University Of Wisconsin-Madison

NIH Research Projects · FY 2026 · 2025-02

Project Summary The objective of this proposed project is to obtain a cellular-level understanding of how nanogenerator (NG)- driven electrostimulation (NG-ES) achieves accelerated wound healing in multiple clinically-relevant models under both normal and ischemic conditions, and identify the optimal signal and device design that will deliver the desired therapeutic effect. In the US, more than 6.5 million people are affected by chronic wounds, resulting in >$25 billion in annual healthcare expenditures. Low-cost, effective, safe, painless, and easily deployed approaches to wound care are urgently needed. Recent studies have shown that ES significantly enhances cellular processes related to wound healing. However, current ES devices for wound healing, due to their complexity in design and operation, are impractical for daily routine use in patients. Leveraging the state-of-the- art wearable NG technology to replace the bulky and rigid battery and related electronics, this project aims to bring the ES technology from a high-cost, high-maintenance technical niche to an over-the-counter disposable Band-Aid®-like bandage. This project is based on an intriguing new discovery from the collaborative work of PIs Wang and Gibson, demonstrating that the NG-ES vastly improved acute wound healing time in both rodent (from 15 to 3 days) and human skin xenograft models (from >30 to 7 days). We further hypothesize that the transient charge-limited electric fields generated by NG-ES are safe and effective for increasing the rate of normal re- epithelialization, recruitment of fibroblasts, extracellular matrix generation, and neovascularization in human skin wound healing. The proposed research is therefore designed to systematically investigate the therapeutic effect for chronic wound healing from the cellular and tissue level to large animal models, and to establish scientific and engineering support to the NG-ES effects. In Aim 1, we will perform in vitro cell culture studies of the major human skin cell types in the wound environment to establish the cellular mechanisms guiding NG-ES wound healing. We will also identify the optimal electric field strength and frequency in these cellular responses, and demonstrate the superior effect of NG-ES compared to conventional ES signals. In Aim 2, we will evaluate NG- ES effects in two complementary human tissue models that mimic the wound healing environment of human skin: whole ex vivo human skin model and in vivo human skin xenograft model, to understand the impacts from the human skin microenvironment, and in a living systemic environment, respectively. In Aim 3, we will scale up and optimize the NG-ES design and integrate with a medical bandage to deliver desired ES signals over a large skin area on pigs and quantify the enhanced wound healing therein. This project will reveal how wounds respond to low-frequency and charge-limited electric fields produced by NGs locally, and provide the preclinical and engineering data necessary to move rapidly into human clinical trials. Success of this research will lead to a new and effective technology for treating both acute and chronic wounds which is low-cost, sustainable and disposable, and thus applicable worldwide, including in austere and resource-poor locations.

NSF Awards · FY 2025 · 2025-02

FoVea is a group whose mission is to create opportunities for all who want to participate in the field of Vision Science. Over the past 10 years, FoVea has launched several initiatives to develop the workforce in the Vision Sciences. The current workshop proposal continues these initiatives. Specifically, this workshop supports (1) a Travel and Networking award program for junior scientists; (2) professional development workshops at the Vision Sciences Society annual meeting; (3) a speaker list highlighting a broad variety of Vision Scientists ; (4) a “playbook” to ensure the longevity of FoVea activities and generational knowledge; and (5) joint activities with Visibility and the SPARK Society to promote the professional development of the Vision Sciences community. The award includes an external evaluation component to assess implementation and stakeholder perceptions of FoVea activities. These efforts complement NSF’s goal of creating opportunities everywhere in STEM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

