronomers at the Monterey Institute for Research in Astronomy (MIRA), Florida Gulf Coast University, The SETI Institute, the University of Maryland Baltimore County, and the University of Wisconsin Madison, along with their international partners aim to determine the interior structures of these stars by the application of a new technique: polarimetric asteroseismology. Seismic waves bounce around the interiors of stars disturbing their surfaces as if in a perpetual star-quake. The collaboration will make observations of this phenomenon in key stars in tandem with ground- and space-based telescopes (including the NASA TESS mission). A new network of the World's most sensitive polarimeters, spanning a third of the Earth will be used to detect the surface oscillations caused by these seismic waves. The team will also build on established code to develop sophisticated new computer models to interpret the multi-faceted data. College undergraduate and high school students, including some from traditionally under-represented groups, will assist with the project and gain their first hands-on experience of observational astronomy and modeling. Citizen scientists will also be involved, and the project will form part of MIRA's public education programs. A very extensive data set will allow the team to determine the interior structures of about 10 beta Cephei and Slowly Pulsating B-type stars in various stages of evolution. This will be enabled by a large-scale coordinated high-precision polarimetric observing campaign. To achieve the needed phase coverage, it will involve multiple observatories, all equipped with state-of-the-art PICSARR polarimeters. To obtain the necessary S/N and frequency resolution (which depends on temporal baseline) will require many thousands of new polarimetric observations spanning more than 2 years, matched to corresponding photometry and spectroscopy – including new and archival data. The observations will be followed by an intensive multi-part analysis involving sophisticated radiative transfer modeling. Integral to the work is the creation of a new software program that combines pulsating star and polarized radiative transfer codes to properly account for the significant effects of rotation. This program will make mode identification feasible using polarimetry in rapidly rotating stars. The results will enable stellar evolution models to be properly calibrated and extrapolated to the supernova stage. 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

Rapid population growth and rising living standards have caused the depletion of global water sources and other valuable resources. Separation technologies that can purify saline or contaminated water and recover valuable components are urgently needed. Separation using nanofiltration (NF) has been widely used for water purification and desalination, but emerging challenges such as the extraction of lithium from brines to satisfy the booming lithium-ion battery market go beyond the capabilities of current NF membrane technology. Thus, the overarching goal of this collaborative project is to explore a novel mechanism known as ion dehydration that can be applied using modified NF membranes to carry out challenging separations. This project leverages expertise in computational simulation, laboratory experimentation, and NF membrane fabrication in an international collaboration with researchers at the Technion - Israel Institute of Technology. Successful completion of this project will advance our understanding of NF separation to address pressing societal needs. Beyond the technical focus, the project will benefit society by educating the public through outreach activities to increase scientific literacy and awareness of water and resource sustainability. NF membranes have been used for water purification and desalination processes for many years. Recently, there has been increasing demand for high membrane selectivity between solutes to enable energy-efficient separation for water purification and resource recovery. However, achieving precise separation between similarly sized and charged ions using current polyamide NF membranes remains a significant challenge. Addressing this challenge requires leveraging mechanisms beyond those prevailing in current water-solute separation. Accordingly, the project will pursue three primary thrusts to regulate the transport and selectivity of monovalent ions in polyamide NF membranes: 1) investigate the role of ion dehydration on the transport and selectivity of ions in state-of-the-art NF membranes; 2) delineate the effect of membrane surface hydrophobicity and charge on ion dehydration using self-fabricated membranes with tunable surface properties; and 3) use molecular dynamics (MD) simulations to support the results of ion dehydration and membrane selectivity experiments. Thrust 1 will utilize a custom-made diffusion cell to probe the ion-ion selectivity for a series of ions with distinct hydration properties at different temperatures, pressures, and solvent types. Thrust 2 will focus on fabrication of thin-film composite polyamide (TFC-PA) NF membranes with systematically altered surface hydrophobicity and surface charge. Thrust 3 will apply MD simulations of water and solute ion transport through a TFC-PA NF membrane, which can be used to understand the relationship between the membrane structure and ion transport/rejection. Beyond the direct technical thrusts, the project will include outreach and educational activities that broaden its impacts by providing research training opportunities to graduate and undergraduate students, especially those from underrepresented groups. The team will also perform outreach activities for K-12 students from local communities of Colorado and Wisconsin to increase scientific literacy and support the Nation’s STEM workforce. 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

