North Carolina State University

NSF Awards · FY 2024 · 2024-08

The main goal of this project is to develop and deliver remote experiments utilizing cloud-based resources aimed at educating a broad audience of students and practitioners in hardware security. In the post-COVID era, it is imperative to develop online education platforms for remote training of both students and the workforce in the field of Hardware Security. Recent advances in this field and FPGA-based cloud servers have enabled an opportunity to move related experiments to an online format that only requires a standard computer and internet connection by the students. Teaching “hardware” security in a socially distanced format poses significant challenges. Essential experiments for teaching key concepts in hardware security necessitate multiple evaluation boards and physical equipment such as voltage supplies, oscilloscopes, multimeters, and function generators. To adapt these experiments for an online platform, the project will explore innovative methods to execute or emulate them using the cloud ecosystem. This project addresses a critical gap by developing a fully online hardware security training module accessible to students and professionals worldwide. This project proposes various comprehensive experiments testing different notions in hardware security. The framework will be designed for both undergraduate and graduate students in the electrical engineering, computer engineering, and computer science departments, leveraging courses developed by the PIs in their respective institutions. The proposed infrastructure includes preparing detailed experiments for instructors with walkthrough documents and organizing student assignments for independent completion. This setup supports not only teaching but also facilitates independent research upon assignment completion. Supplemented with video instructions, these experiments will constitute a comprehensive training module, equipping participants with the necessary skills and knowledge to address complex challenges in this emerging domain, thereby instilling preparedness and confidence. This award is co-funded by the NSF Improving Undergraduate STEM Education (IUSE: EDU) Program. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is further supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Algebra is a gateway for broad STEM pathways. Yet, many students fail to achieve proficiency in algebra, which is arguably a primary cause of inability to pursue advanced STEM disciplines and further hesitancy in taking STEM pathways. This project aims to advance the knowledge in how students learn robust knowledge in algebra. It will allow students to not only derive answers for stereotypical problems but also draw analytical reasoning for unseen problems. The investigators hypothesize that one of the challenges in learning algebra is due to the complication of the web of algebraic knowledge students need to learn. It is argued that the web of knowledge involves conceptual and procedural knowledge and their relations, which the investigators call the connected knowledge. The investigators propose to develop a transformative technology in the form of teachable agent to amplify the effect of learning by teaching. The smart teachable agent asks students questions to justify their reasoning while solving equations. When a student’s response needs to be elaborated, the smart teachable agent further provides a follow up question to solicit a response that reflects a connection between procedural operations and conceptual justifications. The smart teachable agent may ask follow-up questions two to three times. The proposed question-based dialogue between the student (a tutee) and the smart teachable agent (a tutor) is called a constructive tutee inquiry. To implement the constructive tutee inquiry, the investigators will develop an innovative application of large language models (LLM) where multiple LLM invocations will be combined, including one for generating an ideal response to the agent’s question and another one for generating a follow up question based on the gap between the student’s response and the ideal response. The proposed dialogue system will be embedded into an existing online learning environment where students learn to solve linear equations by teaching a teachable agent. As a learning science contribution, the investigators will study a theory of how students learn connected knowledge and how acquisition of connected knowledge facilitate robust learning in algebra. Classroom evaluation studies using the existing systems with the proposed constructive tutee inquiry will be conducted with middle school students in their algebra classrooms. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Providing students with exposure to high quality CT activities within science classes has the possibility to create transformative educational experiences that will prepare students to harness the power of CT for authentic problems. By building upon foundational research in human-AI partnership for classroom support and effective practices for integrating computational thinking (CT) in science, this collaborative research project will advance understanding of how to empower teachers to lead computationally-enriched science activities with adaptive pedagogical tools. This project will also advance knowledge of how to prepare teachers to engage in AI-augmented teaching and human-AI partnerships for classroom support. The project will involve three yearly cycles of teacher professional development, iterative co-design, development of an AI tool (called TRACES), and classroom implementation of the designed learning activities and AI tool within middle school classrooms. Using state-of-the-art AI methods, real-time classroom data will be used to help teachers modify pacing, help specific students