University Of Notre Dame

NSF Awards · FY 2024 · 2024-08

Understanding the brain is one of the greatest challenges in science and engineering. Current brain-machine interfaces, however, are severely limited by power consumption, data acquisition capabilities, and real-time processing constraints. These limitations in turn limit our ability to study brain function and develop treatments for neurological disorders. The NeuroFlex project aims to address these challenges by creating a powerful yet energy-efficient neural interface platform. By combining innovative memory devices optimized for efficient data storage, programmable circuits for high-density neural signal acquisition, and specialized processors for low-power computation, this research could revolutionize our understanding of the brain. The insights gained from this advanced neural interface technology have the potential to unlock new therapies for conditions like epilepsy, Parkinson's disease, and other neurological disorders that affect millions worldwide. Furthermore, the interdisciplinary nature of this project, spanning device engineering, circuit design, and tensor accelerators, will advance these fields and inspire new avenues of research. The project will also develop new tools and educational initiatives. The researchers will organize a workshop on computing with emerging technologies, develop curriculum modules on system-on-chip design, and engage K-12 students through outreach activities aimed at encouraging participation in STEM fields, with a focus on underrepresented groups. The NeuroFlex project will develop an implantable neural interface platform that integrates three key innovations: 1) optimized resistive RAM memory for efficient data storage, 2) programmable analog front-end circuits for high-density neural signal acquisition, and 3) specialized processors for energy-efficient computation of both dense and sparse neural network operations. Intelligent control algorithms and software will orchestrate the flow of data through these components to maximize efficiency within the strict power and size constraints of an implantable device. The research plan encompasses device fabrication and characterization, mixed-signal circuit design, digital accelerator development, and the mapping of machine learning algorithms onto the novel hardware architecture. We will validate our approach through testing of two prototype chips, including one with novel integrated devices. By bringing together experts in devices, circuits, architectures, and algorithms, the NeuroFlex project aims to bridge the gap between neuroengineering and microelectronics to enable a new generation of brain-machine interface technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Child maltreatment occurs at alarmingly high rates and represents a serious threat to children’s healthy development, with negative ramifications lasting into adulthood. Exposure to intimate partner violence (IPV), child abuse, and neglect are particularly detrimental and frequently co-occur. IPV, which typically has an onset during women’s childbearing years, is one of the single largest risk factors for subsequent child maltreatment. Therefore, preventing child IPV exposure is critical for child maltreatment prevention and may be effectively targeted in programming during pregnancy. In order to promote a healthy parent-child relationship, enhancing maternal sensitivity is another key process that should be targeted in prevention approaches for child maltreatment. In this project, we propose an innovative approach that combines efforts to address IPV and to support the mother-child relationship in two developmental periods to prevent child maltreatment. Using a multi-site, randomized controlled trial design, the proposed project aims to assess the individual and combined contributions of prenatal and postnatal programming to prevent child maltreatment. We will evaluate the Pregnant Moms’ Empowerment Program (PMEP), a 5-session group program for women exposed to IPV that is delivered during pregnancy, and the Reminiscing and Emotion Training (RET) program as an additional preventative program delivered when children are 3-6 years old. In addition to testing the independent and synergistic effects of the programs in preventing child maltreatment, we will evaluate mechanisms of treatment change. Participants will include 300 mother-child dyads drawn from an ongoing randomized controlled trial of PMEP (n=230; R01HD098092, MPIs Miller-Graff & Howell) and newly recruited pregnant, IPV-exposed women who will complete the identical PMEP randomized controlled trial protocol at the beginning of the current grant period (n=70). The PMEP trial arm includes randomization to PMEP or prenatal control conditions, and 4 assessments (baseline [T1], post-test [T2], 3-month [T3] and 12-month [T4] post-partum follow-ups). When the children of women who participated in the PMEP trial arm are 3-6 years old, dyads will be re-randomized to receive RET or early childhood control conditions. Families will complete 4 additional assessments during the RET trial arm (baseline [T5], post-test [T6], 3-month [T7] and 6-month [T8] follow-ups). Thus, the study is a 2x2 randomized factorial design (i.e., PMEP in pregnancy (Y/N) x RET in early childhood (Y/N)). Our central hypothesis is that the PMEP and RET programs will each prevent child maltreatment. We anticipate that the effect of PMEP will be mediated by change in IPV and maternal sensitivity, while the effect of RET will be mediated by change in maternal sensitivity. We further expect that the effects of RET on child maltreatment will be magnified for women who participated in PMEP, such that RET is more effective in the context of PMEP- related reductions in IPV and enhancement of maternal sensitivity. This project is innovative in its use of a multi-site, multi-method design and significant in addressing the needs of a vulnerable population.

