University Of Texas At Austin

NSF Awards · FY 2025 · 2025-09

This I-Corps project investigates the commercial potential of a modular energy conversion system that harnesses ambient environmental forces to generate electrical energy. The innovation leverages programmable architected materials that respond to geothermal gradients, wave motion, and structural vibrations, enabling energy harvesting in locations where conventional systems, such as solar or fuel-based generators, are impractical. The system is designed for deployment in remote or disaster-prone areas, such as coastal communities, off-grid sites, or buried utility corridors. Unlike traditional infrastructure, this technology operates independent of the electrical grid and requires minimal maintenance. The market need for such solutions is growing rapidly due to increasing threats from extreme weather, rising demand for resilient off-grid systems, and efforts to expand energy sources. By harnessing compact and scalable energy sources from underutilized natural forces, this technology enhances national health and welfare through improved energy access, enhanced emergency preparedness, and more adaptable infrastructure. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of architected materials that convert environmental stimuli into mechanical energy through controlled deformation. Fabricated using additive manufacturing, these materials feature programmable, instability-driven transitions that enable them to absorb, store, and release energy in response to dynamic environmental inputs. A physics-informed design framework enables the tuning of mechanical response to match specific input conditions, such as frequency, amplitude, and direction. To enhance portability and ease of deployment, an origami- inspired folding strategy enables compact transport and rapid activation in the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Disengagement from mathematics during middle and high school is a widespread concern that contributes to lower academic achievement and diminished long-term participation in STEM fields. Research shows that students' beliefs about their ability to grow and improve--often referred to as growth mindsets--can significantly enhance motivation, persistence, and performance. However, classroom environments and teacher practices play a critical role in shaping these beliefs. This project evaluates a professional development program, Fellowship Using the Science of Engagement (FUSE), designed to help 6th through 9th grade math teachers adopt instructional practices that foster growth mindset-supportive learning environments. The program provides teachers with research-based insights into adolescent development, structured opportunities to revise their instructional language and feedback practices, and personalized guidance through AI-supported coaching. The study examines whether the FUSE program improves teacher mindsets, communication practices, and well-being, and whether these changes lead to increased student motivation, improved perceptions of classroom climate, and higher performance on state mathematics assessments. To evaluate the effectiveness of the FUSE program, the research team will conduct a large-scale randomized controlled trial across a representative sample of Texas public schools. Schools will be randomly selected and all 6th to 9th grade math teachers within each school will be eligible to participate. Participating teachers will be randomly assigned to either the FUSE intervention group or a placebo control group receiving unrelated professional development content. The FUSE program combines asynchronous instructional modules, live virtual coaching sessions, and AI-driven feedback tools to support teacher learning. Data will be collected throughout the academic year through teacher and student surveys, administrative records, and coded classroom video and audio recordings. Key outcomes will include teacher beliefs, instructional practices, emotional well-being, and retention, as well as student motivation, mindset, perceptions of instructional climate, and performance on standardized mathematics exams. The study will also examine how contextual factors such as school setting, teacher background, and classroom characteristics moderate program impact, with the goal of identifying the conditions under which professional learning interventions are most effective and scalable. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Scientific sensemaking, such as using data to reason and develop explanations about phenomena, is core to learning and doing science. Oral and written language, visual and numerical representations, physical models, and other forms of communication are vital to scientific sensemaking, yet research has not yet fully explored how science curricula can be customized to account for the unique communicative repertoires of individual learners within elementary science classes. This project will address this important gap in practice by developing a suite of tools that elementary teachers can use to customize existing open-source, standards-aligned science curricula, such that these curricula are better able to support students with a range of communicative strengths, including multilingualism. Specifically, the project team will partner with elementary educators to co-develop customization tools to accompany peer-reviewed, open-source science education curricula. These customization tools will serve as the basis for professional learning modules on how to support learners who speak one or more languages, in developing and using their full communicative repertoires while engaging in scientific sensemaking, as guided by the curricula. Subsequent research will explore whether and how the professional learning experiences influence participating teachers' beliefs, preparedness, instructional practice and curricular customizations. The resulting empirically based professional learning modules and customization tools will be disseminated widely to achieve national impacts in science learning for elementary students. In this multisite Design and Development project, researchers and elementary teachers will co-design standards-aligned curricular customization tools that support scientific sensemaking among third-, fourth-, and fifth-grade students with varying communicative strengths. Design-based research will explore how the co-design process constrains or enables new beliefs and instructional practices among 24 elementary teachers. The resulting curricular customization tools may include tools for evaluating existing language opportunities, such as linguistic resources that are currently used for sensemaking; tools for enhancing sensemaking displays, such that they include a broader array of representations; and records of classroom customizations that illustrate how customizations can be enacted across a range of elementary classes. In the second phase of the project, these customization tools will be shared with a larger set of elementary educators in the context of research-aligned professional learning experiences. Mixed methods research will explore whether and how these educators' beliefs, preparedness, instructional practices, and curricular customizations shift as they participate in the professional learning. Specifically, the research team will analyze teacher surveys, video recordings from the professional learning sessions and classroom observations, and artifacts from the educators' curricular customizations. The resulting empirical research will advance knowledge regarding how teacher professional learning, and associated materials, can be designed to better account for the unique compositions of elementary classrooms across the nation. This project is supported by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With support from the Division of Chemistry, Professors Sean Roberts of the University of Texas at Austin and Niels Damrauer of the University of Colorado Boulder, together with collaborators from the University of Sheffield in the United Kingdom, will investigate unique quantum states generated by singlet fission. During singlet fission, an excited molecule shares half of its energy with a neighbor, placing both molecules into an entangled quantum state wherein actions that impact one molecule also impact the other. However, developing materials that produce these states requires an improved understanding of the singlet fission process. To achieve this goal, Professors Roberts, Damrauer and their UK collaborators will develop rigid molecular dimers and oligomers that undergo singlet fission and use ultrafast optical and magnetic resonance spectroscopies, along with open quantum-systems theory, to characterize their ability to generate and sustain quantum mechanical entanglement. Their discoveries could further our fundamental understanding of the role that entanglement plays in excited state phenomena, as well as have implications for quantum-based technologies in sensing and chemical catalysis. The project will also create research opportunities for graduate students in advanced quantum information science, thereby contributing to the development of a quantum-enabled workforce. This award is made under the NSF-UKRI lead agency opportunity. Singlet fission converts a spin-singlet exciton into a pair of spin-triplet excitons in molecular systems. Due to their generation from a single excited species, triplet pairs created by singlet fission are spin-entangled upon their production. This entanglement offers potential to design systems for chemical catalysis and sensing that exhibit unique quantum advantages, where the actions that impact one member of a triplet pair can alter the behavior of its counterpart. To evaluate this potential, the team will develop molecular systems that undergo singlet fission and vet their ability to drive multielectron transfer reactions wherein each exciton within an entangled triplet pair simultaneously acts as a charge donor. These efforts will be guided by a theoretical framework developed for describing the creation of entangled states via singlet fission and their subsequent dynamics. In addition to facilitating gains in our fundamental understanding of singlet fission, this project will bring together physicists working on open quantum systems with synthetic and physical chemists to develop a common language and new research tools that will accelerate the exchange of ideas across these distinct disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract The logopenic variant of primary progressive aphasia (lvPPA) is a disorder characterized by gradual decline in language abilities that is typically caused by underlying Alzheimer’s Disease (AD) pathology. The impact of lvPPA on people’s communication lives is profoundly devastating. It can emerge in adults as young as their 50s, stripping them of their ability to communicate and function and affecting their careers, their family roles, and their potential to enjoy the rewards of mid-to-late life. Although lvPPA and other disorders on the AD and other AD Related Dementias (AD/ADRD) continuum are now identified earlier and with greater precision, research investigating interventions to ameliorate lvPPA’s debilitating effects on communication, prolong language skills, and maximize quality of life lags significantly behind. There is a critical need for rigorous investigations of behavioral and neuromodulatory treatments capable of leveraging spared brain areas for functional improvement, specifically for people with lvPPA, who are underrepresented in treatment research. Failure to meet this need means that people with lvPPA and their families are more likely to face an increased disability burden earlier and for a longer period than is necessary. To date, the field has relied on laboratory-based neuromodulation protocols developed for healthy aging and stroke-induced aphasia populations. This approach has prevented development of intervention models aligned with lvPPA functional needs and neurobiological factors, delaying vertical progress in much-needed nonpharmacological interventions. The overall objective of this project is to enhance the potency of proven language treatment for lvPPA by pairing it with tailored and accessible (home-based) neuromodulatory intervention that targets critical brain networks supporting effective intervention in this population. The central hypothesis is that such an approach will be feasible, acceptable, and show evidence of clinical benefit. Guided by strong preliminary data, two specific aims will be pursued: 1) Evaluate the feasibility, acceptability, and preliminary benefit of remotely-supervised transcranial direct current stimulation (RS-tDCS) and virtual speech-language treatment (vSLT) in persons with lvPPA and 2) Identify neural and electrical field factors that predict responsiveness to RS-tDCS in persons with lvPPA. To accomplish these aims, 80 individuals with lvPPA will undergo comprehensive cognitive-linguistic and neuroimaging assessment and RS-tDCS (active or sham) in conjunction with language telerehabilitation. This project’s contribution will be significant because of its broad application to individuals with other disorders on the AD/ADRD continuum who have historically been underserved by rehabilitation specialists and who would benefit from innovative treatments to prolong independence and quality of life.