Applications of artificial intelligence (AI) are exploding and the demand for trustworthy autonomous systems has never been higher. Before an autonomous system is widely deployed and its decisions have real-life consequences, it is critical to be able to evaluate the expected outcome of letting the system make those decisions. Evaluation is particularly vital when stakes are high, for example, when using AI to control expensive robots in changing environments or drive cars at high speeds. Testing such a system's decisions in real environments will allow measure the extent to which extent the machine's decisions are good. Unfortunately, many real-world applications, such as autonomous driving, have far too much variety that could be accounted ahead of time. Furthermore, the most critical situations may be relatively rare, further decreasing testing efficiency and leading to the deployment of autonomous systems that can make poor decisions in critical situations. To address this problem, this project develops new methods for active testing of autonomous decision-making. In contrast to passive testing by simply running the autonomous system, these methods actively seek out the most informative situations to test under. In doing so, they produce a higher confidence evaluation of a given autonomous system's decision-making. Consequently, the methods strengthen society’s confidence in autonomous systems since such systems can be tested and then only deployed if practitioners are confident they make good decisions. More specifically, this project focuses on evaluating autonomous systems that follow policies produced by reinforcement learning (RL) algorithms. The project develops fundamental theory and practical, domain-agnostic methods for active testing of RL-trained policies. These active-testing methods identify consequential data for policy evaluation and then focus test data collection on such data. Specifically, this project consists of three research thrusts. The first thrust derives and implements the optimal policy to follow when evaluating a given policy. The second thrust develops novel adaptive sampling algorithms that reduce inefficiency due to random sampling when collecting data for policy evaluation. Finally, the third thrust develops a methodology for adaptively setting a policy's initial state so as to obtain the most accurate evaluation of that policy in as few trials as possible. The novel methods produced bring new understanding to the study of policy evaluation in RL and the broader AI field. In particular, little research has gone into the fundamental question of how data collection affects the quality of policy evaluation. This project brings new understanding to this question and, in doing so, develops novel adaptive data collection methods that enable effective policy evaluation in realistic RL domains. This project advances foundational RL knowledge on tailoring data collection for accurate policy evaluation by introducing domain-agnostic and theoretically-based methods for adaptive data collection. 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/Abstract Chronic health conditions—diseases that require ongoing medical attention—affect 1 in 5 people of reproductive age in the United States.1 Although these conditions are common, little is known about the interplay of chronic disease and fertility, defined as childbearing goals, timing, and achievement. It is likely that chronic health conditions have implications for fertility,4–7 as chronic diseases may affect the ability to healthily carry a pregnancy to term, to survive childbirth, and to parent with health restrictions and lifespan limitations.8– 10 Prior research has found that women with poor self-reported health and disabilities have lower fertility goals; 4–7 however, it is unclear how chronic health conditions that have implications for gestational health and increase the risk of premature disability and mortality affect fertility goals, timing, and achievement. Due to the likelihood that different diseases affect fertility in distinct ways, the proposed research examines the fertility implications of three serious health conditions: unipolar depression, cardiovascular diseases, and diabetes. This project uses mixed methods data, including two nationally representative datasets, 1) the National Longitudinal Survey of Youth 1997 and 2) the National Longitudinal Study of Adolescent to Adult Health, as well as semi-structured interview data collected by the investigator. This project addresses three aims. In Aim 1, the impact of three chronic health conditions on women’s fertility will be estimated. Using longitudinal data, I will estimate the fertility goals, timing, and achievement of women with and without any of the three health conditions. Sequence analysis and ordinary-least squares regression will be used to estimate the impact of chronic health conditions on these fertility processes. As fertility patterns may differ across health conditions, I will also investigate women’s fertility patterns across the three different chronic diseases. Finally, I will test for differences by race. In Aim 2, qualitative data collected by the investigator will be used to describe how women with the three chronic health conditions make decisions about fertility. Interviews will explore the importance of timing of chronic disease diagnosis, how chronic disease affects decisions about when, whether, and how to get pregnant, and mechanisms through which chronic conditions impact fertility. Data from the quantitative and qualitative phases will be combined using an explanatory integrative approach. In Aim 3, the investigator proposes a tailored training plan to develop the necessary knowledge, methodological skills, and professional competencies to implement the proposed research. This includes training in longitudinal and hierarchical data analysis, as well as integrative mixed methods data analysis. A strong team of senior scientists, with expertise in social demography, fertility, longitudinal data analysis, mixed methods, obstetrics and gynecology, and clinical management of patients with complex pregnancies will mentor the applicant. This study will enable understanding of the impacts of simultaneously managing fertility and chronic health conditions, which has important implications for clinicians, demographers, reproductive justice advocates, and policy makers.