NSF Awards · FY 2024 · 2024-08

This award funds the research activities of Professors Antonio Delgado, Adam Martin, and Yuhsin Tsai at the University of Notre Dame. Particle physics today has an abundance of data from colliders, dark matter detectors, and cosmological measurements. The primary goal of phenomenologists is to scour this wealth of information for signs of new physics, especially particles that are either too heavy to produce in large quantities at colliders or that interact only weakly with the Standard Model. Under this grant, the Notre Dame group will explore new physics through various approaches. They will use cosmological data from the cosmic microwave background (CMB) and the large-scale structure (LSS) of the Universe and study Effective Field Theory, which connects experimental data to new physics phenomena in a way consistent with the symmetries and rules of nature. Through these efforts, the proposed research advances national interests by promoting scientific progress towards understanding the fundamental laws of nature. Furthermore, this research will have a broad impact by guiding future experimental searches and involving close collaboration with experimenters at Notre Dame, Fermilab, and CERN. In addition to this research, Delgado, Martin, and Tsai will continue their teaching (at both graduate and undergraduate levels), outreach efforts through the QuarkNet program, and the organization of domestic and international conferences. More technically, Prof. Tsai will use cosmological data to study strong phase transitions occurring in the late-time universe and measure the lifetime of Standard Model neutrinos. Prof. Martin and Prof. Tsai will also investigate cosmological heavy particle production, focusing on localized signals in the CMB and LSS. Prof. Martin will work on improving Standard Model effective field theory-based searches for new phenomena, emphasizing uncertainties from higher-order terms. Additionally, Prof. Martin and Prof. Delgado will study supersymmetric Effective Field Theories using on-shell amplitude methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Non-technical Abstract: Efficient high-density data storage technology relies on our ability to sense and manipulate electrons. The past decade witnessed the discovery the magnetic topological insulators, a class of materials where the spin of electrons is locked to the direction of their motion. This property has been exploited to design efficient data storage devices utilizing unique magnetic properties. The project aims to design, synthesize and study a new class of topological materials, where the interaction of magnetism with electrons enables new electrical and optical properties. These properties can become the foundation for new means of sensing information that can be exploited in next-generation optical detectors and novel devices. The research activities under this project support the training of graduate, undergraduate and high school students to acquire highly sought-after skills needed to advance the materials and semiconductor workforce of the future. The findings stemming from those research activities are disseminated to a non-expert audience through public library of activities and through social media outreach. Technical Abstract: Layered magnetic pnictides from the EuX2(As,P)2 family (X=In, Mn) are antiferromagnets that can either be topological (X=In) or correlated (X=Mn) insulators. They have recently been studied as single crystals but have yet to be grown as wafer-scale films, necessary for a variety of measurements and applications. This project employs molecular beam epitaxy to achieve the synthesis of thin films of layered magnetic pnictides. Using knobs enabled by epitaxial synthesis, the research team aims to carry out a systematic tuning of the magnetic and electronic properties of these materials. An initial research activity examines how chemical alloying, strain and quantum confinement impact the helimagnetic state of EuIn2As2 and its electronic properties. A specific type of alloying with Mn on the In site is sought to bring a topological and a correlated state into coexistence. A second activity aims to identify manifestations of Berry curvature and quantum geometric phases in the resonant infrared response of EuX2(As,P)2 films through magnetooptical and photovoltaic measurements. Lastly, the project aims to search for a quantized anomalous Hall state by electrically gating EuIn2As2¬ films engineered to be insulating in the bulk. The project synergizes these research tasks with a strong educational component that includes the training of graduate, undergraduate and high school students on tools that are valuable to quantum material science and the semiconductor industry. One of those researchers is recruited from a neighboring women’s college and participates in experiments on site and at user facilities. The findings and discoveries made through this project are partly disseminated to the general public through public library lectures and social media broadcasts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Computational notebooks have become a cornerstone of the scientific computing enterprise, providing an interactive means to acquire and communicate insights, share discoveries, and visualize experimental outcomes. However, computational notebooks today are most suited for small-scale explorations carried out on a single computer, and are quite difficult to use for large-scale computations on high performance computing clusters or commercial clouds. This project will develop NBFlow, a software toolkit for converting notebook computations into workflows that are feasible to execute efficiently on clusters or clouds. This will make it possible to use notebook technologies in conjunction with high performance clusters to enable new discoveries in scientific fields such as high energy physics and geosciences. These technologies will be used to develop new educational curricula, outreach activities for K-12 students, and research experiences for college students. NBFlow will support and advance the use of computational notebooks in scientific research and data analysis by bridging the gap between interactive computation and distributed cyberinfrastructure developed for data-intensive sciences. Today's notebook environments provide easy access to standard artificial intelligence and machine learning toolkits for processing vast datasets with greater efficiency and accuracy compared to conventional methods. However, migrating a notebook from a scratchpad-like analysis to a robust pipeline, which must be distributed across a cluster or cloud infrastructure, currently requires significant efforts by developers and scientists. NBFlow will build upon existing NSF investments in the areas of containerization and workflows that will record notebook executions and schedule tasks for concurrent execution. By experimenting with an integrated notebook-workflow system, this cutting-edge research will advance understanding in data management and distributed computing sub-fields. At the same time, the project will produce novel techniques to robustly capture provenance from notebook-based workflows, a rich source of data in itself, as well as put techniques developed for incremental computation to practice. These technologies will be deployed with active user communities in high energy physics at multiple facilities and in geospatial and sustainability sciences through the I-GUIDE cyberinfrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

Project Abstract Globally, nearly 300,000 people die from hepatitis C virus (HCV) related liver diseases, most of them in developing nations with poor resources. Until the recent COVID pandemic, hepatitis C virus is also one of the leading cause of death in the US by infection. WHO aims to eliminate HCV deaths by 2030. This ambitious goal can only be achieved if HCV infected patients with severe liver disease, including liver cancer, can be diagnosed and treated rapidly. The current gold standard, for HCV diagnostics for both developing and developed countries, is a point-of-care (POC) host antibody test, based on saliva sample or finger prick blood sample, followed by a lab-bound reverse-transcription PCR estimate of the viral RNA (load). The first test does not indicate active infection that should be treated. This is determined by the second viral load test, which also determines the severity of the infection and the selection of proper treatment or more invasive diagnosis. The key obstacle to successfully diagnosing and treating HCV infected patients with liver diseases in the developing world is the second PCR test for viral load. There are very few laboratories for such tests and mail delivery of blood samples is not feasible in a country with low resource. Many patients that tested positive by the POC antibody test do not or cannot travel to the laboratories for the PCR and antigen tests. This proposal aims to integrate two technologies with a smart-phone imaging device to provide a 30-minute one- step POC assay with untreated blood that can quantify viral load and determine the specific liver disease. It is based on a Janus microparticle assay that has the sensitivity of the PCR test but requires much less personnel attention and is much more rapid. Its rapidity (30 minutes) is partly because sample prep is unnecessary. As its signal can only be provided by the virus, rather than its RNA (or protein), only a rapid ultrafiltration step is required to enrich the virus. The PIs have developed such an ultrafiltration technology for extracellular vesicles (EVs). Since the HCV virus is the same size as the EVs, this ultrafiltration technology will be developed for virus and integrated with the Janus particle assay to provide a one-step POC viral load quantification platform. The direct virus assay reduces the sample volume so that capillary drawn blood sample is adequate. The only instrumentation needed for the POC platform is a portable smart-phone based imaging system. Upon completion of this R21 project, a business partner Aopia will design integrated prototypes that can process multiple samples for a major clinical trial after the funding period.