in need, and identify students who can act as peer mentors. The team’s prior research has shown that teachers can be successfully prepared to use and teach with AI in their classrooms, allowing them to notice and respond to these classroom and individual needs. The project will leverage these recent advances to transform the landscape of CT in science education. The project will co-design and test 28 new CT-integrated science activities with teachers and provide learning experiences to 84 science teachers by developing and implementing professional development for CT in science. The project will also iteratively co-design and test the proposed AI-powered support toolset that aids teachers in making data-driven instructional decisions during these activities. The AI-powered toolset co-designed with teachers will provide real-time information to support their ability to notice and respond to student work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Planktic foraminifera are tiny, single-celled organisms with calcium carbonate shells found throughout the open oceans. These shells have left behind a remarkably detailed fossil record, which scientists use extensively to learn about past environments and evolutionary changes. Scientists study these fossilized foraminifera shells to reconstruct ancient environments and understand how species evolved over time. Each species has unique characteristics in the shape of its shell, which can also vary depending on environmental factors. Recent advances in technology have allowed researchers to examine the three-dimensional structure of these shells in great detail. However, current methods like micro computed tomography (micro-CT) scanning are expensive and time-consuming. The new approach will use existing robotic imaging devices and cutting-edge Artificial Intelligence (AI) tools making research more accessible. This method will allow scientists to reconstruct the 3D shape of foraminifera shells from simple two-dimensional microscope images. The goal is to create an affordable and scalable cyber-infrastructure that can be used by scientists, their students and even citizen scientists. This infrastructure includes reliable imaging systems and flexible software that can be updated with new models and analytical tools in the future. The cyber-infrastructure will be built using robotic devices the project team has developed for imaging both dry and wet samples. leading to the creation of a dataset that includes specimens imaged with these robotic devicess, along with images from micro-CT scans. To enhance this dataset, computer-generated models are added which have been designed specifically for this project. The goal is to develop an AI model that can reconstruct the 3D shape of specimens from just a few 2D microscope images—sometimes even just one. The resulting model from each analysis will be accessible through a web portal, allowing scientists and their teams as well as citizen scientists to use it for their research. Project team members are partnering with educators to involve K-12 teachers and students in capturing new images using the imaging system. This additional data will help improve the synthetic data generation process and lead to better understanding of how specimens are categorized. The aim of developing these resources, is to broaden the study of foraminifera morphology and its applications in paleontology and paleoclimate research. This project has the potential to democratize access to advanced imaging techniques and foster collaboration across scientific communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project investigates transit-induced commercial and residential gentrification and displacement through a first-of-its-kind merge of historical market research microdata of individual businesses and households with highly localized data from street-view images, online reviews, and real estate websites. With advanced Artificial Intelligence techniques, this research seeks to unveil the complex processes of neighborhood change and migration patterns around transit stations and, meanwhile, develops versatile tools for analyzing neighborhood dynamics in diverse urban contexts. The research findings can inform policies to mitigate adverse effects on vulnerable businesses and residents and support equitable urban development. Distinguished from previous research with limited samples, this project integrates data from multiple sources to cover the full populations of households, businesses, and housing in a region. Expanding upon causal economic analysis and urban gentrification theories, the project develops innovative data fusion techniques, machine learning models, spatial analysis, and quasi-experimental econometric models to effectively capture the impacts of transit investments on different types of businesses and households. Research objectives include identifying vulnerable businesses, the typologies of establishments that tend to replace them, and the socioeconomic profiles of households and their likelihood of migrating into or exiting station areas. The project examines the accuracy and reliability of socioeconomic microdata and develops a methodological framework for neighborhood dynamics analysis adaptable broadly for research with microdata to understand urban processes and dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The CD300 protein family of immune receptors is encoded by a family of genes present in all mammalian species, including humans. CD300 proteins have been implicated in multiple important roles in human health, including the regulation of cancers, inflammatory diseases, and viral infections. By directly binding pathogens and initiating an immune response, CD300’s play a crucial role in the immune response of humans and other animals. However, the number of CD300 genes varies dramatically between