Project Summary The cell cortex--the outermost part of the cell including the plasma membrane and the underlying cytoskeleton--is extraordinarily important for living systems. As the boundary between the inside and outside of the cell, it must respond to an enormous variety of intracellular and extracellular signals; as the primary motor of cell shape change, it must be capable of rapidly reorganizing to drive diverse processes including cell division, cell locomotion, and cell repair. The biological and medical importance of these cortical capacities cannot be overstated: when functioning properly, they permit proper cell division, morphogenesis, and damage responses; when functioning improperly, they result in unwanted cell proliferation and metastasis, compromised development, and deficits in cell and tissue repair. Many essential cortical behaviors are controlled by the Rho GTPases which, in turn control the cortical cytoskeleton. We have recently discovered that the Rho GTPases and their upstream regulators exert their effects by virtue of their ability to self-organize into periodic cortical patterns of GTPase activity including pulses, propagating single waves, and propagating wave trains. These dynamic, self-organizing patterns are evident in cells of both vertebrates and invertebrates and they direct cell division, cell repair, and long-term cell shape changes. Here we will use new tools and approaches to determine the rules that guide the formation of these patterns and to understand the relationships between specific pattern features and the biological processes they control. This work will provide fundamental insights into the diverse processes that entail cortical reorganization and thus into the many pathologies that ensue when such reorganizations are compromised.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY/ABSTRACT Wilms tumor (WT) is the most common pediatric cancer of the urinary tract. Although the overall prognosis for WT patients is generally favorable after chemotherapy, relapse and deaths still occur in a significant proportion of patients. Therefore, there is a critical need to elucidate the molecular mechanisms underlying the disease causal mutations and develop better treatments. Around 30 gene mutations have been associated with WT. CTR9 germline mutations were identified in some WT families. The mutations caused alternative splicing and deletion of exon 9 of CTR9. However, how one missing exon contributes to the initiation of WT is unknown. CTR9 is the scaffold protein of the PAF complex, which has been well-characterized to regulate multiple steps in transcription. We have modeled the CTR9 exon 9 deletion in an epithelial WT cell line, WiT-49, and found that WiT-49 acquires stem cell features when exon 9 of CTR9 is deleted. When WiT-49 exon 9 deletion cells are implanted into the kidney capsules of mice, they grow more aggressively than parental cells. Gene expression and biochemical analyses uncovered that exon 9 deletion of CTR9 results in profound changes in gene expression and disruption of the integrity of the transcription elongation complex. Flag-CTR9, but not Flag-CTR9exon9, could pull down Elongin A (ELOA) and its associated splicing factors such as SF3B3 and SF3A1. We hypothesize that exon 9 deletion of CTR9 blocks kidney-specific differentiation by dislodging Elongin complex and disrupting transcriptional elongation, resulting in development of WT. We have engineered cell lines to delineate how exon 9 deletion of CTR9 results in the disturbance of transcriptional elongation and alternative mRNA splicing. Our recent studies have shown that CTR9 is essential for precluding the repressive chromatin from spreading by counteracting the activity of EZH2. Therefore, we will also examine if exon 9 deletion of CTR9 acquires stem cell properties to WiT-49 and renders cells sensitive to EZH2 inhibitors and inhibitors of splicing machinery. Successful completion of this project is expected to enhance the understanding of pathogenesis of WT caused by CTR9 mutations and establish the invaluable experimental platform for identifying effective therapies for WT.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY Cell therapy is a rapidly growing field, with Chimeric Antigen Receptor T (CART) cells and stem cell-based therapies showing remarkable efficacy in treating hematologic cancers and regenerating tissue for conditions like Parkinson’s Disease, blindness, type 1 diabetes, and heart failure. However, manufacturing cellular therapies is complex, resulting in high costs and variable product quality. This study aims to address these challenges by developing feedback-controlled closed-loop processes for differentiating human pluripotent stem cells (hPSCs) into cardiomyocytes (CMs). Despite advancements in hPSC-CM differentiation processes over the past decade, enabling their adoption in various applications, including human development studies, disease modeling, drug screening, and clinical trials for heart failure treatment, significant variability exists between batches and cell lines during manufacturing. This variability becomes more pronounced with the transition from 2D to 3D platforms. Building on preliminary data comparing successful and failed hPSC differentiation batches that identified genetic and epigenetic features predictive of CM generation, notably variations in WNT and MAPK signaling in cardiac progenitor cells (CPCs), this study aims to develop control strategies to enhance differentiation reproducibility across various conditions. This project's overarching goal is to improve the robustness and reproducibility of hPSC differentiation into CMs through feedback-controlled closed-loop manufacturing processes. Leveraging single-cell transcriptomics and epigenomics, our project will gain a deep understanding of differentiation failure modes, enabling early identification and correction of process deviations. The proposed aims are: Aim 1: Identify 2D CM differentiation failure modes through integrated single-cell transcriptomics and epigenomics. Single-cell RNA sequencing (scRNAseq) and transposase-accessible chromatin sequencing (ATACseq) performed throughout CM differentiation runs and analysis of cell trajectories will identify key points of deviation from differentiation and predict mechanism leading to off-target cell-type generation. Aim 2: Develop 2D closed-loop CM differentiation processes and evaluate their robustness compared to standard protocols. We will build genetic and epigenetic sensor hPSC lines to monitor gene expression and chromatin accessibility at the single-cell level, test modulation of cardiac developmental pathways to rescue low efficiency CM batches, and compare the robustness of closed-loop processes to standard protocols. Aim 3: Develop scalable, 3D closed-loop CM differentiation processes and assess process variability. Utilizing a 3D bioreactors for CM manufacturing, we will compare failure modes in 3D to 2D differentiation, develop cell sensors and processes to modulate developmental signaling pathways, and increase the reliability of CM production in scalable manufacturing platforms.

NIH Research Projects · FY 2026 · 2025-02

PROJECT ABSTRACT RNA condensates, also known as membraneless organelles or RNA granules, are dynamic concentrations of protein and RNA that spontaneously coalesce in the absence of a limiting membrane within the cytoplasm or nucleoplasm through liquid-liquid phase separation. Eukaryotic cells harbor various RNA condensates, including nucleoli, processing bodies, stress granules, and germ granules. Functioning as distinct states of liquid, these droplets and their surrounding cytoplasm play a crucial role in concentrating reactants for cellular organization and facilitating specific biochemical reactions during development. RNA, a critical component of RNA granules, can promote or inhibit condensation, alter material properties and substructure organization, and modulate interactions with surfaces. DEAD-box RNA helicases perform ATP-dependent RNA remodeling activities and have been shown to regulate RNA condensates, but the mechanistic details connecting RNA, RNA chaperones, and biological phenotype are largely unexplored. As interest in RNA condensates as an organizing principle in the cell has grown, concerns regarding the evidence supporting their biological significance have emerged. A key challenge for the field is developing technical approaches to measure and manipulate condensates in biological systems and physiologically relevant biochemical reconstitutions. In this proposal, we will investigate the regulation of germ granules, an RNA condensate implicated in germ cell totipotency. Germ granules in C. elegans, have proven a valuable model for understanding RNA condensate properties and biological functions. RNA helicases are critical and conserved regulators of germ granule structure and function in animals including humans. The proposed research aims to establish a framework for understanding the regulation of condensates by RNA helicases and the impact of condensation on RNA helicase activity. To achieve these goals, we will combine analysis of condensates in an animal model with biochemical reconstitution to investigate the molecular mechanics that underpin condensate regulation, dynamics, and function in native cells. We will employ rigorous testing of emerging models through detailed in vitro and quantitative cell-based assays using techniques including genome editing, condensate biophysical measurements, quantitative super-resolution microscopy, and RNA biochemistry. This project uniquely combines our multidisciplinary expertise to unravel fundamental cellular mechanisms and provide new insights that will enable the identification of novel therapeutic targets for condensate-related diseases.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) is the leading cause of dementia affecting nearly 7 million Americans, and the number of affected individuals is projected to rise as the nation ages. In addition to the defining features of amyloid and tau pathology, synaptic loss associates with cognitive dysfunction in AD, with postmortem investigations revealing substantial reductions in synaptic density among people with dementia, and studies suggesting this process may be linked with tau pathology. However, the accrual of AD pathology and associated synaptic loss has been difficult to measure in living humans across the AD clinical and biological continuum. Thus, this F31 proposal aims to address this gap using in vivo measures of tau pathology secretion and neuroimaging of synaptic density to better understand the biologic and clinical progression of AD. My proposed aims are to 1) determine the extent to which tau pathology secretion associates with synaptic loss among richly characterized participants on the AD continuum using blood biomarkers of AD and synaptic positron emission tomography (PET), and 2) determine the regional relationship between tangle pathology and synaptic density using tau PET and synaptic PET. This project will leverage the resources of the NIA-funded Wisconsin Alzheimer’s Disease Research Center. The sample will include cognitively unimpaired AD biomarker negative and positive participants, as well as individuals with mild cognitive impairment and participants with dementia due to AD. The project leverages sophisticated and promising measures, including a novel plasma biomarker of brain-derived tau pathology, hyperphosphorylated tau at amino acid residue 217 (pTau217) and [C-11]UCB-J, a PET radioligand that binds to synaptic vesicle glycoprotein 2A (SV2A). This synaptic vesicle protein is found in presynaptic nerve terminals throughout the brain and is a promising target for studying synaptic loss in individuals across the AD continuum. The proposed study is expected to address gaps in knowledge about the progression of AD and the factors that contribute to cognitive decline, and inform future intervention studies to mitigate cognitive decline in AD. Finally, my mentorship team and I have developed a comprehensive and integrated training plan for this fellowship which will support my research training as a committed predoctoral student from a community which is underrepresented in the biomedical research workforce.