NIH Research Projects · FY 2025 · 2025-09

Abstract: The analysis of complex, large-scale datasets is critical for advancing biomedical and behavioral research but is often hindered by limited access to statistical expertise and resources. Artificial intelligence (AI) offers a promising solution by providing tailored, scalable, and accessible guidance to researchers, enabling them to overcome these challenges. This R25 proposal responds to RFA-DA-25-039: Education Activities for Responsible Analyses of Complex, Large-Scale Data. The overarching goal is to develop, evaluate, and disseminate StatWiseAI, an AI-powered educational tool that supports methodological rigor in analyzing complex datasets involving brain, behavioral, genomic, and socioenvironmental data. By leveraging OpenAI’s GPT-4, StatWiseAI will provide tailored, expert-informed guidance to researchers, enhancing their ability to conduct rigorous, reproducible, and ethically grounded analyses. This project has three specific aims. Aim 1: To develop StatWiseAI as an AI-powered educational tool for rigorous data analysis. We will curate domain-specific knowledge, fine-tune GPT-4 using expert-verified case studies, and design interactive features that allow researchers to explore advanced analytical methods relevant to their disciplines. Aim 2: To evaluate StatWiseAI through pilot testing among NIH investigators. The evaluation will focus on usability, content quality, and feasibility, using both subjective measures (e.g., user feedback) and objective measures (e.g., AI-generated response quality compared to ChatGPT). Insights from the evaluation will inform iterative refinements. Aim 3: To disseminate StatWiseAI and assess its long-term impact. We will implement StatWiseAI across diverse NIH research communities, collaborating with NIH training programs and leveraging professional conferences. Regular updates will ensure the tool remains technologically and scientifically current, meeting evolving user needs. StatWiseAI is distinguished from generic AI tools like ChatGPT through its integration of curated resources, expert-verified case studies, systematic prompt training, and periodic expert reviews. It offers a unique educational experience, helping users build AI literacy alongside content expertise. The tool is designed to address common pain points researchers encounter, including integrating disparate datasets, mitigating biases, and navigating advanced statistical methods. The proposed project builds on the PD/PI’s extensive expertise in AI integration, behavioral science, and health informatics. The interdisciplinary team includes experts in neuroimaging, genomics, behavioral science, biostatistics, AI ethics, and computer science, ensuring comprehensive support for StatWiseAI’s development and dissemination. StatWiseAI will transform the way researchers approach complex data analysis, reducing barriers to statistical expertise and fostering a research culture of enhanced methodological rigor, reproducibility, and innovation. By empowering researchers across all career stages, it will contribute to advancing the field and producing high-impact, reliable science.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract. APOE4 homozygosity is a major genetic risk factor for late-onset Alzheimer’s disease (AD). The hippocampus is a brain region that is particularly affected by AD. The hippocampus exhibits different patterns of neuronal population activity that support different memory functions. Yet, the extent to which specific hippocampal neuronal population patterns are susceptible to disruption by APOE4 homozygosity remains unknown. Here, we propose to examine how APOE4 homozygosity affects hippocampal neuronal population dynamics that support different memory operations in the hippocampus. In healthy wildtype rats, memories are represented by coordinated populations of hippocampal “place cells”, neurons that fire in specific spatial locations and are thought to code the “where” component of episodic memories. During active behaviors, place cell populations represent successive locations across learned trajectories by firing in organized sequences within cycles of the ~8 Hz theta rhythm. Place cell sequences that fire during active exploratory behaviors later reactivate or “replay” in a temporally compressed manner during awake rest and slow-wave sleep. Replay is thought to play a key role in memory consolidation and retrieval by transferring a compressed memory format from the hippocampus to downstream cortical regions that store memories long-term and direct memory-guided behaviors. Theta-coordinated sequences of place cells and place cell sequence replay are widely believed to be key mechanisms underlying hippocampal memory encoding, consolidation, and retrieval. Yet, to our knowledge, no prior study has investigated how place cell sequence coding of memory representations is affected by APOE4 homozygosity. This project will address this critical gap in knowledge by recording large populations of place cells in the hippocampus in a rat model of APOE4 homozygosity, ApoE4 knockin (ApoE4-KI) rats. ApoE3 knockin (ApoE3-KI) rats will serve as a control group given that APOE3 in humans is not associated with an increased risk of AD. We will apply a state-of-the-art Bayesian decoding approach to place cell populations recorded during waking behaviors and sleep to determine how transmission of information related to specific memories and experiences is affected by APOE4 homozygosity. Specific Aim 1 will determine whether development of new memory representations coded by theta-coordinated sequences of place cells during active behaviors is impaired in ApoE4-KI rats. Specific Aim 2 will test the hypothesis that replay of place cell representations of new memories during rest and sleep is deficient in ApoE4-KI rats. Results from this project will reveal new insights about how APOE4 homozygosity affects hippocampal neuronal population coding of new spatial memory representations during memory formation, recall, and consolidation. We expect that this approach can be used in future studies to test whether proposed treatments for AD alleviate impairments in memory representations associated with APOE4 homozygosity.