species; for example, rodents, dogs, and armadillos encode more CD300 genes than humans. Little is known about the function of these receptors across different species. Results from this project will shed light on the evolutionary history and functional diversification of the CD300 gene family, providing valuable insights into how these receptors contribute to immune function and disease susceptibility in all mammals including humans. This knowledge has the potential to enhance our understanding of rules that govern the emergence of novel immune function while simultaneously informing the development of new therapeutic strategies. Additionally, the interdisciplinary efforts from this project will yield new K-12 educational modules that align with state standards, will be widely available to public school teachers including those in the Eastern Band of Cherokee Indian community, and will feature gamified elements to enhance STEM education. It will foster scientific literacy and engagement. The human genome encodes seven structurally similar CD300 genes in a single cluster on chromosome 17 which are presumed to have arisen through tandem gene duplication events. Select CD300 proteins have been shown to bind specific phospholipids and are implicated in pathogen recognition and immune defense. However, mammals and other vertebrate species also encode clusters of CD300 homologs with variable gene content across species. Little is known about the functional diversification and evolutionary dynamics of CD300 genes across these lineages. As a consequence, it remains unknown whether the emergence of novel CD300 genes is associated with the development of novel functions. This project will use a multi-omic approach to fill this knowledge gap by mapping the molecular and functional diversification of CD300 orthologs and paralogs, and experimentally testing ancestral CD300 functions. By using a comparative approach to study CD300 genes, the proteins they encode, and the lipids they bind, this research will provide insights into the mechanisms that drive the generation of immunogenetic diversity across vertebrates, while also creating a critical receptor-ligand framework for novel therapeutic development. This award was co-funded by SBS/DEB. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

There is an urgent need to reduce atmospheric carbon dioxide, the most abundant greenhouse gas that is driving global warming. Although much of the world’s CO2 is stored in plants, plants are carbon limited, particularly C3 plants which include many food and biofuel crops. This project will develop a simplified version of one of the alternative carbon-fixing pathways, the reductive TCA cycle, with the idea of introducing it into biofuel crops to increase CO2 fixation and plant productivity. Two of the enzymes that fix CO2 in this cycle are derived from thermophilic organisms that live at over 90 °C in hot springs and deep ocean vents. Enzymes active at high temperatures are less flexible at room temperature than related enzymes active at lower temperature. This project will develop an application of single particle cryoEM to analyze enzyme flexibility, where the PI will study cryoEM at the National Institute of Environmental Health Sciences (NIEHS). The PI will create a computational model of the simplified reductive TCA cycle that will then be used to characterize the cycle as a whole and predict how it will function in plants. In addition to training undergraduates, workshops will be held at North Carolina State University and at local minority-serving universities, expanding access to cryoEM. Finally, the PI will develop a capstone class for Biochemistry seniors, with an emphasis in combating global warming. The long-term goal of this project is to increase the yield of C3 plants by supplementing carbon fixation by Rubisco in the Calvin Cycle. The investigators will optimize a synthetic version of the reductive TCA cycle developed by researchers at NCSU composed of just five enzymes. The slow step of the cycle is catalyzed by two thermophilic enzymes: 2-Oxoglutarate Carboxylase (OGC) and Oxalosuccinate Reductase (OSR), enzymes that utilize ATP to capture bicarbonate from solution and carboxylate and reduce 2-oxoglutarate to isocitrate. An integral component of the research strategy is based on comparing the temperature dependent activity and dynamics of thermophilic and related mesophilic enzymes. Thermophilic enzymes are typically stable at room temperature but not very active due to reduced dynamics. The PI will develop a protocol to analyze enzyme dynamics by variable temperature single-particle cryoEM during a six-month sabbatical at NIEHS. Samples will be vitrified from different temperatures, between 4-50 °C, with the Leica vitrobot at NIEHS, capturing multiple conformations that reflect the dynamics at that temperature. The dynamics will be modeled by Molecular Dynamics simulation. Variable temperature techniques will be applied to OGC and OSR. In addition, the rate-limiting step of these enzymes will be identified via a series of enzyme assays. Predicted temperature-associated residues will be tested through mutagenesis. The PI will develop a high temperature vitrification protocol using a Linkham cryostage, where the vitrified sample is heated with a laser under liquid nitrogen vapor, then rapidly refrozen. Finally, the team will develop a computational flux model of the synthetic rTCA cycle, in collaboration with Megan Matthews at the University of Illinois at Urbana-Champaign. The computational model will provide perspective for the future optimization of the crTCA cycle and its application in plant hosts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Mathematical models of physical and biological systems are foundational in life science. In order for these models to provide reliable information for