NIH Research Projects · FY 2026 · 2025-02

Project Summary: Mechanical forces are critical in diverse biological processes, including coagulation, cancer metastasis, embryonic development, and immune function. Individual receptors exert forces at the piconewton scale, one trillionth the force required to lift an apple. Our lab seeks to develop novel “mechanoimaging” technologies that convert receptor level forces into visible light via fluorescence. We also seek to use programmed cellular forces to control biology, with a special focus on regulating the function of immune cells. Here, we propose to quantify and control receptor-level forces in viscoelastic materials. Despite their importance to biology, receptor-level forces have not been measured in viscoelastic materials. This is a fundamental limitation in the field of mechanobiology because biological tissues are viscoelastic, meaning they exhibit time-dependent deformation under applied force. With our mechanoimaigng technology, we will determine the magnitude and distribution of forces that cells use to interact with and physically remodel viscoelastic biomaterials. By measuring fibroblast and cancer cell forces transmitted to viscoelastic biomaterials, we will learn how cells physically remodel and migrate within the viscoelastic extracellular matrix. Additionally, we will seek to answer key questions in T cell immunology. Reductionist in vitro experiments reveal that T cells transmit forces via their T cell receptor and that these forces are critical for T cell activation. Whether T cells transmit forces that facilitate activation in viscoelastic materials is unknown. We seek to test the hypothesis that viscoelasticity produces defects in T cell mediated cytotoxicity in diseases such as cancer. To address this hypothesis, we will measure T cell forces in viscoelastic materials, quantifying both cell- material and cell-cell forces. Finally, we cannot always engineer the mechanical properties of a tissue or a cell. Accordingly, this proposal aims to engineer cells to exert strong forces even in viscoelastic materials via creating Artificial Mechanoreceptors that circumvent normal mechanoregulation, a capability with implications for optimizing cancer immunotherapy.

NSF Awards · FY 2025 · 2025-02

PART 1: NON-TECHNICAL SUMMARY Large Language Models (LLMs) are an advanced artificial intelligence method to generate innovative ideas from the processing of copious volumes of information at speeds for which human engagement is simply infeasible, if not impossible. This research project focuses on developing a framework for extracting valuable information from non-text-based, sources to improve, grow and enhance LLMs with a focus on the design of new metallic systems with superior properties and performance. By helping LLMs to ingest and learn from data formats such as graphs, tables, microscopy images and other rich, layered and complex scientific data, this project is using LLMs to uncover relationships in materials that might be overlooked or never discovered by relying solely on textual data from scientific papers, written studies or textbooks. This work supports the realization of broadly democratizing the design process for materials investigations while fostering innovations that could surpass materials performance limits as commonly held today. In this way, this work aligns well with NSF’s mission to promote the progress of science. This project also coincides with the goals of the U.S. Materials Genome Initiative by harnessing the power of materials data and developing a skilled workforce to drive innovation and strengthen U.S. competitiveness in materials science. PART 2: TECHNICAL SUMMARY This project aims to establish an effective framework to ingest, inform and leverage multimodal data directly from experiments to advance large language models (LLMs) for hypotheses generation toward the design of new and superior metallic alloys. LLMs have significant potential to produce novel and innovative design hypotheses by integrating textual information across extensive domains of literature at speeds and volumes far beyond human capacity. A even more revolutionary opportunity however, lies in expanding the variety of formats of information available for LLMs to learn from. Presently, LLMs are restricted largely to data contained in text. This research project is actively expanding this also include the rich, complex, layered and multi-modal data found in materials science experiments including, but not limited to: segmented, annotated, quantified and labeled micrographs from SEM and TEM; x-ray diffraction patterns; graphical constructs; and even images. To achieve this aim, this project is building and training enhanced LLMs for the generation of metallurgical design inquiries through the use of high-quality, high provenance, context-rich, metallurgical dataset standards derived from a variety of sources including the research team’s very own experimental data and datasets obtained from open-source literature with all the metadata and context required to ensure high confidence in interpretability and repeatability. This work supports the realization of broadly democratizing the design process for materials investigations while fostering innovations that could surpass materials performance limits as commonly held today. In this way, this work aligns well with NSF’s mission to promote the progress of science. This project also coincides with the goals of the U.S. Materials Genome Initiative by harnessing the power of materials data and developing a skilled workforce to drive innovation and strengthen U.S. competitiveness in materials 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 · 2025-01