NIH Research Projects · FY 2025 · 2025-09

Research in my laboratory is focused on understanding the structure and function of macromolecular machines using cryo-electron microscopy (cryo-EM). There are three broad avenues of investigation: (1) mechanisms of RNA-guided CRISPR-Cas surveillance (2) methods to solve structures of human complexes in a highly parallel manner, and (3) understanding nucleic acid processing machines, including ribosomes and RNA polymerases. We are fundamentally interested in understanding how molecular machines are assembled and perform their myriads of functions. We use multidisciplinary approaches to perform detailed mechanistic studies that, in the end, lead to high impact work and broad applications. Here, I propose how our approach of studying basic enzymology will lead to important insights into (a) how Cas9 can function as a multi- turnover enzyme, (b) how PRIME editors recognize and edit DNA targets, (c) the structure and function of retrons, and (d) determining the mechanisms of novel antiviral defense systems in bacteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Individuals with primary progressive aphasia (PPA) present with inevitable erosion of communicative function caused by neurodegenerative diseases such as Alzheimer’s disease and frontotemporal lobar degeneration. Losing the ability to communicate is a devastating, life-changing process that impacts both individuals living with these disorders and their care partners. There is increasing evidence indicating that restitutive interventions (i.e., those focused on ameliorating core communication impairments) are efficacious in PPA. Recognizing the limitations of current restitutive methodologies, especially for advanced cases and those from ethnoracially and linguistically diverse backgrounds, the proposed research pioneers a comprehensive, multicomponent progressive aphasia (Multi-PA) treatment protocol. This novel approach is tailored not only to individuals facing the multifaceted communication challenges of speech/language-led dementias but also extends education and training to their care partners. The project leverages the success of our previous NIH-funded work, which confirmed the benefits of restitutive speech-language telerehabilitation for PPA. It aims to evolve these methods by integrating compensatory and partner-focused strategies, addressing the broader needs of patients across varying stages of disease severity and clinical presentation. Such a holistic approach is particularly vital as up to 40% of individuals with PPA present as mixed or unclassifiable, highlighting the need for a more adaptable intervention model. In a significant shift from traditional methodologies, this randomized pilot and feasibility study emphasizes the value of patient-reported outcome measures (PROMs), alongside performance-based outcomes, in Latinos and non-Hispanic/Latinos. This approach aims to capture a more authentic and comprehensive picture of patients' quality of life and overall well-being, factors often overshadowed by conventional, performance-focused metrics. Over a two-year span, the study will execute a Stage 1b randomized pilot/feasibility telerehabilitation trial involving 30 participants with PPA and 30 care partners, with half of the sample composed of Latinos/Hispanics. This strategy underscores the project's commitment to including perspectives of individuals from diverse ethnoracial and linguistic backgrounds in the early stages of intervention development. In summary, this project seeks to 1) evaluate the feasibility and acceptability of the Multi-PA telerehabilitation model for participants and their care partners and 2) evaluate suitability of outcome measures and preliminary patterns of treatment response to inform a future efficacy trial for Multi-PA. This innovative project stands to significantly influence care paradigms for Alzheimer’s disease and related dementias (ADRD), advocating for a person-centered approach that acknowledges the diversity and complexity of individuals living with speech/language-led dementia. Aligned with the NIA’s strategic priorities, the project holds promise for reshaping intervention strategies and enhancing communication and quality of life for individuals from diverse ethnoracial and linguistic backgrounds with ADRD and their care partners.

NIH Research Projects · FY 2026 · 2025-09

Quantity Meets Quality: Examining input statistics for early word learning from the infant's point of view Project Summary / Abstract Children acquire language in everyday contexts where the language environment plays a critical role in supporting early language development. In early word learning, infants learn object names by linking heard words to seen objects in their environment. This is well recognized as a computationally complex problem due to referential uncertainty -- at the moment of hearing any word, there may be many potential referents in the learner's immediate environment. Many laboratory experiments are designed based on various assumptions about the uncertainty challenge that young learners face, which may not reflect the uncertainty encountered in everyday learning experiences. Despite considerable research efforts, little is known about the referential uncertainty infants face in their everyday contexts. We built a Home-like Observational Multimodal Environment (HOME) to examine how 12- to 24-month-olds and their parents jointly create input statistics for early word learning in everyday activities. Using head-mounted eye-tracking technology in the HOME lab, we will record infant gaze and parent speech during three everyday activities: toy play, book reading, and meal preparation. We will specifically focus on the moments when parents produce object names in their speech, precisely measuring the infants' in-the- moment attention toward those named objects. Through the analyses of high-density gaze and speech data, we will characterize the referential uncertainty at individual naming instances from the infant's point of view (Aim 1), examine the computational mechanisms that operate on the input statistics to achieve successful word learning (Aim 2), compare the quantity and quality of the input statistics among the three different everyday activities (Aim 3), and determine whether the quantity and quality of the input statistics in those activities predict individual differences in vocabulary growth (Aim 4). This proposed work holds significant potential for breaking barriers that stall the current understanding of early word learning, developing innovative methods to quantify the multimodal nature of the language environment, and providing mechanistic insights into the origins and consequences of individual differences in language development.