NSF Awards · FY 2024 · 2024-07

This award supports organization and hosting of National Science Foundation (NSF) Spectrum Week 2024, a conference held in Arlington, Virginia on May 13-17, 2024. As in 2023, this week brings together three major programs funded through the NSF Spectrum Innovation Initiative program (SWIFT, NRDZ, and SpectrumX) to hold meetings and social events in a collaborative environment. New elements for 2024 include: co-location and coordination with IEEE DySPAN 2024 to foster publication and dissemination of peer-reviewed research results; the National Spectrum Managers Association (NSMA) industry trade group Annual Conference; and a Federal government outreach workshop for public engagement on the National Spectrum Research and Development plan. The week of events is unique in the way it convenes a large cross-section of the spectrum research community as well as stakeholders in industry and government, and in so doing, integrate students into the forward-thinking conversations, networking, and career development opportunities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

Project Summary The accumulation of mutant myocillin leads to decreased aqueous humor outflow and results in ER dysfunction. Grp94 is a major chaperone localized to the ER that is responsible for modulating ER stress and the folding of select client proteins to maintain proteostasis. Through a number of key studies, it has been shown that Grp94 attempts to fold mutant myocillin, but instead co-aggregates and creates a toxic gain of function for Grp94 that results in POAG. Recently, we discovered Grp94 selective inhibitors and demonstrated both in vitro and in vivo that they reduced aggregation and restore intraocular pressure, providing a new mechanism for the treatment of POAG. Therefore, we propose in this application to optimize our lead compounds, determine their mechanism of action, and provide additional data to support their development as topically administered therapeutics not only for POAG, but also steroid-induced glaucoma.

NSF Awards · FY 2024 · 2024-07

Tens of millions of Americans interact with artificial intelligence (AI) tools to find information, answer questions, or help them solve problems. One key drawback of these systems is lack of personalization: since modern AI systems do not know whom they are talking to, they can only give generic answers to user questions. But the answer to the question “why is the sky blue?” should be different if the person asking the question is a college student or a young child. This project aims to enable an AI model to provide more appropriate responses to users depending on their unique backgrounds, experiences, and needs. It will first gather a diverse dataset in order to characterize what kinds of responses are preferred by different people. The project will then use these data to develop AI systems that can tailor their answers to individual users, as well as evaluate how well the AI systems personalize responses. To achieve this personalization, the AI systems will learn to explicitly represent the kind of person they are talking to, based on their background or previous interactions, and then use this representation to generate an appropriate response. This project will result in AIs that can provide personalized, specific responses based on the person asking the question as well as resources that will help other personalize AIs. These resources will include datasets of personalized questions and answers, interfaces and visualizations to understand why AI provides specific responses over others; interviews and discussions with community members to understand their needs; and code and models that will allow others to build, train, and deploy personalized AI systems. While large language models (LLMs) trained on massive datasets have shown impressive performance on a variety of tasks, they still exhibit biases and struggle to be equally useful for everyone. While initially pre-trained on a language modeling objective, most LLMs are further fine-tuned to align their outputs with human preferences. However, existing techniques assume a “one size fits all” approach, ignoring diversity in user needs. This project will first construct probes to detect cases where models fail to adapt to the diverse needs of different users. Then, this project will develop Personalized Feedback for Diverse Populations (PFDP) to identify when models should be sensitive to the unique needs, knowledge, and background of users by examining the training trajectory of models and comparing models' answers to human preferences. PFDP will enable the development of models that can detect examples that are difficult for computers but not for humans, explain why such disparities in difficulty exist, and represent users’ needs and preferences within the model. To correct those shortcomings in the data, we focus on data curation: we propose techniques to automatically create new examples that ask questions about under-represented groups or require targeted responses to create adversarial prompt and response pairs with a human in the loop. Finally, with these new data, we develop techniques to allow modern architectures to make the most of these difficult (but few) examples. These techniques will allow for fine-tuning LLMs with a small curated subset of data that is robust to variations in prompts and will lead to the generation of acceptable answers for a diverse population of users. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

How different health care approaches can address maternal and birth outcomes is important to our understanding of health care inequities and outcomes nationally and globally. This doctoral dissertation research provides a systematic scientific investigation into the different kinds of midwifery training and knowledge that impact maternal and birth outcomes amongst underserved populations. Results from this research advance our understandings of the various networks of maternal care that are most effective in reducing maternal mortality rates and improving birth outcomes. The broader impacts of this study are the training of a graduate student in scientific anthropological methodology. Further, research results will be made available to policymakers, healthcare professionals, and midwifery certification organizations and can be utilized by healthcare professionals and policymakers to improve maternal health initiatives nationally and globally. To analyze the impacts of different health care approaches on maternal and birth outcomes, the investigators use qualitative interviews, participatory immersion, and methods from cognitive anthropology. They test the impacts of various forms of person-centered care on health outcomes. This research contributes to the advancement of scientific knowledge of maternal healthcare systems and to improving health and professional outcomes for both mothers and care practitioners. It makes significant contributions to medical anthropology, the science of public health, and to research on health inequities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Random objects in general metric spaces have become increasingly common in many fields. For example, the intraday return path of a financial asset, the age-at-death distributions, the annual composition of energy sources, social networks, phylogenetic trees, and EEG scans or MRI fiber tracts of patients can all be viewed as random objects in certain metric spaces. For many endeavors in this area, the data being analyzed is collected with a natural ordering, i.e., the data can be viewed as an object-valued time series. Despite its prevalence in many applied problems, statistical analysis for such time series is still in its early development. A fundamental difficulty of developing statistical techniques is that the spaces where these objects live are nonlinear and commonly used algebraic operations are not applicable. This research project aims to develop new models, methodology and theory for the analysis of object-valued time series. Research results from the project will be disseminated to the relevant scientific communities via publications, conference and seminar presentations. The investigators will jointly mentor a Ph.D. student and involve undergraduate students in the research, as well as offering advanced topic courses to introduce the state-of-the-art techniques in object-valued time series analysis. The project will develop a systematic body of methods and theory on modeling and inference for object-valued time series. Specifically, the investigators propose to (1) develop a new autoregressive model for distributional time series in Wasserstein geometry and a suite of tools for model estimation, selection and diagnostic checking; (2) develop new specification testing procedures for distributional time series in the one-dimensional Euclidean space; and (3) develop new change-point detection methods to detect distribution shifts in a sequence of object-valued time series. The above three projects tackle several important modeling and inference issues in the analysis of object-valued time series, the investigation of which will lead to innovative methodological and theoretical developments, and lay groundwork for this emerging 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 2024 · 2024-07