Stochastic optimization is a fundamental research discipline and a workhorse of learning algorithms. It addresses problems that are stochastic—or random—in nature, such as those arising in the training of machine learning models. The existing theory underlying most learning and optimization algorithms often relies on the simplifying assumption that the data examples observed during training are representative of the data on which the model will be tested or deployed. However, this assumption rarely holds in practice. For example, a facial recognition model trained on broad U.S. data may exhibit varying performance across states with differing demographics, raising concerns about both accuracy and fairness. Similarly, in e-commerce, customer behavior can shift dynamically in response to pricing strategies. This research aims to develop robust algorithms capable of handling these dynamic and uncertain data scenarios. The work will advance optimization techniques to address fundamental supervised learning tasks, yielding algorithms with provable error guarantees that are both computationally and data efficient. These advances will enhance our understanding of learning in dynamic and uncertain data environments, which are central to modern machine learning. Broader impacts include fostering cross-disciplinary collaborations, mentoring students, and organizing a workshop to engage diverse early-career researchers, thereby supporting education, diversity, and innovation in science. The project will develop new optimization-inspired algorithms to address learning under two models of distributional shifts, forming two main research thrusts. The first thrust focuses on scenarios where the training and testing data distributions differ, studied through the distributionally robust optimization framework. The goal is to train learning models that perform well under worst-case test scenarios within a predefined ambiguity set. By leveraging the structured nature of fundamental tasks in regression and classification, this research aims to address limitations of existing approaches, which often rely on overly general assumptions and produce overly pessimistic results. The second thrust addresses situations where data distributions shift in response to the trained model, such as in performative prediction settings. The key challenge is to achieve stability under these shifts, which translates into solving stochastic fixed-point equations and nonconvex optimization problems. The focus is on advancing algorithmic techniques to tackle structured learning problems, providing guarantees for tasks such as learning single-index models. The proposed research is expected to contribute novel algorithmic techniques in stochastic optimization and learning theory, particularly in areas such as min-max optimization, stochastic fixed-point problems, and learning under label noise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Although more than 88,000 patients in the US are waiting to receive a kidney transplant, each year we discard more than 4,900 deceased donor kidneys. Many of the discarded kidneys are imperfect but still offer a survival advantage compared to continued dialysis. Transplantable organs are often discarded due to patient and physician decisions to decline offers. However, systematic human errors in decision-making, known as cognitive biases, can negatively impact these decisions. Understanding the role that cognitive biases play in decisions to accept or decline organs can help identify strategies to improve patient and physician decision- making, decrease the organ discard rate, and optimize outcomes for patients awaiting transplantation. The proposed project is a 5-year Mentored Patient-Oriented Research Career Development Award (K23) that responds to PA-20-206 and will support the career development of Carrie Thiessen, MD, PhD. The project has three specific aims. The first two aims will characterize the status quo bias that may cause patients to underestimate the risks of continued dialysis relative to transplant. The third aim examines the extent to which physicians experience a bandwagon effect that leads them to overvalue other clinicians’ assessments of organ quality. For Aim 1, we will interview patients who are being re-evaluated to stay on the kidney transplant waiting list to determine how worsening health status affects their decision to decline consent to imperfect kidney offers. For Aim 2, we will use national registry data to characterize waitlist patients who continue to decline consent to imperfect organ offers as their health worsens. We will explore the impact of transitioning from non- consent to consent to imperfect organ offers on the likelihood of transplantation, death on the waiting list, and delisting. For Aim 3, we will determine the effect of knowledge of prior declines on transplant surgeons’ organ acceptance rates by conducting a single-center pilot study that randomizes actual organ offers to presentation with the number of prior declines (standard-of-care) versus temporary masking of the number of prior declines (intervention). Under the guidance of a multi-disciplinary team of skilled mentors, Dr. Thiessen will supplement this mentored research with formal training in behavioral economics, advanced qualitative methods and biostatistics, and interventional study design. As a transplant surgeon and bioethicist at an academic institution with an outstanding research environment and a track record of successfully supporting early-stage surgeon-scientists, Dr. Thiessen is well-positioned to conduct this project. This K23 will provide Dr. Thiessen with the protected time, mentorship, and training to achieve her long-term goal of becoming an independent health services researcher who optimizes outcomes for patients with end-stage organ disease by improving patient and physician decision-making frameworks.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY/ABSTRACT Hereditary peripheral neuropathies (HPNs) are common disorders that involve degeneration of nerves in the peripheral nervous system. Common clinical signs include progressive muscle weakness, foot deformities, and sensory deficits. There are no FDA-approved treatments for HPNs. Spontaneous large animal models of neurodegenerative diseases have consistently proven valuable. Domestic dogs are excellent models of human disease because they cohabitate with humans, their genomic features are highly conserved with humans, their peripheral nerves have similar metabolic requirements to humans, and they have an aging phenotype that mimics humans. Late-onset peripheral neuropathy (LPN), a spontaneous degenerative HPN that is common in Labrador Retrievers, is poised to be an excellent spontaneous canine model for human axonal HPNs. To initiate the use of this model for fundamental research and pre-clinical trials, further investigation into its pathologic basis is needed. Thus, we propose to further characterize the neuropathologic and molecular features of LPN to establish it as a spontaneous canine model for human axonal HPNs. The current known neuropathologic features of Labrador LPN align with human axonal HPNs, although the neuromuscular junction is a key feature that has not yet been researched. In Aim 1a, we will investigate the neuropathologic architecture of the neuromuscular junction and corresponding peripheral nerves in LPN-affected Labradors and controls. We have identified a novel variant in a robust candidate gene that strongly associates with LPN-affected Labradors, but the mechanism by which this variant contributes to axonal degeneration is unknown. In Aim 1b, we will evaluate the functional effect of this variant on its gene and protein product. Uncovering the molecular mechanisms contributing to axon degeneration in Labrador LPN is essential in identifying overlapping treatment targets between Labrador LPN and human axonal HPNs. In Aim 2, we will identify abnormalities associated with Labrador LPN using a novel induced-motor neuron model. The training plan proposed under this award involves extensive individualized training in research techniques, conceptual knowledge, scientific communication, responsible conduct of research, collaborative science, clinical neurological and surgical skills, and professional development. My sponsors’ laboratories, the Comparative Biomedical Sciences graduate program, the dual DVM/PhD program, and the University of Wisconsin-Madison School of Veterinary Medicine will provide me with an outstanding opportunity to undertake research and training that will form a solid foundation towards my goal of becoming an independent veterinary clinician scientist. The work performed under this award will contribute to the establishment of Labrador LPN as a spontaneous canine model for human axonal HPNs. In the future, this model will be used as the critical link to human clinical trials, serving to assist in the discovery of a treatment for this disorder.