NIH Research Projects · FY 2025 · 2024-08

Discovery of Human HLA T Cell Epitopes in Human Metapneumovirus HMPV is a leading cause of lower respiratory infection in children and adults worldwide. Although nearly all people are infected with HMPV by age 5 years, immunity to HMPV is incomplete and re-infections occur throughout life. More severe disease occurs in persons with underlying conditions such as asthma, chronic obstructive pulmonary disease, HIV, or prematurity. Data from our group and others shows that T cells are important to clear infection in mice and humans, yet T cell response can also contribute to disease severity. Thus, characterizing the human T cell response to HMPV is important to guide safe and effective vaccine development. Our lab has established methods to discover human MHC-I and MHC-II epitopes using HLA-transgenic (huHLA-tg) mice and human PBMCs. The huHLA-tg mice express different HLA supertypes, which collectively capture a large proportion of different HLA alleles among diverse human populations. We propose to apply these methods to discover HLA-restricted HMPV epitopes recognized by CD8+ and CD4+ T cells. We will screen both overlapping peptide pools and predictope peptides by ELISPOT against T cells derived from huHLA-tg mice infected with HMPV. Individual epitope peptides that are confirmed by ELISpot will be ordered as tetramers from the NIH Tetramer Core. Tetramers will be used to thoroughly analyze and characterize HMPV epitope-specific CD8+ T cells, including: effector molecule production such as IFN-γ, TNF, IL-2, perforin, and granzyme; degranulation using CD107a; transcription factor profile; and inhibitory receptor expression. Dominant epitopes confirmed by tetramer staining will be tested for protective efficacy using peptide vaccination and live virus challenge of mice. HLA-typed human PBMCs will be used to confirm bone fide recognition of the epitopes by human CD8+ T cells. The results of this project will identify widely shared HMPV epitopes that can be used to design candidate vaccines and evaluate the response to any HMPV vaccine. These tools will be needed to understand human immune responses to both natural infection and vaccines, which is important given the immune-mediated pathology associated with HMPV.