A fundamental aspect for data analysis is the ability to compare data sets, in order to measure (dis)similarity and quantify patterns present in the data. However, data is often too large and complex to analyze in its entirety, and therefore different techniques are used to summarize the data in order to work with smaller, more manageable representations of it. This project studies the data-comparison problem through the lens of mathematics, using geometric and topological signatures to represent these shapes concisely. This project will consider a variety of different kinds of shape data which live in some larger geometric or topological space (e.g., GIS trajectories, point sets, meshes, 3d scans, or graphs), and consider classes of algebraic, geometric, and graphical signatures which can be used to represent these shapes concisely. The project draws primarily upon the nascent yet rapidly developing area of topological data analysis, where tools from topology like homology or homotopy are combined with geometric measures to create robust analysis tools for analyzing the shape of data. Graduate and undergraduate students will be tightly integrated into the project, and special efforts will be made to involve students from underrepresented groups. Additional efforts by the research team include planning a workshop focused on women in this field, as well as broadening diversity and inclusion efforts in their own universities. The project focuses on shapes that have some common underlying annotation framework on top of the signature, which is usually additional structural or geometric information from the original embedding. The research consists of two major components. In the first, the investigators are initiating a principled study of algorithms and approaches to develop a unified framework which leverages multiple signatures for shape comparison. The goal of this phase is to provide theoretical results as well as empirical evaluations on a variety of data sets and signatures. The second major component of the project studies inverse problems, which aim to reconstruct shapes from a combination of signatures. Such problems are notoriously difficult for geometric or topological signatures, as they are necessarily lossy and remove certain types of information. During the course of the project, the investigators are also developing a shape signatures toolkit that enables computation of a range of signatures and distances, adding to the software both existing notions of distance and new ones developed over the course of the project. This project is jointly funded by the Algorithmic Foundations Core Program and by the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This project will use state-of-the-art computer simulations and machine learning algorithms to advance fundamental understanding of water vapor and water vapor mixture adsorption in porous materials. These are key to developing technologies like atmospheric water harvesting and carbon capture in the presence of water vapor. These are crucial for society in the context of water security, climate change, and other grand challenges. This research project uniquely combines powerful computational modeling and machine learning tools to produce new models that can describe multicomponent water vapor adsorption in porous materials. The research will lead to fundamental insights into the complex interactions between the materials and the adsorbed components of the mixture, which are crucial for technological advancements in areas of national importance including climate change and water security. Outreach and education components within this project include investigating commercialization opportunities for dehumidification processes through the Notre Dame Engineering, Science, and Technology Entrepreneurship Excellence Masters (ESTEEM) program and undergraduate and graduate student training via research experiences and education modules in existing electives. This research program will develop a hybrid modeling paradigm for multicomponent adsorption involving water vapor. The new modeling approach will integrate molecular modeling, statistical mechanics, and machine learning techniques to systematically determine new models capable of describing mixture adsorption with water vapor. The development of these technologies requires fundamental understanding of water vapor and its multicomponent mixtures in confinement and a framework that allows for proper material and technological evaluations in this scenario. A major bottleneck in this context is the lack of models allowing for proper prediction and evaluation. The research goal of this proposal is to produce new hybrid models capable of accurately and efficiently describing multicomponent water vapor adsorption in porous materials. This will be accomplished through a unique combination of machine learning, statistical mechanics, molecular modeling, active learning, and autonomous experiment design. This combination will yield a novel methodological framework for the development of mixture adsorption models that deal with water vapor. These goals are supported by two research objectives to (1) combine active learning with statistical mechanics for molecular modeling of multicomponent water vapor adsorption and (2) develop hybrid models that combine thermodynamic theory and machine learning. These models will be developed using mixtures relevant to atmospheric water harvesting and carbon capture in humid streams. The proposed research seeks to establish new computational methods that are generalizable and transferable to other scenarios, with water vapor serving as a challenging and societally important class of demonstration problems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Workflow management is important to effectively and productively deploying complex computations, e.g., combining chains of dependent and complex tasks such as data gathering, data processing, simulation, training, inference, validation, and visualization. This Pathways to Open-Source Ecosystems (POSE) project seeks to "harmonize" Python programming language based workflow management, build sustainability, and better support complex computational workflows, both in research and commercial environments. Harmony will deliver a software ecosystem that is used by some of the most impactful science projects, from understanding the beginning of the universe to discovering new therapeutics for viruses. This Phase I project will engage in ecosystem discovery, organization and governance, and community building to build the Harmony Open-Source Ecosystem (OSE). Both small and large scale science require workflows to deliver science results. The Harmony OSE will provide new capabilities to integrate, interoperate, and interchange components to create high performance workflows. The project will lower the barrier to implement sophisticated workflows and manage their execution at scale all from a familiar Python interface. Harmony will bring together the community to consider how efforts can be more tightly integrated and managed and how an OSE can benefit all stakeholders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-06