NIH Research Projects · FY 2026 · 2025-01

Project Summary Despite the great success of immune checkpoint inhibitors in treating a subset of cancer patients by blocking PD1/PDL1 pathway, the treatment outcomes in triple-negative breast cancer (TNBC) remain dismal partially due to the immunosuppressive microenvironment and impaired T cell infiltration in the TNBC. We recently developed a platelet-mediated immune checkpoint inhibitor delivery system (designated P-aPDL1) that could efficiently target the post-surgical tumor site and bioresponsively release aPDL1 in the format of platelet-derived microparticles upon the activation of platelets at the tumor site. We have demonstrated the enhanced treatment efficacy against post-surgical B16F10 melanoma and 4T1 TNBC tumor recurrence, while treatment outcomes on the intact non-immunogenic 4T1 TNBC without pre-treatment with surgery remain to be improved. In this proposal, we intend to develop multi-faceted strategies to enhance the treatment efficacy of P-aPDL1 against TNBC through (1) increasing the tumor-selective P-aPDL1 accumulation by triggering a local thrombus formation at the TNBC site; and (2) modulating the immunosuppressive tumor microenvironment through depleting tumor- associated macrophages (TAMs). Specifically, in AIM 1, we will apply a truncated tissue factor-RGD (tTF-RGD) peritumorally or systemically to trigger the local thrombus formation in the tumor tissue and attract the accumulation of P-aPDL1. Both 4T1 TNBC primary and metastatic tumors will be used to evaluate the treatment efficacy. In AIM 2, we will intratumorally inject TAMs-depleting nanoparticles (PLX-NP) to modulate the immunosuppressive microenvironment to facilitate the infiltration of effector T cells to synergize with P-aPDL1 for enhanced treatment efficacy against TNBC. In AIM 3, the combination treatment strategies identified in AIM 1-2 will be evaluated on a fully validated patient-derived xenograft (PDX) TNBC humanized mouse model. This proposal will develop a suite of new combination immunotherapy strategies to increase the anti-tumor efficacy against TNBC and provide significant insights into advancing the current immunotherapeutics for treating other non-immunogenic tumors in the clinic.

NSF Awards · FY 2025 · 2025-01

NON-TECHNICAL SUMMARY This award, provides support for two summer-long workshops, entitled the Solid State and Materials Chemistry Collaboration Incubator (SSMC-CI), that merge the advantages of in-person hackathon-style events and longer-term residential programs to more effectively address materials science problems and nucleate new methods and techniques. The on-going and rapid evolution of artificial intelligence and large-scale computations can be witnessed in many areas of everyday life, from the way advertisements are tailored to individual users on the Internet to the generation of images and text from simple prompts. These advances also have the potential to transform the methods by which scientists design and discover new materials to meet the technical and environmental challenges the nation faces today and will meet in the future. The expertise needed to adopt data science and computational techniques, however, is quite distinct from that used by researchers skilled in the synthesis and analysis of new substances, which in turn are different from the skills needed to measure materials performance. SSMC-CI aims to lower the barrier for materials scientists and data scientists to collaborate. It brings together teams of scientists from a range of backgrounds to collaborate on pressing problems in the areas of solid-state compounds and materials. Each workshop begins with an initial 3-day in-person session to jump-start interactions among team members followed by a program structured around online meetings to sustain and nurture these collaborations, with the goal of creating multidisciplinary teams that have a track-record of progress on scientific questions that are challenging to pursue through traditional approaches. The award is supported through the Solid State and Materials Chemistry program and the Condensed Matter and Materials Theory program, both in NSF’s Division of Materials Research. TECHNICAL SUMMARY Many of the most pressing challenges in solid state and materials chemistry call for an integrated, multidisciplinary approach that combines expertise in materials synthesis, properties characterization, theoretical analysis, computational simulation, and data-science methods. The formation of collaborative teams to meet these challenges encounter the difficulty of bringing people with the right combination of skills together and developing sustained interactions among them. This project, which is supported through the Solid State and Materials Chemistry program and the Condensed Matter and Materials Theory program, both in NSF’s Division of Materials Research, establishes two Solid State and Materials Chemistry Collaboration Incubator (SSMC-CI) events as annual summer workshops to support and nurture the nucleation and growth of such teams, in a manner that merges the advantages of in-person hackathon-style events and longer-term residential programs. Each the SSMC-CI workshop follows a program of the form: (1) a call for proposals of research problems and applications to participate, from which teams are formed, (2) an in-person hackathon in which teams begin to work on their respective projects, as well as receive training in data-science methods, (3) a series of online sessions, during which teams report on their progress and plans for next steps, and (4) a virtual symposium for the teams to make final presentations of their results and lessons learned from their 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 · 2025-01