NIH Research Projects · FY 2025 · 2024-08

Although it is recognized that systemic maternal immune responses influence the fetus, how local decidual and placental inflammation, in the absence of an infectious agent, impacts the developing fetal immune system is not understood. Developing a deeper understanding of decidual/placental inflammation will provide opportunities for the development of targeted therapeutics either in utero or once offspring are born. Thus, the long-term goals of this project are to understand how the in utero environment impacts the developing fetal immune system, and to assess the long-term immunological consequences. The central hypothesis is that sterile inflammation at the maternal-fetal interface (MFI) leads to skewing of the fetal immune system towards a pro- inflammatory phenotype. To understand how sterile inflammation affects the fetal immune system, Tisseel, a binary product where fibrinogen and thrombin are co-delivered to create rapid clotting, will be injected to the MFI of pregnant rhesus macaques (Macaca mulatta), resulting in clotting and placental infarct development, leading to necrosis and inflammation. Specific Aim 1: To test the hypothesis that Tisseel injection into the MFI leads to sterile inflammation of the decidua and placenta. Tisseel or sterile saline will be injected into the MFI in the late second trimester/early third trimester (~GD90) of pregnant rhesus macaques. At ~GD155, the placenta and fetus will be obtained and weighed, and the fetus will be euthanized. MFI tissues will be processed for histopathology, molecular and immunological analyses, including spectral flow cytometry, scRNA-seq and Spatial Transcriptomics. Specific Aim 2: To test the hypothesis that Tisseel injection into the MFI leads to fetal immune system programming towards a proinflammatory phenotype. At experimental delivery, the fetus will be euthanized, fetal tissues will be collected and mononuclear cell (MC) suspensions prepared. MCs will be analyzed for gene expression (scRNA-seq) and chromatin accessibility (scATAC-seq). Overall, the proposed project will address how sterile inflammation at the MFI impacts the developing immune system. Because the fetal immune system is pliable, it makes this developmental stage an attractive opportunity for targeted therapeutics that could prevent long-term health consequences. Completion of this K01 proposal will also allow me to gain training in the development and use of experimental NHP models of pregnancy complications, skills in advanced data analysis, and understanding of the fetal immune system. This will facilitate my transition into an independent investigator in maternal-fetal health.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of mortality in patients with lupus nephritis (LN). Moreover, LN patients of Black race face even higher ASCVD risk. Less than 15% of eligible LN patients receive ASCVD prevention. This is because clinicians and patients need to know: a) who is at risk and will benefit most from ASCVD prevention; b) how to start and tailor therapies per each patient’s risk; and c) what is the efficacy and safety of ASCVD therapies during pregnancy—fears of young women that lead to stopping such therapies. This proposal is designed to provide Dr. Shivani Garg, MD, MS with the training needed to become an independent physician scientist researching interventions to improve ASCVD prevention in LN. The goal of the proposed research is to develop effective ASCVD preventive interventions that support clinicians and patients in decision-making and support use of ASCVD-risk reducing therapies to improve outcomes and survival in LN. Garg’s earlier innovative work identified that subclinical renal arteriosclerosis in kidneys at LN diagnosis is a window into the heart. This approach can identify patients who will benefit from ASCVD prevention at LN diagnosis. To answer the two remaining questions/concerns of patients and clinicians, Garg’s project will: a) Develop an implementation guide for clinicians to support decisions to 1) tailor ASCVD prevention by each patient’s risk; and 2) start ASCVD-risk reducing therapies based on evidence for effectiveness and safety (Aim 1). Using a validated method that combines scientific evidence from literature and expert consensus this guide will be developed. A retrospective performance testing will be done to test its public health impact. b) Create a shared decision-making tool to support patients and clinicians in decision-making and identifying the best aligned approach to their personalized ASCVD preventive care (Aim 2). This tool will be informed by feedback from multidisciplinary experts, clinicians, and patients of diverse backgrounds to ensure biases and difficult concepts are addressed thereby delivering a racially, culturally, and socially appropriate tool. c) Test the guide and shared decision-making tool in clinics to evaluate if patients and clinicians will adopt, use, and recommend the tools (Aim 3). Additionally, this step will inform the real-world impact and feasibility of using such interventions in busy clinics. The data from this project, along with the novel correlations with subclinical renal arteriosclerosis, offer a foundation for a multi-site R01 study to test the effectiveness of ASCVD preventive interventions in reducing the risk of ASCVD in LN. Dr. Garg is in an ideal environment to complete this research and receive mentored training in implementation and decision science, qualitative methods, health equity, and clinical trials. This proposal addresses a significant clinical dilemma and serious gaps in ASCVD prevention research in LN and offers critical support for her growth to lead ASCVD prevention research to improve outcomes and equity in LN.