NIH Research Projects · FY 2025 · 2025-09

Abstract Autism is a neurodevelopmental condition that is highly prevalent and that comes at high cost to affected children and their families. The costs of autism can be attributed, at least in part, to the effects of the condition on children’s social communication and language development. Younger siblings of autistic children (HL-Sibs) are highly likely to receive a future diagnosis of autism and/or language disorder themselves. Even those HL- Sibs who are not diagnosed have an increased likelihood of displaying differences in social communication and delays in language development. There is increasing support that preemptive interventions may influence caregivers’ use of strategies that scaffold development and translate to effects on social communication and language skill in HL-Sibs. However, prior studies of preemptive interventions have been limited by inclusion of samples that are homogenous in race, ethnicity, and socioeconomic status, likely reflecting those families who can readily access the medical centers and academic institutions conducting this research. Additionally, past investigations suggest that effects of preemptive interventions may (a) yield only indirect effects on child social communication and language, and (b) vary according to child and caregiver factors, but no previously identified mediators or moderators of preemptive intervention effects have been replicated, and many factors that likely explain or influence differential intervention effects have not yet been explored. Further, the extant literature testing the efficacy of preemptive interventions has administered treatments across a broad range of child ages, and we do not yet know precisely when preemptive treatment is most likely to yield favorable effects. Finally, some studies have reported high rates of attrition amongst families assigned to treatment, suggesting that caregivers may not find preemptive interventions to be acceptable. The present study will, thus, evaluate the efficacy of ImPACT, an intervention that is established for use with autistic children and that has amassed some past empirical support as delivered in vivo in HL-Sibs, as administered by telehealth (hereafter referred to as tele-ImPACT) relative to a no-treatment control for effects on caregiver strategy use, proximal child skills, and distal child social communication and language in a large, representative sample in the context of a rigorous randomized clinical trial. Advanced analytic approaches will be employed to test numerous putative moderators and mediators of tele-ImPACT effects, and qualitative approaches will be utilized to assess the acceptability of the candidate treatment amongst caregivers. INNOVATION AND IMPACT: This innovative and interdisciplinary study is expected to (a) provide empirical support for a novel preemptive intervention that is theoretically motivated and developmentally informed, as well as accessible for and acceptable to families, and (b) lend new insights into the developmental windows in which, mechanisms by which, and the subgroups for which this intervention works. Such findings would have strong implications for theory, research, and clinical practice in young children at elevated likelihood for autism and developmental language disorder.

NSF Awards · FY 2025 · 2025-09

To understand speech, the brain must quickly sort through a stream of changing sounds and identify the patterns that carry meaning. This ability is shaped by experience, as sounds that are easily recognized by individuals with significant exposure to a language may go unnoticed by individuals without that exposure. Over time, the auditory system adapts by reorganizing how it processes and prioritizes important sound features. This fine-tuning allows the brain to efficiently extract the sound patterns most relevant for communication. Understanding how these neural changes occur is essential for building more accurate models of auditory processing and for developing brain-based tools for clinical diagnostics, neurobiometric identification, and brain-computer interfaces. This project examines how long-term experience with different speech environments shapes both perception and brain activity. Using auditory perception tasks and non-invasive brain recordings, the study compares how speakers of languages with different levels of sound complexity perceive and process key speech features. Artificial intelligence techniques are used to identify reliable neural markers of language-specific and general auditory processing and to model how the auditory system adjusts to familiar and unfamiliar sounds. This work advances our current understanding of the factors that shape auditory processing, has the potential to update current models of auditory neuroplasticity, and supports the development of brain-based technologies focused on auditory function. The project also provides hands-on research experience for both graduate and undergraduate students, helping to train the next generation of scientists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Age-related cognitive decline and Alzheimer's disease are associated with hippocampal interneuron loss and hyperexcitability, which reflects the selective vulnerability of inhibitory neurons. Our preliminary data shows a ubiquitous reduction of inhibitory synapses onto hippocampal CA1 stratum radiatum dendrites in aged rats compared to young adults, regardless of their cognitive status. Aged rats that showed spatial learning deficits had enlarged excitatory synapses on these same dendrites, disrupting local E/I balance, which is important for learning and memory. In aged animals that maintained learning capacity, the remaining inhibitory synapses were larger, thus preserving local dendritic E/I balance. These data suggest that while some interneurons are vulnerable in the aging process, others are resilient and provide a potential compensatory role for maintaining local excitation and inhibition. The broad heterogeneity of interneurons reflects subtypes with various molecular profiles, firing rates, and differential targeting to specific CA1 dendrites. We hypothesize that specific interneuron subtypes are more susceptible to age-related changes, while others are resilient and contribute to compensatory learning mechanisms by maintaining local E/I balance. Studies that previously identified vulnerable interneuron subtypes in the aging brain typically used techniques to isolate one interneuron subtype at a time, limiting our understanding of how diverse interneuron populations interact and contribute to circuit function in the aging brain. Furthermore, the subcellular organelles and local resources crucial for ultrastructural plasticity, such as mitochondria, endosomes, smooth endoplasmic reticulum, and polyribosomes, remain largely uncharacterized in these interneuron subtypes during aging. Consequently, we lack a comprehensive understanding of how interneuron interactions and age-related alterations in their ultrastructural resources contribute to cognitive decline or resilience. This knowledge gap is exacerbated by limitations in traditional techniques for identifying interneuron subtypes that often compromise tissue quality, hindering ultrastructural analysis via serial section electron microscopy (3DEM). We propose using a novel ultraplex correlative light and electron microscopy (CLEM) method that allows for simultaneous identification of multiple interneuron subtypes via RNA and protein markers in tissue optimized for 3DEM. In Aim 1, we will use ultraplex CLEM to quantify interneuron subtypes and their subcellular structures in hippocampal tissue from behaviorally characterized aged rats, capturing any intrinsic ultrastructural changes that occur in these cell populations. In Aim 2, we will use our new machine learning tools to segment and reconstruct local interneuron circuits and synapses in distinct CA1 layers to identify alterations associated with age-related cognitive decline and resilience. This approach will provide valuable insights into the role of specific interneuron subtypes in maintaining E/I balance and cognitive function during aging, ultimately contributing to our understanding of selective neuronal vulnerability in Alzheimer's disease.