Understanding what drives the evolution of new species is a central question in biology. Groups of species that have recently evolved provide a good system for trying to understand the genetic changes that led to establishment of new species. This research combines the fields of genomics, developmental biology, ecology, and physiology to examine a new lineage of flowering plants in Hawaiʻi in the genus Bidens (family Asteraceae). The project will generate new genome assemblies and experimentally identify the genetic and developmental changes responsible for leaf, fruit/seed, and flower evolution in this group of species. This project will also provide training in inter-disciplinary evolutionary concepts and approaches for undergraduates, graduate students, and postdoctoral researchers, including those from underrepresented groups; improving the scientific workforce in the United States by preparing them to strongly contribute to scientific research, education, and/or technological advancements. This project will use newly developed genome sequencing methods to infer the broader evolutionary history of Polynesian Island Bidens, along with continental relatives. The updated understanding of how Bidens reached remote Pacific islands and diversified will provide the backbone for comparative evolutionary genomics of our six target species (three Hawaiian endemics and three continental). Comparing these genome sequences and differences in gene expression will allow us to identify the genetic changes that contribute to the unique ecological and morphological diversity of the Bidens adaptive radiation. Concurrent with the other objectives of the project, undergraduate students at UH Mānoa (a Native Hawaiian serving institution) will receive year-long internships in Hawaiʻi and short-term exchanges at Auburn (AU) and Wisconsin (UWM) via AHi-WiRE; Auburn-Hawaiʻi-Wisconsin-Research Exchange to receive training in plant evolutionary genomics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary Metastatic castrate resistant prostate cancer (mCRPC) is the lethal form of the disease, and while multiple therapies such as androgen signaling inhibitors (ARSIs) are available, drivers of resistance such as alterations in androgen receptor and lineage plasticity, including shifts to neuroendocrine prostate cancer (NEPC), are common. A major unmet clinical need in mCRPC is longitudinal monitoring to continually assess for molecular transitions that drive treatment resistance. Current clinical monitoring with PSMA imaging or PSA levels can detect progression, but do not provide the underlying molecular mechanism, which is needed to rationally select the next line of therapy. These methods are also less effective as androgen independence and lineage plasticity emerges. Fragmentomics provides a liquid biopsy approach using fragments of circulating tumor DNA (ctDNA) on standard commercial cancer gene panels. Paired with machine learning, it can be used to monitor for the transition between prostate adenocarcinoma and NEPC, complementing standard variant identification. This innovative integrated method provides tremendous performance, logistical, and cost benefits compared to separate assays. This study will investigate ARSI response and resistance in multiple prospective trials using three specific aims. 1) It will determine if baseline ctDNA fragmentomics scores are prognostic biomarkers in mCRPC in comparison to ctDNA fraction and other clinical variables; 2) It will detect the emergence of diverse ARSI resistance mechanisms in longitudinal ctDNA that are independent of androgen receptor or NEPC- associated DNA alterations to assist in treatment decisions; and 3) It will interrogate ctDNA fragmentomics biomarkers in the Alliance A031201 trial to determine their prognostic value and ability to identify emergence of ARSI resistance and biomarkers of dual ARSI response. Utilizing a single unified liquid biopsy assay powered by machine learning provides the maximum data for each of these aims in a cost-effective manner. In addition, a single targeted panel ctDNA sequencing assay allows for maximal use of a plasma sample, as splitting a sample for multiple assays can decrease the sensitivity of each, especially at very low ctDNA quantities. Thus, successful implementation of these aims would revolutionize the ability to monitor and make treatment decisions in mCRPC and could serve as a model for such work for other cancers.

NSF Awards · FY 2025 · 2025-01

Coastal flooding in the Great Lakes region poses significant risks to communities, infrastructure and ecosystems due to fluctuating lake levels, heavy rainfall, and coastal erosion. These stressors that are expected to worsen with climate change are already overwhelming stormwater systems and damaging property. They will also disproportionately affect communities suffering from historical and ongoing socioeconomic disparities and environmental injustices. This project aims to enhance the resilience of Great Lakes coastal cities by co-producing climate information with city practitioners and community-based organizations (CBOs) that can support their decisions to better prevent, prepare and adapt to these stressors. This will be achieved by creating an online participatory tool that integrates climate, hydrological, spatial data and participatory GIS. This decision support tool (DST) called Participatory Urban Modeling and Climate Projections for Community-Driven FlOoding Resilience (PUMP-COR) will be developed with participants in two Great Lakes cities: Benton Harbor (MI) and Milwaukee (WI). It will allow participants to better understand and visualize their risks and make choices that can influence the implementation of solutions. By directly engaging CBOs and city practitioners, this project will broaden and diversify participation in both cities and cultivate more inclusive development decisions that can be generalized to other decisions and geographies. Specifically, the project will: 1) explore the viability of integrating three existing modeling efforts (across climate, hydrological and spatial information) to inform decision-making that builds the resilience of households, communities, and cities to flooding risks; 2) organize focus groups with CBOs and city practitioners to better understand different definitions and perceptions of resilience, risk, equitable and just solutions, and to provide feedback to each other and to the research team about their preferences and aspirations for PUMP-COR; and 3) build a network of researchers, CBOs, city practitioners, professional associations, and regional organizations to engage in the co-production process for the ongoing project and for a future broader proposal based on what can be learned from the planning grant. This broader proposal and engagement with communities will focus on how online DSTs can increase the number of people, communities and cities using PUMP-COR to build resilience of coastal cities. The outcomes will include improved decision-making for flood resilience, enhanced community participation, and better climate information tailored to the needs of diverse decision-makers. This research will significantly contribute to the fields of climate modeling, participatory GIS, and the science of actionable knowledge, offering innovative solutions for climate adaptation and resilience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY The quantity and quality of daily care needed by children with neurologic impairment (NI), i.e., a neurologic disorder with functional and/or intellectual disabilities, averages >50 hours/week and surpasses what is considered safe for one individual. Without support to accomplish these tasks, severe health consequences can occur, including severe illness exacerbations requiring hospitalization, missed school, lower quality of life, and financial and social hardship. In fact, 25% of all hospital days in children’s hospitals are for children with NI. To address these realities, families of children with NI attempt to create caregiving networks, e.g., extended family, home nurses, respite providers, school aides, etc., to meet their extensive and technical care needs. However, families must develop their own systems to identify, recruit, and manage network members to ensure safe and high-quality daily care for their child. To maximize health in this population, there is an urgent need to understand how caregiving networks assemble and operate to influence child health outcomes. Data from our pilot work indicates that smaller, denser networks, with more triadic closure (e.g., closed communication loops and professional caregivers correlate with fewer serious health events, defined as death or hospitalization. Further, our qualitative work indicates that the caregiving networks for children with NI are potentially modifiable through network-based interventions. Thus, to maximize health there is an urgent need to understand how caregiving networks influence child health outcomes. To achieve our long-term goal to improve the health of children with NI through optimized caregiving networks, we propose to conduct essential Stage 0 research (NIH Stage Model for Behavioral Intervention Development) using a mixed methods approach to understand how caregiving networks influence health outcomes for children with NI. We will use standard ego-centric social network approaches to identify the contributions of caregiving network characteristics to serious health events and child quality of life. We will also conduct qualitative interviews to identify barriers, facilitators, and consequences of caregiving network characteristics for this population. If successful, this Stage 0 research will be immediately followed by Stage 1-3 research to generate, adapt, and pilot test an intervention to promote caregiving network function and improve the health of children with NI.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Filamentous fungi are a major source of valuable natural products (e.g. antibacterial penicillin, the immunosuppressant cyclosporin, the cholesterol-lowering drug lovastatin, the fungicide azoxystrobin and the pesticide paraherquamide). Yet despite progress in genomic sequencing and the characterization of large numbers of fungal NPs, the identification of small molecules with genuinely new structures and activities has not kept pace with genome sequence. These key gaps are a consequence of current bioinformatic and genome mining procedures which are biased towards locating well-characterized classes of NPs, which follow the assumption that all genes important for synthesis of any particular NP will be clustered and lack consideration of how fungal ecology can inform chemical synthesis. We have addressed these challenges by developing the first genome-mining pipeline capable of identifying the new widespread chemical class of natural products in fungi, the isocyanides and uncovering the unexpected role of copper in regulating both isocyanide synthase (ICS) biosynthetic gene cluster (BGC) expression and compound production. To continue these advances, here we focus on three key areas of investigation including (i) elucidating products of ICS BGCs containing genes encoding unusual enzymatic features unique chemistries and/or target proteins, (ii) examining the hypothesis that isocyanides direct microbiome composition dependent on metal concentrations and (iii) exploring the potential of computational innovation to significantly advance an understanding of the foundations of fungal NP logic. Overall, this work will advance the field of NP discovery in new directions that includes promoting the chemistry and ecology of the poorly understood isocyanide NPs, dissecting consequences of NP elaboration on microbiome composition and aligning molecular and chemical proficiencies with computational innovations for uncharted NP discovery routes. Using molecular, chemical, ecological and computational tools, we are poised to uncover fundamental questions in complex fungal networks governing NP synthesis. Better understanding of these mechanisms is critical to elucidating NP classes with specific biological roles and, ultimately, to provide knowledge to address escalating direct and indirect challenges confronting human health worldwide.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract Feeding processes are intertwined in a number of human health investigations, though the precise mechanisms through which these operate remains elusive. Neuropeptides are signaling molecules that provide a dynamic fingerprint of neuronal function, serving as a rich resource for pathway elucidation and biomarker discovery. Despite this potential, neuropeptide analyses are met with many hurdles, stemming from their low in vivo abundance and a lack of computational resources for confident identification and accurate quantification. It is also imperative that the investigation of feeding processes through neuropeptides is conducted in a global and unbiased manner, as neuropeptide activity is heavily co-modulated by other neuropeptides. My proposed aims to bridge these gaps to elucidate feeding mechanisms through holistic investigation of the neuropeptidome. This task will be accomplished through use of a crustacean model organism, Cancer borealis, revered as the premier model for neuroendocrine investigations of this nature for its simpler, yet well-characterized, nervous system with established neuropeptide homology to humans. A multipronged approach through strategic employment of data-independent acquisition (DIA) mass spectrometry, key for overcoming the inherent bias of the mass spectrometer toward highly abundant analytes, will be used to profile and subsequently quantify neuropeptides from C. borealis neuroendocrine tissue extract. As neuropeptide identifications are not immediately amenable to existing software packages designed for protein analysis, partially due to the endogenous nature in which they are analyzed, a software suite, named EndoGenius, will be designed and optimized specifically for identification and quantification of neuropeptides obtained from DIA experiments (Aim 1). In parallel, a spectral library of crustacean neuropeptides will be built for identification of neuropeptides from DIA experiments, a strategy to maximize the extent of neuropeptide elucidation and overall coverage (Aim 2). To improve resolution of neuropeptide abundance alterations and enable multiplexed analysis, isobaric tagging of neuropeptides will be utilized together with DIA mass spectrometry, providing a complete picture of neuropeptide profile changes in response to feeding (Aim 3). Each aim is designed for specific application to feeding samples, collected at four time points, contributing to the overall neuropeptidome associated with these critical behavioral functions. I hypothesize that these methods will comprehensively resolve the temporal profile of neuropeptidome changes induced following feeding activity with high levels of quantitative accuracy. Following these advancements, the findings can be used to elucidate feeding regulatory mechanisms as well as neuropeptide biomarkers that are associated with feeding disorders.