The Impact of Novel Vector Control Tools on Asymptomatic Plasmodium falciparum Infection Prevalence and Transmission Potential Project Summary Malaria is a major cause of morbidity and mortality in many countries in the tropics and subtropics. Most malaria programs focus on diagnosis and treatment of clinical infections and blanket vector control through bed net distributions. This strategy does not adequately target the asymptomatic reservoir, which is the source of up to 95% of malaria transmission. There is a lack of understanding of the ability of many current and new malaria control tools to shrink the asymptomatic reservoir because many studies evaluating interventions focus on clinical incidence as the main outcome. Spatial repellents are a novel tool for malaria control. They release volatile compounds that drive away mosquitos from houses. Spatial repellents might reduce clinical incidence by up to 50%. Their impact on the prevalence of asymptomatic infections has not been systematically studied. The ability of spatial repellents to reduce clinical incidence is currently being field-tested in a funded large-scale cluster randomized trial in western Kenya. Our preliminary data shows that nearly half of all individuals in the study site carried asymptomatic malaria infections at baseline. Given the importance of these infections as a source of transmission, there is a need to understand whether spatial repellents can also reduce this reservoir in order to assess their full potential to combat malaria. Here, we propose to study the impact of spatial repellents on asymptomatic P. falciparum prevalence. The objectives of this application are to: 1) Quantify reductions in asymptomatic prevalence after the introduction of spatial repellents. We will screen 9000 blood samples currently being collected for P. falciparum infections by qPCR and microscopy. 2) Compare mosquito-to- human and human-to-mosquito transmission in intervention and control clusters. We will quantify gametocytes and compare parasite and gametocyte prevalence and density to vector density, sporozoite rate, and entomological inoculation rate (EIR) at different time points before and after blood sample collections. The outcomes of the proposed research include: 1) Knowledge of the potential of spatial repellents to reduce asymptomatic prevalence, and 2) an understanding of the changes in mosquito-to-human and human- to-mosquito transmission after introduction of spatial repellents. Our data on the relationships between parasitological and entomological measures of transmission intensity, collected in 20 clusters at three timepoints, will enable a better understanding on the impact of malaria control beyond the use of spatial repellents. Our work aims to accelerate malaria elimination, and thus to improve the lives of people living in malaria-endemic countries.

NSF Awards · FY 2024 · 2024-06

The next generation of wireless systems will largely be deployed in radio spectrum that is shared, not only between commercial networks such as cellular and Wi-Fi, but also with various incumbents such as federal radar systems and fixed microwave links. Mid-band spectrum (now being defined to extend up to 24 GHz) is becoming the workhorse of wireless networks due to the favorable propagation and system characteristics that balance range with bandwidth. Recent spectrum allocations confirm these spectrum priorities: in 2020, the US made available 1.2 GHz of spectrum in the 6 GHz band (5.925 - 7.125 GHz) on an unlicensed, but shared, basis. Similarly, the CBRS band (3.55 GHz – 3.7 GHz) is shared with Navy radars. The mid-bands are also very congested, leading to the need for more, and better, spectrum sharing. Spectrum Sharing Sandbox (S3) will be a data platform that will enable measurements and experiments on deployed 6 GHz and CBRS networks thus enabling the CISE community to develop a better understanding of how different types of sharing are performing in the real world. Commercial CBRS and Wi-Fi 6E devices will be deployed in multiple indoor and outdoor environments on the University of Notre Dame campus and will offer (i) the ability to verify propagation and interference characteristics in realistic deployed environments; (ii) select I/Q captures of adjacent channel and co-channel interference scenarios using software-0define-radios (SDRs); and (iii) rich Layer 1 - 2 data captures for AI/ML algorithm development and testing. An experimental platform such as S3 does not exist today in academia or industry. S3-enabled real-world experimentation and measurements will drive future spectrum policy in new shared bands, such as the 7 - 8 GHz band. All relevant research outcomes from the community will be presented to the appropriate spectrum regulatory bodies, such as the FCC and NTIA, for maximum policy impact. High-school and undergraduates will also have access to S3 via existing Summer Scholar and REU programs at SpectrumX and Notre Dame that prioritize under-represented minorities. The data-sets that are created will be available to the entire CISE community and contribute to other NSF-funded programs such as Colosseum, RFDataFactory and ns-3. The project web-site is: https://spectrumsharingsandbox.org/. All experiment descriptions, measurements, publications, software, workshop proceedings, tutorial materials, webinar recordings and other outcomes of this project will be maintained for 3 years and remain accessible via the website after the completion of the project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-05