NSF Awards · FY 2025 · 2025-09

This project studies locally homogeneous geometric manifolds, which are abstract mathematical objects designed to model the physical universe. The term locally homogeneous refers to the presence of a high degree of local symmetry which is captured by an object called the (local) symmetry group. It is this symmetry group which dictates the geometry, in the following sense: the meaningful quantities we can measure in a geometric manifold, such as lengths or angles, are exactly those which are invariant under the symmetry. There are many different possible symmetry groups which lead to different types of geometric manifolds useful in many contexts across mathematics and physics. There can also be many different geometric manifolds with the same local symmetry group. These all have the same local properties but can look very different at large scale. The space of all such possibilities is called a moduli space. While the precise features, for example the shape or size, of the universe is a question for empirical physics, a moduli space is the mathematical answer to the question of what possible features the universe could have. The research goals of this project are, roughly, to better understand several special types of locally homogeneous geometric manifolds which have mysterious but tractable behavior. This project contributes to the growing base of foundational mathematical knowledge on which many innovations in science and engineering are eventually built. Broader impacts of the project focus on guided student research, contributing to training the next generation of research mathematicians. The principal investigator (PI) will train graduate students to perform research related to the main topics of the project. The PI will also advise undergraduate computation and visualization projects focused on these research themes. The project develops a new program to study the surface group representations comprising higher rank Teichmuller spaces by examining extreme behavior in the Benoist limit cone. Initial results suggest that such extreme behavior is achieved by fundamental topological structures on the surface, analogous to the maximally stretched laminations appearing in the study of Thurston's asymmetric metric. The project also continues a broad program to study convex real projective structures, building on the PI’s prior work on convex real projective Dehn filling in dimension three, and the principal investigator's prior work on a general notion of convex cocompactness in projective space (of any dimension) generalizing the well-studied notion from Kleinian groups. Finally, the project will extend themes from the PI's body of work on Margulis spacetimes to new affine geometry contexts in higher dimensions, motivated by the Auslander Conjecture. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Induced pluripotent stem cells (iPSCs) offer immense promise for autologous therapies by using the recipient’s own cells to engineer new functional tissue. Many of the targeted diseases for iPSC treatment research, such as cardiovascular diseases, are disproportionally found in aging individuals. While iPSCs have been generated from donors of all ages, even centenarians, the influence of donor age on iPSC functionality remains a critically understudied aspect. Moreover, the role of environmental age on differentiated iPSC functionality is not well understood. This research aims to comprehensively address this gap, particularly focusing on the effects of age- related changes in both the donor and environment of iPSC-derived endothelial progenitors (iPSC-EPs). My preliminary data indicates that iPSC-EPs from mature donors (above the age of 30) exhibit a diminished capacity for self-assembling into microvasculature. Building on this observation, I propose a two-fold investigation (Fig. 1). Firstly, I will characterize differences between iPSC-EPs sourced from neonatal, mature, and aged donors, exploring differentiation yield, methylation patterns, gene expression levels, and vascular cell biopotency. This analysis will provide a holistic understanding of how donor age persists through iPSC differentiation into vascular lineages. Secondly, I will investigate the influence of age-related changes in the microenvironment on iPSC-EP functionality by employing innovative 3D hydrogel environments with tunable stiffness and variable composition properties (Fig 1.). With age, there is a general increase in tissue stiffness, but also a change in extracellular protein composition. By decoupling the effects of stiffness from ECM composition, I expect to characterize the specific contributions of these factors and their role in determining microvascular plexus structure, gene expression, and protein expression profiles. Through the examination of iPSC lines sourced from neonatal, mature, and aged donors, matched by sex and somatic cell origin, this research aims to provide crucial insights into the potential limitations of using iPSCs from older individuals for tissue-engineered vascular applications. I hypothesize that age-related differences in gene expression will result in limited microvasculature formation in iPSC-EPs from aged donors, but exposure to a young microenvironment may offer a promising avenue for rejuvenation. This study holds significance for advancing the field of iPSC research and informing the development of effective strategies for personalized vascular therapies, especially as it relates to targeted aged populations.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The lack of cell culture strategies that faithfully replicate both form and function of the extracellular matrix (ECM) hinders our ability to effectively monitor, diagnose, and treat disease. This arises from the contemporary reliance on in vitro cell culture methods that fail to replicate complex biological structures, while in vivo cell cultures use animal models that have disparate physiology from humans. As a result, there is a gap in fundamental knowledge surrounding how cells respond to various stimuli (or cues), limiting our ability to understand and fight illness. Filling this gap necessitates new precision processes to create 3D structures that emulate the natural ECM for in vitro cell culture. Herein, a feedback loop between digital light processing (DLP) 3D printing and cell studies will provide new process-structure-function relationships between the technology to create biomimetic scaffolds and concomitant cell-matrix interactions. Open fundamental questions to be addressed herein include: 1) To what extent does using visible and near infrared light in DLP 3D printing improve cell viability and metabolic activity over using UV light? 2) What are the optimal microscopic mesh sizes and macroscopic pore geometries to facilitate uniform cell morphology and proliferation? 3) How does the layered topography of DLP 3D printed structures influence cell behavior (e.g., adhesion, mobility, and differentiation)? 4) How do cells respond to sharp vs. smooth interfaces where a cue is transitioning (e.g., stiffness gradients)? 5) How do cellular responses vary for individual vs. combined mechanical and biochemical cues? Answers to these questions will be accomplished using human mesenchymal stem cells (hMSCs) as a model system given their multipotent versatility and microenvironment sensitivity that is representative of many cell types. DLP 3D printing was selected as the fabrication platform owing to its unparalleled combination of speed, resolution, and low cost. However, this technique to-date has been predominantly restricted to harmful UV light exposure, toxic acrylic resins, and the production of rigid homogeneous parts that differ from the vital heterogeneity and softness present in many ECMs. The Page lab is uniquely positioned to address these issues given their existing expertise and infrastructure that will enable integration of heterogeneity into biomimetic structures by using benign visible to near infrared (multi-)color and intensity (grayscale) light projection. This will allow for precise 3D spatial control over network structure, mechanical properties, and protein tagging. hMSCs will be directly encapsulated in synthetic ECM mimetics that emulate tissues ranging in stiffness from lungs (“supersoft”) to ligaments (“hard”). Additionally, localization of proteins (e.g., growth factors) will be used to direct adhesion, movement, and growth of hMSCs. Cell viability and behavior within the 3D scaffolds will be systematically characterized to elicit structure-function relationships that address the above fundamental questions and that will inform further bioprinting optimization and cell culture. If successful, this work will provide a platform to improve our capability to monitor, diagnose, and treat diseases in a non-invasive in vitro manner.