NSF Awards · FY 2025 · 2025-01

Climate change is exacerbating the frequency and severity of extreme phenomena on the Earth’s surface, including melting of arctic ice and thawing of permafrost in cold regions around the world. Mitigation of these hazards requires an improved fundamental understanding of soil water freezing and thawing phenomena, which will ultimately advance our ability to predict and model soil water freeze and thaw behavior. This project seeks to develop a new paradigm for quantifying unfrozen soil water content in freezing environments. Improved basic understanding of soil water freezing and thawing behavior will impact a broad range of hydrologic science applications, including the hydro-mechanical behavior of permafrost, land settlement, soil erosion, and land sliding. Education and outreach initiatives will be integrated with the research to capitalize on the collaborative nature of the project, including a summer student exchange program and a continuing education webinar series hosted by an industrial collaborator. A new theoretical framework based on a generalized soil water potential that includes both capillarity and adsorption will be formulated to quantify local pore water pressure distribution and corresponding phase change phenomena in soil. Modern laboratory approaches will be advanced to measure soil water isotherms defining the constitutive relation between water potential and water content and soil freezing curves defining the constitutive relation between soil temperature and unfrozen soil water content. Outcomes from the effort will include: clarification of fundamental relationships among soil water potential, soil water retention, and soil water freezing/thawing for a wide range of soil types and pore fluid chemistries; generalized closed-form equations for modeling soil freezing and soil thawing curves; and practical predictive relationships that may be adopted to estimate soil freezing and thawing behavior from fundamental soil properties including specific surface area and cation exchange capacity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary Cells have a dynamic and reciprocal relationship with their extracellular matrix (ECM). Cells synthesize, degrade, and rearrange local ECM proteins and molecules even as the physical properties of the ECM, (including its stiffness, density, and orientation), regulate cell phenotype, behavior, and communication. Disease and injury disrupt this dynamic relationship causing cells to produce local patterns of ECM remodeling that can be pathologic, such as fibrosis, or inadequate, leading to tearing or a persistent wound. Understanding ECM heterogeneity represents a key opportunity to enhance knowledge of how disease and injury develop and progress. Therefore, my research program focuses on increasing the rigor and reproducibility of approaches to capture ECM remodeling. Specifically, we ask (1) how do ECM geometry and structure vary regionally at multiple scales? and (2) how does heterogeneity influence a tissue’s local and global load-bearing function? My research program answers these questions by addressing critical limitations in the visualization and mechanical characterization of heterogeneous ECM. Although groundbreaking techniques have emerged that enable micro and even nano-scale ECM imaging, a crucial tradeoff is field of view or sample size. For example, laser scanning microscopy provides unparalleled visualization of collagen, the most abundant ECM protein in the human body, capturing fiber dimensions and morphology with clarity far beyond traditional microscopy. However, it covers, at most, a couple hundred microns in each direction. Therefore, a critical challenge in applying high-resolution imaging to heterogeneous pathologic samples involves selecting this micron-sized imaging window from the centimeters of available tissue. Our group is at the forefront of increasing rigor and repeatability in this field, advancing low-resolution imaging, image segmentation, and analysis automation. Another barrier to investigating ECM heterogeneity is the lack of experimental protocols and analysis tools to quantify spatial variations in mechanical properties . Inverse methods have addressed technical challenges in this area but face limited adoption due to their complex mathematical techniques and specialized codes. We will address this gap using a novel experimental system to generate a repository of full-field measurements on a range of heterogenous samples. Then, we will create sample-specific finite element models using a popular open-source software and encourage the inverse mechanics community to outperform us. To study pathologic remodeling under highly controlled conditions, we will also build a specialized clamping system that connects cell-seeded tissue-engineered constructs to our mechanical and quantitative polarized light testing systems. Our research program addresses specific challenges in quantifying regional differences in ECM remodeling to address fundamental questions about how pathology develops and progresses. Ultimately, insights from this program will improve disease detection and prognostication as well as localized treatment.