decision-makers, they must be rigorously analyzed and calibrated using advanced statistical and mathematical methods. The RTG project, Modeling and Uncertainty Quantification for Life Sciences (UQ4Life), will train a diverse group of students and postdoctoral fellows with the technical skills needed to advance mathematical and statistical research in UQ and the soft skills required to lead interdisciplinary teams. All research conducted by trainees will address important scientific problems and be conducted in collaboration with domain experts, ensuring that new knowledge will be disseminated beyond the mathematical and statistical communities. To extend the impact beyond NC State, the project includes a Distinguished Seminar Series and UQ Hackathon that are shaped and conducted by UQ4Life trainees and designed to engage with leading UQ researchers. The ambitious program is organized around three specific aims: (1) train data scientists with core proficiency in modeling and UQ methodology, (2) create an interdisciplinary culture to solve important life-science problems, and (3) strengthen and diversify the STEM workforce. To achieve these aims, the PI team have assembled a vertically-integrated team of researcher mentors from the Statistics and Mathematics Departments at NC State. Trainees will be given professional development mentoring, networking opportunities, and training and experience in communication and leadership. The interdisciplinary training provided by UQ4Life will result in significant advances in both methodology and application of modeling and UQ. Trainees will develop new methods for modeling life-science systems, Bayesian methods for model calibration and processing large datasets, and equation learning methods that use machine learning concepts to estimate dynamic non-linear relationships between state variables. These new methods will be motivated by important applications spanning the areas of climate, physiology, ecology, and disease modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Most insects carry bacteria that are inherited from their mothers and live within their cells. Some of symbiotic bacteria manipulate their insect hosts’ reproduction in ways that improves the reproduction of host insects carrying the bacteria. Some of these symbionts sabotage host sperm such that fertilized eggs laid by females without the bacterium die early in life (“cytoplasmic incompatibility,” or “CI”). Of the five bacteria known to cause CI, Wolbachia is best studied, yet Cardinium hertigii, the focus of this study, causes CI without the same genes Wolbachia uses. The overall goal of this proposal is to discover the molecular mechanism by which Cardinium causes CI. The project is expected to have important benefits: Cardinium targets animal cell division, a fundamental process that can be better understood when agents that interfere with it are studied. In addition, CI-causing bacteria may be used for pest or vector management. The CI-causing Wolbachia reduces host susceptibility to viruses of insects that carry it, and is currently being introduced to mosquito populations around the world to reduce vector-borne viral diseases. This project will also engage elementary, high school and undergraduate students from under-represented groups in scientific education and research through outreach programs and research opportunities at all three institutions. Furthermore, a Citizen Science project led by The University of Arizona and North Carolina State University will engage amateur entomologists in research through their support in collecting and rearing parasitic wasps of whiteflies to census for Cardinium. Maternally inherited bacterial symbionts of arthropods that manipulate host reproduction profoundly influence host biology and evolution. Symbionts causing CI sabotage host sperm such that fertilized eggs of uninfected females die in early embryogenesis. The independent evolution of CI in Wolbachia and Cardinium represents a remarkable case of convergent evolution of a complex trait. In recent NSF-supported work, we identified two putative Cardinium CI genes in a Cardinium strain that infects a parasitic wasp of whiteflies, Encarsia suzannae. In three objectives we aim to determine the mechanism of CI in Cardinium: 1. Dissect the mechanism of Cardinium cEper1 CI candidate genes using an integrated study of localization, discovery of interacting host and Cardinium proteins, and mutation analysis. 2. Express genes in Drosophila to verify the role of these candidates in CI. 3. Identify CI genes of cEina3 causing CI in E. partenopea and compare to cEper1. Cardinium produces a virtually identical CI phenotype to Wolbachia with different genes. Study of Cardinium CI genes in the non-model Encarsia as well as heterologous models yeast and Drosophila will increase our understanding of both Cardinium and Wolbachia, by showing us which processes are common and which are unique, and by shedding light on core host processes vulnerable to manipulation. Given the interest of the scientific and public health communities in symbiont-based pest or vector-management strategies, a deeper understanding of host-symbiont interactions in the comparatively understudied Cardinium may also lead to new contexts for applications, or for applications in which Wolbachia is not effective. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award supports research on developing privacy-preserving industrial data analytics methods that enable collaborative condition monitoring and decision-making among distributed manufacturing systems. In modern manufacturing sectors, comprehensive data analytics is critical for detecting anomalies, diagnosing faults, predicting the failure times of key assets, and optimizing operational decisions. The effectiveness of these data analytics models relies heavily on the amount of historical data available for model training. Unfortunately, individual manufacturing facilities often lack sufficient historical data on anomalies, faults, and failures to independently train effective monitoring and decision-making models. This research addresses this challenge with a novel solution that enables multiple geographically distributed manufacturing systems to collaboratively utilize their collective data to construct condition monitoring and decision-making models while keeping each system’s data local and confidential. By facilitating this collaboration, the project aims to overcome the limited data availability challenge and enhance the overall performance and reliability of manufacturing systems. This research helps enhance national economic competitiveness by improving manufacturing efficiency and reliability, aligning with the National Science Foundation's mission to promote the progress of science and advance national health, prosperity, and welfare. The project plans to develop a federated learning framework comprising four primary components: data curation, feature engineering, analytics and decision-making, and verification and deployment. This approach represents the first systematic solution for privacy-preserving collaborative condition monitoring and decision-making within the industrial asset management sphere. Techniques such as robust low-rank statistical learning for data curation, supervised dimension reduction for joint feature engineering, classification-based anomaly detection, regularization-based fault diagnosis, parametric statistical learning for prognostics, and federated optimization algorithms for operational decision-making will be developed. These techniques are designed to handle diverse data formats prevalent in manufacturing systems, such as time series, profiles, and image streams. Successful implementation of these methods will significantly advance the fields of federated learning, statistical learning, and industrial data analytics, providing substantial benefits across manufacturing and service industries by improving equipment reliability, reducing costs, and preventing failures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Tidal flooding is occurring at increasing rates in coastal communities due to sea-level rise. Recent research demonstrates that tidal floodwaters can be contaminated with fecal matter from human and animal sources. However, we do not yet know what causes fecal contamination of floodwaters, or what are the risks to public health resulting from these contaminated floodwaters. The goal of this project is to characterize risks posed to people who encounter floods either directly, or through recreation in coastal waterways that receive floodwater drainage. To achieve this goal, the project team will measure pathogens and microbial markers in floodwaters to assess fecal contamination sources and develop models to predict public health risks. The movement of floodwater into coastal waters will be tracked and modeled, and fecal bacteria will be measured to understand risks faced by swimmers and other recreators. Society will benefit from the results of this research as stakeholders can use the information to protect human health from emerging threats posed by sea-level rise. Additional benefits to society result from findings that may lead to the development of new engineering approaches for preventing or eliminating tidal floodwater contamination. Educational and research training will increase scientific literacy and help develop the Nation’s STEM workforce. Older stormwater drainage networks in coastal areas that were designed to move rainwater away from developed areas now act as conduits for tides and rising seas to flow into communities, resulting in “tidal floods.” Recent research demonstrates that floodwaters often have elevated fecal bacteria concentrations indicative of hazardous contamination, potentially driven by sewage system failures. How these tidal floods mobilize and transport microbial pollutants is poorly understood, as are the impacts on coastal water quality. The goal of this project is to establish a quantitative understanding of the causes and risks from failing coastal stormwater infrastructure under sea-level rise. The specific objectives designed to achieve this goal are to: i) Characterize microbial risks through measurement of indicator organisms, pathogens, and microbial source tracking targets; ii) Determine the fate of contaminated floodwaters in adjoining recreational waters using measurements and a coupled hydrodynamic model; and iii) Evaluate the contribution of contaminated tidal floodwaters to adjoining coastal water quality. This project will provide the first quantitative measurements of microbial risk associated with exposure to tidal floodwaters, creating foundational insight on how coastal communities encounter water quality hazards due to tidal floods. These findings will contribute to a mechanistic understanding of tidal flood hydrodynamics that environmental regulatory agencies and public health officials can use to determine when mitigation actions should be implemented to address tidal flood risk. 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 award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries. The teams will develop transdisciplinary and convergent research approaches on cultural heritage and climate change, foster collaboration among the research community across several regions, and contribute to knowledge advances at the global level. This project seeks to create a framework for identifying climatic indicators that are applicable to coastal cultural heritage which can be used to improve climatic risk assessments by connecting potential impacts with different local, societal, economic and environmental factors such as wellbeing, sustainable culture and tourism, and infrastructure. The project will develop a set of indicators of climate change, along with a locally adaptable protocol for stakeholders’ engagement in identifying critical climate impacts that are relevant to local and cultural contexts. The project team will develop a climate adaptation decision support tool to evaluate assessment approaches across different spatial scales. The framework will not only help to better integrate different knowledge streams in climate risk assessments, but also to improve coordination and collaboration between different decision-makers involved in cultural heritage preservation. The project seeks to help provide relevant data through several case studies that can serve as examples for developing cultural heritage policies and practice. 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