NSF Awards · FY 2025 · 2025-09

With support from the Chemical Theory, Models, and Computational Methods (CTMC) and Chemical Structure and Dynamics (CSD) programs in the Division of Chemistry, Professor Doran Raccah of the University of Texas and Eric Bittner of the University of Houston are organizing the Texas Winter School on Chemical Applications of Quantum Information. Scheduled for Winter 2025 in Wimberly, TX, this specialized week-long program is tailored for PhD students in theoretical and experimental physical chemistry. The winter school aims to bridge critical educational gaps identified by the NSF-UKRI Bilateral Workshop on Quantum Information Science in Chemistry. A combination of lectures and tutorial sessions will be used to develop both a conceptual and practical understanding of topics at the intersection of quantum information and theoretical chemistry. More advanced discussions will be fostered around research talks from selected participants. To enhance accessibility, this initiative will create freely available, student-edited course materials to extend educational benefits to the broader scientific community. The Winter School will systematically address topics at the intersection of quantum information science (QIS) and chemical dynamics. Through an intensive mix of lectures, tutorials, and collaborative learning, this winter school will offer focused instruction covering areas such as quantum light-matter interactions (both strong and weak coupling regimes), stochastic quantum dynamics, quantum coherence, and entanglement in chemical systems. The lectures will draw from modern advances in a variety of fields including polaritonics, quantum computing, and entangled light spectroscopy. Participants will actively engage with the specialized content of the workshop by contributing to a collection of notes and team coding projects, which will subsequently be made accessible via Mathematica notebooks, python notebooks, and similar tools on Wolfram Cloud and/or GitHub. This structured, collaborative format is designed to equip the next generation of scientists with essential theoretical and computational skills necessary to advance research at the critical interface of chemistry and quantum information science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Profs. Simon M. Humphrey and Graeme Henkelman at the University of Texas at Austin are leading a collaborative research program involving the synthesis, catalytic and computational studies of new nanoparticle-based catalyst materials. The targeted catalysts are based on unstudied (and, in some cases, previously inaccessible) compositions of metallic elements, such that cooperative properties provide access to catalysts with new types of reactivity, and advanced chemical selectivity. Metal-based catalysts are widely utilized in large-scale conversions of simple chemical feedstocks to provide value-added products that are crucial to produce drugs, polymers, textiles, detergents, and many other materials required by modern society. Catalysts also reduce the production of waste by-products, which is environmentally important. This project will specifically aim to discover and study new catalyst materials that contain combinations of three or four different metallic species, whose specific catalyst compositions that have been predicted by computational chemistry to be capable of achieving advanced reactivity in the conversion of (a) bioethanol into hydrogen, and (b) to use the generated hydrogen in reaction with carbon dioxide to produce methanol. Bioethanol is an increasingly widely available liquid fuel, produced in the USA by natural (biological) fermentation of waste biomass, corn, grain etc. Meanwhile, biological processes do not provide easy access to methanol, which is required as a key chemical feedstock but must be prepared by traditional chemical processes that utilize fossil fuels, resulting in negative environmental impacts. The overarching aim of this project is to design new catalysts that can directly convert bioethanol into hydrogen and methanol. The performance of newly prepared catalysts is assessed using tandem experimental and computational methods. This work therefore provides a deeper fundamental understanding of how catalyst composition relates to reactivity enhancements, for future industrial implementation. With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Profs. Simon M. Humphrey and Graeme Henkelman at the University of Texas at Austin are leading an experimental-computational research program that targets the formation of new ternary and quaternary metallic nanoalloys for selective conversion of bioethanol into hydrogen and methanol. The collaborative research team utilizes computation approaches to predict specific combinations of noble and earth-abundant transition metals, which may not be accessible in bulk but are stable on the nanoscale. Synergistic effects in the nanoalloys are predicted to enable catalytic reactivity that is highly tunable towards two specific reactions: (a) reforming of bio-ethanol with water to yield hydrogen and carbon dioxide; (b) subsequent conversion of the hydrogen and carbon dioxide to generate selectively methanol. Bioethanol is an increasingly available and viable chemical feedstock, but the synthesis of methanol is still almost entirely dependent on traditional, energy-intensive pathways and produce by-products of environmental concern. Esoteric synthetic methods will be exploited to determine routes toward previously unstudied metallic alloys, enabling detailed model studies to elucidate important relationships between catalyst composition and structure and resulting reaction selectivity. The catalysts will feature new combinations of noble metals (Rh, Ir, Pd, Pt, Au) alloyed with earth-abundant transition metals (Co, Ni, Cu). The overarching goal is to obtain commercially viable catalysts for future production of methanol from bioethanol, at scale. To achieve this aim, the team utilizes model reaction studies conducted in water, under industrially realistic reaction conditions. By leveraging fundamental experimental results in combination with a palette of spectroscopic techniques, further computational studies provide realistic theoretical models at the atomic scale, which predict necessary catalyst refinements to further improve reaction activity and selectivity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

There is a growing need to educate more students in quantum science—an exciting and rapidly advancing field that underpins many modern technologies, including semiconductor chips and medical imaging tools like MRI. Currently, quantum science is primarily taught within physics and chemistry departments, and existing courses and training approaches often fall short in supporting the interdisciplinary collaboration needed to solve the field’s most pressing questions. This National Science Foundation Research Traineeship (NRT) award at University of Texas at Austin brings together faculty from the Cockrell School of Engineering and the College of Natural Science to design new courses and hands-on learning experiences to prepare graduate students from diverse academic backgrounds for the quantum workforce of the future. The University of Texas at Austin—recognized for its strong science and technology ecosystem—offers an ideal environment for this initiative. Austin’s expanding high-tech industry and the recently established Texas Quantum Institute will provide students with meaningful connections to real-world applications and opportunities in quantum research and the broader STEM workforce. NRT trainees will gain valuable skills through research, teamwork, mentorship, career development, and community engagement. Approximately 100 graduate students will participate in this program and 16 funded graduate trainees from several departments, including electrical engineering, computer science, mechanical engineering, and physics, will be served. This project will prepare participating students for careers in the growing quantum technology sector. Trainees will also participate in public outreach activities at nearby high schools and community colleges, reaching an additional 300-500 individuals. Led by faculty from electrical engineering, computer science, mechanical engineering, and physics, this NRT research program is built around four interlinked research themes: foundational studies of qubits; quantum transduction through photonic and acoustic integration; quantum algorithm development; and applied research in quantum networking, communication, sensing, and simulation. These projects hold great promise towards scalable technologies in quantum networks, quantum sensors, and quantum algorithms. The project is laterally integrated across quantum platforms and vertically integrated across colleges, departments, and researchers. Trainees will engage in collaborative research projects that bring together different fields and encourage innovative thinking across disciplines while being mentored by a multidisciplinary faculty committee. The project will create five new quantum science and technology (QST) courses that include hands-on learning to provide foundational training for students from a wide range of academic backgrounds. The project will also build a well-rounded training experience that combines QST courses, interdisciplinary research, skill-building workshops, mentoring, internships, and public engagement activities. Trainees will receive grounding in perspectives from industry and entrepreneurship to help prepare them for broad career outcomes. Additional graduate and undergraduate students will be encouraged to join QST classes, attend seminars, and participate in research efforts. One outcome of the project is institutionalization of a graduate certificate in QST to help prepare a skilled workforce in quantum science beyond the life of the grant. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, and potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