Summary/Abstract Malaria control and elimination face significant challenges due to drug resistance, particularly to the current front-line artemisinin-based combination therapy in the Greater Mekong Region, to artemisinin (ART) derivatives in Myanmar and now disturbingly in eastern Africa where most serious falciparum malaria cases are concentrated. To ensure the continued effectiveness of ART and its partner drugs, early detection and characterization of resistance, robust surveillance and subsequent isolation are required. World-wide clinical and molecular surveillance of artemisinin resistance (ART-R) relies on southeast Asian characteristics of longer parasite clearance times from patients and genotyping for mutations in the most common ART resistant gene, k13 (a gene on chromosome 13 coding for a kelch domain protein, K13). The Chittagong Hill Tracts (CHTs) in Bangladesh borders Myanmar, and is a forested, hilly and remote region that is endemic to 90% of the country’s malaria. Recent clinical clearance and in vitro assays not only in the CHTs but also in Africa demonstrate that although combination treatment remains efficacious in infected patients, a cohort of the field isolates display in vitro low-moderate ART-R with no K13 mutations. This suggests that clones with partial ART-R exist and are going undetected by patient parasite clearance studies and molecular surveillance. Proactive analysis of the impact of these molecular determinants will be critical to characterize resistance before the problem is widespread in these regions in the pre-elimination era. We hypothesize that 1) Clinical ART resistant mutations can be predicted from in vitro generated mutants in isolates from the same spatiotemporal space. 2) ART sensitivity is changing in the CHTs with continued use of artemisinin combination therapy since 2004. The initial low-moderate ART-R mediated by causal K13 independent mutations will be an optimal platform for highly resistant and highly fit K13 mutations to arise and spread. We will test our hypotheses with two specific aims. In Aim 1, we will perform whole-genome sequencing of in vitro evolved K13 independent ART-R in recent CHT isolates; in parallel determine the drug sensitivities and genomic sequences of parasites recently isolated from CHT patients and functionally validate top candidate genes by CRISPR-Cas9 editing. In Aim 2, we will perform preemptive resistance and fitness cost analysis of major K13 mutations in the K13 independent ART resistant and ART sensitive CHT genetic backgrounds, to determine if these “unknown factors” assist the resistance potential and sustainability of these K13 mutations. Together, this study will provide a comprehensive view of the complex emerging ART-R with a broader applicability to similar scenarios in Eastern Africa.

NIH Research Projects · FY 2025 · 2024-05

PROJECT SUMMARY / ABSTRACT A collaborative, multidisciplinary research project is proposed to develop and validate an innovative platform to study separate phage species and to study the interactions between bacteriophages and their host bacteria. We will create a microfluidic capillary electrophoretic (CE) platform to separate bacteriophage from complex microbiota while sorting them into enriched fractions. This system will be used to culture, manipulate, image and characterize phage electrophoretic behavior on a molecular level, and to study fundamental cellular processes that occur during the interaction between the phage and microbial cells. The platform supports direct injection and measurement of microphysiological cultures and infections and supports direct analysis and imaging of phage, phage-host and host mixtures from complex microbiota. This platform maintains viability of the organisms, sorts and enriches rare populations from complex mixtures in order to study fundamental molecular processes by direct measurement and identification of novel phage populations. The development of the proposed phage platform will provide a new tool for the manipulation of other viral components of the microbiota.

NIH Research Projects · FY 2025 · 2024-05

PROJECT SUMMARY Pathogenic mycobacteria damage the phagosome and interact with the macrophage cytoplasm. The mycobac- terial factors that control bacteriolysis in the cytoplasm are unknown. The objective of this proposal is to define the genes controlling mycobacterial bacteriolysis in the macrophage cytoplasm. The central hypothesis for this application is that mycobacteria actively control bacteriolysis in the cytoplasm. To test this hypothesis, the fol- lowing specific aims will be tested. The applicant has adapted an established reporter for cytoplasmic bacteriol- ysis from Listeria monocytogenes for use with Mycobacterium. Under the first aim, the applicant proposes a targeted approach to define the impact of known virulence factors on mycobacterial bacteriolysis. The objective of Aim 1 is to define if known virulence pathways protect M. marinum from bacteriolysis. The applicant will test the working hypothesis that known virulence pathways that modulate the inflammasome protect Mycobacterium from bacteriolysis in the macrophage cytoplasm. M. marinum strains lacking specific virulence pathways will be generated and bacteriolysis will be measured during macrophage infection. Under the second aim, the applicant proposes an unbiased approach to identify genes required for cytoplasmic adaptation of mycobacterial patho- gens. The objective of Aim 2 is to Identify genes the control mycobacterial bacteriolysis. The applicant will test working hypothesis that several conserved pathways protect mycobacterial pathogens from bacteriolysis in the cytoplasm. Two complementary genetic screens to identify molecular pathways controlling mycobacterial bacte- riolysis in the cytoplasm during macrophage infection. The applicant expects that the successful completion of Aim 1 will determine if known virulence pathways protect mycobacteria from bacteriolysis. The completion of Aim 2 is expected to identify new pathways that promote mycobacterial survival in the cytoplasm. Completion of the proposed aims will contribute an initial understanding of the mechanisms required for preventing mycobac- terial bacteriolysis in the macrophage cytoplasm, moving the field in a new direction. This contribution will be significant because it will identify a new molecular mechanism underlying mycobacterial pathogenesis. The ap- plication is conceptually innovative because it focuses on mycobacterial determinants that protect mycobacteria from bacteriolysis in the cytoplasm, which represents a shift in focus for the field. The experimental design is innovative because it applies a novel indirect reporter of cytoplasmic bacteriolysis that has not previously been used to study mycobacterial species.