NIH Research Projects · FY 2025 · 2024-08

Project Summary Clinician-delivered relapse prevention interventions for alcohol use disorder (AUD) are effective when delivered but the vast majority of adults with an active AUD do not receive them due to well-known barriers to clinician-delivered treatment. Digital therapeutics (smartphone “apps” that are used to prevent, treat, or manage a medical or psychiatric disorder) can address these barriers. Unfortunately, the benefits from digital therapeutics may be constrained because engagement with them is often not sustained or matched to patients’ needs. The next wave of “smart” digital therapeutics that include embedded machine learning lapse prediction models powered by personal sensing can address these constraints by guiding patients to sustain engagement with the specific interventions and supports that are most personally risk-relevant and therefore most effective. Personal sensing has been possible within digital therapeutics for AUD for several or more years. Machine learning lapse prediction models are emerging now, and the models developed by our team meet or exceed performance thresholds necessary for useful clinical applications. We are well-positioned to develop a smart version of our Center’s A-CHESS digital therapeutic by embedding our lapse prediction model into an existing version of our digital therapeutic that already has sensing capabilities. However, we must first determine how best to provide model feedback to patients so that they use this information and follow its recommendations. In this application, we propose to optimize feedback from our lapse prediction model (via daily engagement messages) both to increase risk-relevant engagement with Smart A-CHESS and to improve clinical outcomes over six months among 416 participants with moderate to severe AUD. Following the Multiphase Optimization Strategy, we factorially manipulate four candidate components of these daily engagement messages that convey transparent, individualized, risk-relevant information from our machine learning lapse prediction model to participants. These message components include: 1) lapse probability, 2) lapse probability change, 3) important model features, and 4) a risk-relevant module recommendation. These components use output that would be available from any machine learning lapse prediction model such that conclusions about the impact of these components on engagement can generalize beyond our specific machine learning model. Similarly, engagement messages including these message components could be used in any smart digital therapeutic for AUD, allowing conclusions to generalize to current and future variants of smart digital therapeutics for AUD. At the conclusion of the grant period, we will also deliver this optimized smart digital therapeutic as a tangible product and model for how to embed sensing and machine learning into other existing digital therapeutics.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY/ABSTRACT Trans and nonbinary (TNB) individuals experience mental and physical health disparities compared to cisgender peers, such as higher rates of depression and anxiety, increased risk for suicide, substance use and poor physical health. These disparities are associated with internalized anti-TNB stigma, discriminatory interpersonal encounters, and systemic and institutional barriers to accessing necessary social, financial, educational, and health resources, and are exacerbated among TNB People of Color (POC) and those with fewer resources. Research across populations indicates that social support has multiple beneficial effects, including mitigating adverse mental health outcomes and promoting well-being. There is a critical need to identify the most effective types of social support for all TNB individuals, particularly those facing the greatest barriers to societal institutions and resources. Using community-engaged and innovative approaches to rigorously over-sample TNB POC, we will identify within-group differences in the specific forms social support that promote health and mitigate risk across TNB communities. Data from a total of 1299 TNB individuals diverse in race and ethnicity will be collected across several studies using multiple methods (e.g., qualitative interviews and focus groups; ecological momentary assessment, longitudinal survey research) to develop and validate a multi-faceted social support measure and test its function within an intersectional minority stress theory-derived longitudinal model. The overall objective of this proposed research is to take the critical step toward this long-term goal by advancing knowledge of social support as a mechanism by which to reduce anxiety, depression, and suicide risk, promote well-being among TNB individuals, and to develop the measurement tools necessary to investigate their impact on mental health and well-being over time and in context. This project includes the following three specific aims: (1) Use community engaged approaches, we will collect data to increase in-depth understanding of how TNB individuals experience and use social support to address adverse mental health concerns, (2) Develop and validate a multifaceted measure of social support for TNB individuals for use in research and clinical settings, and (3) Longitudinally assess the effects of social support in a minority stress model of TNB mental health. Data ( focus groups, individual interviews with community leaders, and daily diary social contacts logs) will be collected to inform essential domains of social support for TNB people [Aim 1]. This information will be integrated into the development of a multifaceted social support measure and cutting-edge network-based psychometric analyses will be used to validate the measure with a sample of 1000 TNB participants [Aim 2]. Longitudinal survey data from 1000 TNB adults at three time points to test the strength of direct and moderating effects of various dimensions of social support on mental health (e.g., depression, anxiety, suicidality) and mitigating mental health risk associated with anti-TNB stigma [Aim 3].

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.