PART 1: NON-TECHNICAL SUMMARY The operation of batteries critically relies on the movement of charged ions through an electrolyte medium. If solids could be used as electrolyte rather than the flammable liquids employed today, the resulting devices would be safer. Furthermore, among the possible ions, magnesium and calcium carry twice the charge of lithium, the current technological incumbent, so batteries based on these metal ions could also store more energy while minimizing the use of critical materials. However, these "multivalent" ions have difficulty moving through solids, which has prevented their use in practical batteries. With support from the Solid State and Materials Chemistry program in the Division of Materials Research at NSF, researchers at University of Texas at Austin and University of Illinois Chicago combine advanced computer simulations with laboratory experiments in a feedback loop to design materials that overcome this fundamental barrier. The simulations predict atomic structures and chemical compositions that should allow fast calcium or magnesium movement. The team then synthesizes the best candidates, measures their properties as electrolytes, and uses the results to refine the predictive models. Success in this work could lead to a new class of solid electrolyte batteries that combine high energy storage with safe operation. To enhance the impact of the research, the project aims to introduce undergraduates to cutting-edge scientific topics early in their career, conducts student exchange between institutions to enhance workforce development, and promotes wide exchanges of ideas through international symposia. These efforts advance fundamental knowledge in materials chemistry, train the next generation of scientists and engineers, and contribute to U.S. goals for innovation in energy and technologies with secure supply chains. PART 2: TECHNICAL SUMMARY Achieving fast conduction of multivalent ions through solids remains a fundamental challenge in solid state chemistry. With support from the Solid State and Materials Chemistry program in the Division of Materials Research at NSF, this project integrates computational and synthetic exploration to identify and develop solid electrolytes with high conductivity for magnesium and calcium. The work focuses on broad families of mixed anion compounds of magnesium and calcium crystallizing in an anti-perovskite structure. The anionic sublattice is formed by different combinations of pnictides or rotatable cluster anions in order to assess their impact on ion dynamics. The technical approach involves first-principles calculations to screen candidate compounds that are chemically stable, exhibit rotatable anions, and possess low ion-migration barriers. In parallel, the team pursues the synthesis of predicted phases to validate and enhance computations. After successful synthesis of promising candidates, the atomic structure and ion transport are measured using X-ray diffraction, impedance spectroscopy, and nuclear magnetic resonance techniques. Lastly, to further enhance the movement of ions, predictions are used to guide the experimental introduction of aliovalent dopants on the anion sublattice to generate cation vacancies. This integrated theory-experiment approach seeks to establish design principles for fast multivalent-ion conduction in solid electrolytes that push new boundaries in the movement of ions through solids while informing the development of batteries with unique performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Alcohol use disorders is a complex polygenic disease. Cell-type specific data from medial prefrontal cortex (mPFC) from alcohol-dependent mice shows dysregulated gene co-expression networks and transcription factors. This proposal tests the hypothesis that genetic rescuing of dependence-dysregulated gene regulatory networks and co-expression modules will result in reversing escalated alcohol use. The proposal tests this hypothesis in three specific aims: Aim 1 (K99): To test the effect of genetic rescuing an astrocytic dependence-upregulated gene co-expression module on escalated alcohol drinking through performing parallel perturbations of the top connected hub genes of the module. This aim will assess the contribution of gene co-expression modules to alcohol drinking phenotype; Aim 2 (K99): To identify cell-type specific dependence-dysregulated chromatin accessibility and gene regulatory networks. This aim will allow for the (a) construction of cell type-specific gene regulatory networks through connecting cell type-specific chromatin accessibility to gene expression data in alcohol-dependent and control samples, allowing for the identification of alcohol-dysregulated gene regulatory networks, Aim3 (R00): To reverse alcohol escalation through rescuing alcohol-dysregulated gene regulatory networks. This aim performs global non cell-type specific perturbation of a set of transcription factors in alcohol-dependent mice followed by characterization of the resulting changes in alcohol-drinking phenotypes. Dr. Nihal Salem’s goal is to be an independent researcher in the field of alcohol research and to develop a research program that spans genomics, bioinformatics, and functional studies linking transcriptomics to alcohol drinking phenotypes. This proposal outlines a research and training plan to provide Dr. Salem with two years of mentored postdoctoral training and three years of support to start her independent research direction.