NIH Research Projects · FY 2026 · 2024-05

PROJECT SUMMARY/ABSTRACT Spinal cord injury in mammals triggers a cascade of cellular events that lead to the loss of sensory and motor function caudal to the site of injury. Following spinal cord injury, immune cells, including microglia and macrophages, infiltrate into the lesion site and become activated. Depleting microglia and macrophages in mammalian systems has shown both beneficial and detrimental effects post-injury. Identifying the specific regenerative immune requirements in mammalian systems has proven difficult due to a complex combination of anti-regenerative barriers. In contrast, zebrafish spontaneously regenerate a fully severed spinal cord and provide a platform for identifying pathways necessary for spinal cord regeneration. The zebrafish immune system is conserved with mammals, and therefore provides a unique system to identify pro-regenerative immune pathways. In preliminary data, I found microglia and macrophages are necessary for functional and anatomical recovery post-injury, but the pathways directing microglia/macrophage-dependent spinal cord regeneration are not known. Microglia and macrophages are highly plastic cells, and their gene expression and behavior have direct implications on functional outcomes following neural injury. This proposal will identify microglia/ macrophage-specific cellular identities, gene expression, and pathways that are necessary for spinal cord regeneration in the adult zebrafish. First, two of the most important functions of microglia and macrophages following spinal cord injury are to direct the healing of injured tissue and clear the lesion site of cellular debris. Aim 1 (K99 Phase) will utilize loss-of-function mutants to define genes upstream of wound healing that are necessary for re-establishing immune privilege of the spinal cord after injury. Aim 2 (K99/R00 Phase) will move from the adult zebrafish spinal cord to a human cell culture system to visualize behavior in human iPSC-derived microglia and test the conservation of pro-regenerative gene function in human cells. Lastly, the origin of immune cells will dictate their cellular function and effect on regeneration, and the origins of pro-regenerative microglia and macrophages are unknown. In Aim 3 (R00 Phase), I will perform lineage tracing in the adult zebrafish regenerating spinal cord to characterize the origin of expanding immune cells post-injury. These Aims are designed to apply my strengths in zebrafish genetics and regeneration to the new field of neuroimmunology. To facilitate my ability to carry out these proposed experiments, I have assembled a team of advisors and collaborators, taking advantage of the vibrant neural injury and neuroimmunology communities at Washington University School of Medicine. This proposal will generate novel tools and protocols to measure the immune events during spinal cord regeneration and offers a foundational niche in the spinal cord injury field through which I can launch a future tenure-track research faculty position. Additionally, work proposed here will identify pro-regenerative pathways that have direct relevance to human health and provide potential therapies for human spinal cord injury patients.

NIH Research Projects · FY 2026 · 2024-03

Project Summary The overall goal of this project is to develop lower-limb clothing and optimization-based algorithms that merge information from electronic textiles, inertial sensors, portable load cells, and biomechanical models to characterize the pressure sources at the physical interface between residual lower-limbs and prosthetic sockets. The project will enable analysis of pressure for more than 16 continuous hours outside the laboratory with automatic calibration of pressure magnitude and its location relative to the human body. The health outcomes of prostheses and orthoses depend on the physical interface between compliant human tissue and the rigid or padded structures of the device. Quantifying this pressure can inform clinicians on how to prevent ulcers or other side effects of poorly managed physical interfaces, particularly for users with reduced sensation (e.g., due to diabetes or spinal cord injury). However, existing knowledge to relate interface pressure and health outcomes is inconclusive, in part, because 1) available data do not capture the long-term effects of interface pressures and 2) measurements represent mostly laboratory conditions. Pressure recordings depend on expert supervision for sensor calibration and placement — to the extent that there is no user-driven method to accurately measure a complete day of pressure activity. The state-of-the-art focuses on sensor design to improve accuracy and ease of use, but accurate pressure readings are only a portion of the challenge; unsupervised pressure measurements require biomechanical context for calibration and interpretation. For example, pressure on the distal end of a prosthetic socket can increase when there is a transition from swing to stance or a volume change of the residual limb. This project will enable interface pressure and gait-event information for a complete day of activity outside the clinic and the laboratory without expert supervision. The central hypothesis is that, by combining information from inertial sensors, external forces, pressure sensors, and biomechanical models, it is possible to generate accurate user-driven pressure measurements without the need for expert calibration or placement. The specific aims of this project are to 1) create an optimization framework to calibrate pressure sensors with partially unknown placement, 2) develop a clothing paradigm for pressure and inertial measurements in residual limbs, and 3) demonstrate the feasibility of one day of unsupervised pressure measurements outside the laboratory. Successful completion of these specific aims will establish a generalizable wearable sensing platform, which enables the analysis of long-term body exposure to lower-limb device pressures. The project will apply existing knowledge in biomechanics, robotics, mechanics and materials for stretchable e-textiles, and robust optimization to accomplish these aims. Subsequent studies might focus on reducing the risks associated with asymmetric loading through pressure monitoring, or the design of feedback control laws for lower-limb exosuits that maximize comfort through pressure feedback.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY Suicide is a leading cause of global mortality and rising suicide rates have been particularly steep in the United States. Accordingly, NIMH is investing heavily in prevention, including calls for improved precision care— interventions delivered based on specific, granular understanding of person-level vulnerabilities and their interaction with local environments. Although stress, a well-established concurrent and prospective risk factor for suicidal ideation (SI), is experienced by all, it varies at both group- (e.g., discrimination, local events) and person- (e.g., arguments, accidents) levels in timing, frequency, and experience. Thus, there is a significant need to improve the accuracy in detailing the stressful events – SI relationship, as well as elucidate person-level markers that increase vulnerability to this association. Tonic (trait-like) and phasic (state) impulsivity (IMP) and emotion dysregulation (ED) are well-established predispositions for suicide risk. IMP and ED alone are insufficient markers of SI risk, yet each may increase risk for SI in the context of stressful life events. Their joint effects may also potentiate the relationship between changes in person-level stress and risk for momentary SI; however, this has yet to be tested, leaving little known about person-level, moment-to-moment SI prediction. The overarching objective of this research is to specify person-level conditions that portend imminent SI, information that has the potential to inform the development of effective just-in-time interventions, consistent with precision care objectives of the NIMH Strategic Plan. This research will: (1) evaluate the direct effect of real-time stressful events on concurrent and prospective momentary SI, (2) evaluate the moderating effects of tonic (trait-like) and phasic (state) IMP and ED on the prospective relationship between real-time stressful events and momentary SI, and (3) explore the propensity to experience SI in response to prior stress as a potential moderator of the proximal relationship between real-time stressful events and momentary SI. A sample of individuals at high risk for suicide (i.e., adults age ≥18 with persistent lifetime SI and SI during the past 6-months) will complete a baseline session and 30-day period of ecological momentary assessment to evaluate tonic and phasic IMP and ED, real-time stressful events, and momentary SI. Goals of the fellowship training plan, which will take place at the University of Notre Dame, include: (1) enhance knowledge of emotional, personality, and cognitive components of suicide risk; (2) develop advanced knowledge of the assessment of suicide risk factors; (3) enhance knowledge of advanced statistical analyses for within- and between-persons research; (4) engage in professional development activities and develop advanced research skills; (5) enhance knowledge of ethical research practices with high-risk populations.