NSF Awards · FY 2025 · 2025-01

Many biological and physical systems exhibit high degrees of internal complexity that resist direct mathematical description. Examples include ecological dynamics in a varied environment and fluctuating flame fronts in combustion. Nonetheless, such systems often exhibit tractable behavior when viewed at large or fine scales. This "asymptotic" behavior plays a major role in applications, and often has a universal character that unites the study of disparate systems. In this project, the principal investigator (PI) will combine several mathematical methods to identify and justify asymptotic phenomena in partial differential equations (PDEs) originating in the sciences. This work has the potential to shed light on a variety of systems including ecological invasion, atomic deposition, and fluid shock formation. The PI is committed to undergraduate and graduate mentorship, with the particular aim of supporting students from underrepresented backgrounds. This project will explore the asymptotic behavior of various deterministic and stochastic PDEs in significant limiting regimes. The project comprises three interconnected lines of work. (1) The PI will study the long-time propagation speed and front structure of solutions to reaction-diffusion equations in heterogeneous and random environments. This investigation encompasses a dual analysis of associated branching particle systems. (2) The PI will combine analytic and probabilistic methods to study long-time and white-noise limits of several physically motivated stochastic PDEs, including stochastic conservation laws and stochastic heat equations near criticality. (3) The PI will investigate the action of weak viscosity on internal shock formation in the compressible Navier--Stokes equations. This involves a delicate coupling between hyperbolic and parabolic approximations. 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 · 2025-01

The broader impact of this I-Corps project is the development of agricultural sensors which are capable of directly measuring plant-available nitrate at the plant root zone in real-time throughout the growing season. This ability allows real-time decisions to be made regarding fertilization rates, including using spatially and temporally varying rates. Potential target customers include corporately owned farms, soil science researchers, and fertilizer suppliers. These companies have a commercial interest in knowing the nutrient profile dynamics within soil, and the solution can help achieve real-time continuous monitoring. Optimizing fertilizer usage may provide economic benefit through reduced overall costs, particularly for corporate farming. Additionally, reductions in nitrate waste are also beneficial for the environment, allowing for more sustainable business practices. 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 electrochemical sensors which can operate in soil environments. The solution introduces a nanoporous hydrophilic membrane that serves as a selective barrier, filtering out soil particulates while allowing nitrate ions to reach the sensor surface, minimizing interference from other soil components and ensuring a more accurate and consistent nitrate concentration measurement. Additionally, the in-situ soil sensor provides real-time soil nitrate concentrations measurements, enabling immediate insights into soil nutrient levels. This capability facilitates timely and informed decisions regarding fertilizer application and land management, enhancing overall agricultural productivity and environmental quality. 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 · 2025-01

Project Summary This application requests funds for travel awards to expand the diversity of attendees at a long-standing Biennial International Perceptual Learning Workshop. Robust improvements in visual perception can be induced through experience on a variety of visual tasks. This visual perceptual learning has the potential to provide powerful insight into the organization and flexibility of the mammalian visual system. The recognition that visual perceptual learning can be clinically applied to improve visual functions with age and disease have intensified recent efforts to optimize and characterize training paradigms and standardize outcome measurements. In addition, the persistence of perceptual learning in the adult visual system contributes to a revision of our understanding of how the capacity for plasticity is retained in mature sensory cortices. The overall goal of this international conference is to provide a forum for discussion and debate of recent advances, long-term debates and new directions for the field of perceptual learning. This conference has become an important opportunity for individuals across the globe with expertise in perceptual learning ranging from psychophysics, neurophysiology, functional imaging, computational neuroscience, and perceptual rehabilitation to receive critical commentary of new unpublished work, and to influence and learn from each other. The conference attracts both pioneers and newcomers to the field, and has a strong representation of NEI-funded American scientists. The meeting serves an important need as the field of perceptual learning is highly interdisciplinary and there are no other forums that bring together researchers across the many disciplines that contribute to the field. The meeting also has been successful in enhancing interactions between junior and senior investigators, providing opportunities for new collaborations and influencing subsequent work by identifying the most important issues in the field. The specific goals for the 2025 Visual Perceptual Learning Workshop are: Specific Aim 1: To highlight new and exciting developments on the frontiers of perceptual learning. Specific Aim 2: To promote communication and interactions between meeting attendees Specific Aim 3: To achieve the highest scientific quality and diversity, by optimizing a balance of gender, age, ethnicity, and nationality.