NSF Awards · FY 2025 · 2025-09

Competition is fact of life: all organisms are constantly engaged in competition for mates and other resources, and they often do so aggressively. These competitive interactions can have important consequences not just for the individuals that engage in them but also for the evolution and persistence of populations and species. For example, scientists have shown that new species can evolve when territory holders direct more aggression towards competitors that resemble themselves compared to those that are dissimilar. However, we know very little about the physiological and neurobiological processes that control this kind of aggression that is biased toward individuals that look more similar to oneself. The proposed research will use a combination of social learning experiments, genetic analysis, neural activity mapping, and brain gene expression analyses to investigate this question using two highly social cichlid fish species that differ in body coloration. This highly integrative approach will advance our understanding of how social competition influences evolutionary change and biodiversity. The multidisciplinary project will build collaborative relationships among institutions in the US (two R2 and one R1), Switzerland, and Tanzania. It will also provide research and training opportunities for undergraduate and graduate students. This project will promote STEM engagement through the development of an interactive exhibit on cichlid social behavior at a local children’s discovery museum. Together, these efforts will answer fundamental questions in organismal biology while having a significant societal impact by preparing students to enter STEM fields. Male-male competition for mates and breeding territories is thought to play an important role in population differentiation and speciation. If males bias territorial aggression towards males that resemble their own phenotype, rare male phenotypes would be involved in fewer costly fights. The resulting negative frequency-dependent selection could facilitate the evolution of distinct phenotypes and stabilize the speciation process. However, the underlying proximate mechanisms remain mostly unknown. The proposed research will assess the cognitive, genetic, and neural basis of aggression biases in the cichlid species pair Pundamilia nyererei (males are red) and P. pundamilia (males are blue). These two species co-occur at different locations in the East African Lake Victoria with varying degrees of reproductive isolation, allowing us to study the mechanistic basis of aggression biases at different stages of speciation. The project has the following objectives: First, this research will estimate the number of genetic loci regulating aggression biases and body coloration and their degree of linkage using quantitative genetics and quantitative trait locus mapping. Second, this research will assess how social experience and social context shape aggression biases. Finally, the project will determine the neural mechanisms regulating aggression biases using a combination of neural activity mapping, pharmacological manipulation of dopamine signaling, and neural transcriptome analysis. This highly integrative approach will provide insights into how male-male competition contributes to speciation, agonistic character evolution, and species coexistence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Effective emotion regulation (ER) is essential for successful development, as difficulties in ER are associated with significant emotional, behavioral, and mental problems. Early in life, parental responses to their child’s negative emotions play a significant role in the development of adaptive ER. However, little work has explored how in-the-moment parental responses during in-vivo emotional events influence children’s ER. Furthermore, while the functioning of biological systems is crucial in shaping the development of effective emotional processes, there is a significant gap in understanding how in-the-moment parental responses influence children’s neural and physiological regulation. Aligning with NICHD’s Strategic Objective 4 which focuses on “improving child and adolescent health and the transition to adulthood”, this K99/R00 proposal aims to explore how in-the-moment parental responses influence key neural and physiological markers of ER, especially in response to negative emotions (Aims 1, 3, and 4). Additionally, the quality of the parent-child relationship is hypothesized to play a crucial role in moderating the effects of the relationship between parental responses and children’s neural and physiological regulation. Therefore, the proposed study will also examine how parent-child relationship quality influences the association between parental responses and the child’s neural and physiological ER (Aims 2, 5). This research will employ EEG and ECG measures (i.e., alpha asymmetry, respiratory sinus arrhythmia) to assess children’s neural and physiological ER processes. Observational assessments of parental responses will also be conducted to capture dynamic parent-child interactions. The findings from this project could significantly improve our understanding of how in-the-moment parenting affects child ER by integrating neural and physiological data, offering insights that may inform future intervention strategies. The K99/R00 phase of the award will provide essential training in four key areas: 1) EEG and ECG methodologies; 2) behavioral coding of parental responses to children’s negative emotions; 3) longitudinal study design, data collection, and analysis; 4) theoretical and methodological approaches to studying the impact of parent-child relationship quality on child ER. A multidisciplinary team of mentors and consultants has been assembled to support the candidate in achieving these training objectives. Ultimately, this research will offer new insights into the dynamic relationship between parenting and children’s neural and physiological regulation during early to middle childhood, a period when ER transitions from being primarily externally regulated by parents to becoming more internally regulated by the child. The proposed research and training plan will prepare the candidate to establish an independent and distinctive research program that employs multi-modal methods to advance the field of developmental psychology, with the long-term goal of informing evidence-based strategies that promote healthy emotional development in children.

NSF Awards · FY 2025 · 2025-09

The CloudSec project leverages Artificial Intelligence (AI) to protect sensitive scientific resources and data. Scientific computing relies heavily on secure and efficient access control systems to protect sensitive resources and data. However, there is no effective tool to ensure these critical access control policies truly reflect the intentions of users. As a result, sensitive data and resources have been exposed and this threat persists despite the best intentions of both scientists and cybersecurity experts. To counter this threat, CloudSec helps researchers and developers of scientific infrastructure to collaboratively work to develop access control policies that align with user intent. Improving the security of access control policies requires overcoming the disconnect between high-level scientific workflows and low-level infrastructure policies. To accomplish this, CloudSec uses Large Language Models (LLMs) and Natural Language Processing (NLP) to provide a new tool-assisted approach. CloudSec supports cross-layer policy analysis to compare user level policy with system-level policy to detect discrepancies. When mismatches are found, Cloudsec explains how they may lead to security vulnerabilities, guiding suggestions to refine policies to better align with user intent. The project’s intellectual contributions advance foundational tools and methods for securing cyberinfrastructure, including innovative policy analysis techniques, intuitive tools for capturing user intent, and interactive workflows for refining access policies. Its broader impacts include enhancing the security of scientific discovery and workflows, fostering collaboration between researchers and developers on policy design, and integrating findings into computer science and scientific computing curricula at partner institutions. CloudSec is piloted and validated using real-world applications hosted on the Tapis project, a secure platform for managing computational research. The resulting CloudSec methodology is designed to be broadly applicable across other cyberinfrastructure systems and made available as open source, hosted on GitHub, and a publicly available web application and API. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

One of the biggest unknowns in projecting future sea level is how fast the Antarctic Ice Sheet will melt in response to continued warming. An increase in high-latitude snowfall may offset some ice sheet melt due to warming of surrounding ocean and atmosphere, though it is not yet known how effective this compensating mechanism is, or under what timescales or conditions it might be important. To better understand these competing processes, researchers are investigating moisture-driven mechanisms of ice sheet growth during a past interval in Earth’s history where the climate was warm (the Miocene Climate Optimum, about 17 to 14.8 million years ago). During this time, Earth was warmer than today, yet geologic records hint at episodes where Antarctica was gaining ice. This project brings together an interdisciplinary team of experts across three institutions to investigate the potential for moisture-driven ice growth using a combination of advanced Earth system models and geologic data, while providing hands-on interdisciplinary geoscience training for graduate and undergraduate students. Researchers will use isotope-enabled climate and ice sheet models to test a suite of hypothesized mechanisms for precipitation-driven Antarctic ice growth during the Miocene Climate Optimum. Each model simulation tracks the oxygen isotopic concentration of ice, generating a modeled oxygen isotope signal that can be compared directly against deep-sea isotopic records. To evaluate model simulations, the team will generate a new high-resolution record of Antarctic Ice Sheet volume using paired benthic foraminiferal oxygen isotopes and Mg/Ca measurements from a deep-sea sediment core from 17-15 Ma, providing a key dataset for model validation alongside a synthesis of published geologic records spanning this time. Data-model comparisons will evaluate how well each modeled mechanism can explain the observed ice volume and oxygen isotope changes recorded in deep sea sediments. Specifically, investigators will explore the ice-growth potential of local polar mechanisms (such as ice-proximal ocean warmth and sea ice cover), as well as global hemispheric processes (such as CO2 and orbital forcing) that influence the heat and moisture transport to the ice sheet. Miocene data and model output will contribute to international community synthesis efforts, and project results will provide critical context for understanding long-term trajectories of global sea level. 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.