NIH Research Projects · FY 2025 · 2023-09

Glial cells, including astrocytes, are the most prevalent cell type in the brain by far, and their dysfunctions are known to play a role in a host of neurodevelopmental, neurodegenerative, neuroimmune, and neuroplastic diseases and disorders. However, they have been greatly understudied relative to neurons, and it remains unclear what role, if any, they play in cortical folding. Therefore, there is a critical need for deeper mechanistic understanding of the role of glial cells in brain development across health and disease. The long-term goals of the PI and co-I are to use their backgrounds, in computational mechanics and molecular and cellular neuroscience, respectively, to understand the process of cortical folding. Here they combine their complementary expertise to investigate the role of astrocytes in gyrification using a combined computational-experimental approach. The overall objective of this CRCNS proposal is to relate cellular behavior at the microscale to cerebral morphology and cortical folding at the macroscale. In particular, we will evaluate two potential mechanisms of astrocyte proliferation: 1) that astrocytes push on the cortex or 2) that the cortex pulls on astrocytes, causing them to grow in response. To that end, we will experimentally manipulate and track astrocytes in the developing ferret brain using in utero electroporation (Aim 1), develop and calibrate computational models of both mechanisms of astrocyte behavior in cortical folding (Aim 2), and use our models to evaluate their likelihood (Aim 3). This proposal is strongly founded on our own prior work, which has shown that astrocyte proliferation under gyri is necessary for the formation of cortical folds in the ferret brain, and that an experimentally-calibrated computational model can capture the dynamics of cellular behavior and the resulting tissue-level mechanics and morphology. The combined experimental-computational approach proposed here will contribute to our fundamental understanding of the role of glial cells in brain development, which could be important in the study of neurodevelopmental diseases and disorders, advanced diagnostics, and effective treatments. Furthermore, our experimentally-validated computational framework could be used to design experimental approaches to test mechanistic hypotheses and to identify pathways for treatment in spatio- or temporally-specific events such as prenatal infection, illness, or exposure.

NIH Research Projects · FY 2025 · 2023-09

PROJECT SUMMARY One of the most fundamental properties of cells is the ability to transduce signals to other cells and the surrounding environment. Well recognized modes of cellular signaling include direct cell-cell interactions via membrane receptors and ligands and the release of soluble factors, such as growth factors, cytokines and chemokines. The more recently described extracellular vesicle (EV) is now also considered as an important mediator of cell signaling, allowing cells to exchange proteins, lipids and genetic material. EVs are secreted from nearly all cell types and EV-based communication relies on the ability of vesicles to deliver bioactive molecules to other cells. The field of EV biology is rapidly evolving and expanding, affecting almost all biomedical disciplines, from oncology and obstetrics to infectious diseases and stem cell biology. Cells release EVs not only in culture but also in vivo, and diverse types of vesicles have been isolated and analyzed from almost all bodily fluids, leading to the postulation that EV-based liquid biopsies can be used for diagnostics. However, despite the excitement and hundreds of new publications on EVs in recent past, several basic hypotheses regarding their function remain experimentally untested. A major challenge in EV research is the huge and often underappreciated diversity in shed vesicles. Many of the impediments to advance EV biology and application, stem from the inability to separate a complex population of vesicles into subclasses of particular sizes, compositions, and biogenesis pathways. Microvesicles (MVs) are an EV subtype which are shed by the direct outward budding of the plasma membrane. They are present in biological fluids and appear to be involved in multiple physiological and pathological processes. However, much remains unknown regarding the biogenesis and role of these vesicles as signaling mediators. Here we propose strategies to catalog molecular cargoes targeted specifically to MVs and identify new regulators of cargo loading. We also aim to interrogate GTPase-regulated cell signaling pathways that regulate MV release. Finally, we will examine the interactions of MVs with receptors on endothelial cells and consequent signaling pathways activated in the recipient cell. These studies will provide new insights into a rapidly evolving frontier in signal transduction, as well as the molecular basis of various diseases.

NIH Research Projects · FY 2024 · 2023-09

Protein Structure, Dynamics, and Aggregation in Phase Separated Droplets Abstract Amyotrophic lateral sclerosis (ALS) is a severe and deadly disease. In recent years, it has been discovered that a key mechanism of disease progression lies in liquid-liquid phase separation (LLPS) of a number of peptides and proteins. Indirect evidence has also emerged that LLPS can induce protein folding/aggregation into amyloid- like hydrogels in a number of different diseases, including ALS, type-2 diabetes, and Alzheimer’s disease. We recently reported direct in-situ evidence that phase separation induces a folding transition for peptide and proteins derived from ALS. This proposal aims to build on that work to develop and apply spectroscopic tools for in-situ characterization of protein structure, dynamics, and solvation within phase-separated droplets, in order to identify the structure and mechanism of formation of these folded proteins and gels, and to study how these changes relate to the disease state of ALS. To accomplish this, we will use two-dimensional infrared spectroscopy (2DIR), infrared microscopy, and 2DIR microscopy, to probe changes in secondary structure and hydration of peptides and proteins within droplets, and understand the fundamental biophysical processes involved in protein LLPS. Key questions that we aim to answer are: What role does solvation serve in the driving forces governing LLPS? Can volumetric crowding in polymer dense LLPS droplets promote changes in protein secondary structure? Can LLPS drive protein folding/aggregation into potentially toxic amyloid states? We will be able to answer these questions for in-situ studies, something currently not possible with other techniques. The strategy outlined in this proposal is designed with the long-term goal of building a research program that can perform structural studies in complex biophysical systems, turning the full suite of structure sensitive observables in nonlinear IR spectroscopy towards addressing questions in whole cell systems.