Human communication has traditionally been dependent on sensory systems such as seeing, hearing, and/or touch, but words and symbols that are available to senders and are understood by receivers still limit most current communicative methods, even when they include non-verbal content. Brain-to-brain interface (B2BI) is an emerging technology that combines sensing the brain (brain-computer interface, or BCI) and stimulating the brain (computer-brain interface, or CBI) to enable communication between two brains directly through their neural activities. A BCI (for example electroencephalography- or EEG-based motor-imagery or MI BCI) reads a sender’s brain activity and dispatches information to a CBI (for example, via transcranial magnetic stimulation), which activates a receiving brain, thereby facilitating direct brain-to-brain communication. Since its proof of concept in 2013, B2BI has been demonstrated in both animal models and human subjects where the same or different brain regions are recorded and stimulated in a variety of interesting contexts. Despite exciting advances in B2BI, there are still major gaps and barriers that need to be addressed, including but not limited to the lack of B2BI that work directly with neural information instead of indirectly through computer interpretation. The intellectual merit of the project lies in its innovative and integrative approach to developing an emergent neural interface technology that enables an individual’s brain to communicate with another’s by bypassing sensory exchange and language entirely. Project outcomes will have broad impact in medical applications such as enhanced communication with behaviorally non-responsive or less-responsive patients, neuro-rehabilitation for stroke victims, and ultimately advanced communication for healthy users. The research will help facilitate the adoption of high-accuracy, image-guided, ultrasound technology in B2BI applications, and will produce the first truly bi-directional B2BI thereby laying the groundwork for the next step in human communication. The overarching goal of this exploratory project is to create a direct bidirectional B2BI using non-invasive technology that combines a contemporary EEG-based MI BCI as the neuro-imaging technology and transcranial focused ultrasound (tFUS) as the neuro-stimulation technology. To this end, two objectives will be pursued: First, we will determine the optimal parameters (including, duty cycle, inter-stimulus interval, and acoustic intensity) of the proposed excitatory tFUS. These parameters have been used in other studies, but not in a comparative analysis to determine the ideal combination. Furthermore, tFUS has been sparingly used with humans in a B2BI. Experiments will be conducted to develop and test parameters for tFUS modulation of the human brain, especially determining what values of key parameters produce the greatest excitatory response in a participant’s brain. Then, a direct bidirectional B2BI built upon a MI BCI and the tFUS system resulting from the first objective will be assessed, with a focus on healthy human subjects in a more realistic task setting than those used in previous studies. The approach aims to replace the peripheral nervous system device with another BCI and CBI component, allowing brain information to be transmitted in both directions around the loop. Two research questions to be addressed include: Can the MI and tFUS based bidirectional B2BI system allow subjects to perform better than chance in a bidirectional collaborative task? and, Do the more detailed measures of performance (AUROC, bit rate, mutual information, and classifier accuracy) vary with task condition? 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

The massive amounts of genomic DNA in the cell must undergo major structural transformations when genetic information is replicated or repaired. During these processes, branch-shaped DNA structures are formed as intermediates and then are processed into linear DNA products. Many protein factors collaborate during the formation and processing of different types of branched DNA structures. How many of these proteins interact with branched DNAs and enable their processing is not clear. The objective of this collaborative project is to determine how a genome-protecting complex called Smc5/6 manages various branched DNA structures. The two research teams will combine biochemical and biophysical expertise to achieve high-resolution understanding of Smc5/6 structure and function. The outcomes will advance knowledge of how cells quickly resolve DNA repair intermediates to ensure genome integrity. In addition, the project will have educational impact through workshops that cultivate interest in DNA and genome research among middle-high school students and teachers, as well as interdisciplinary research training opportunities for undergraduate and graduate students. Smc5/6 is emerging as an important and highly conserved protein complex that is required for the processing of DNA repair intermediates. However, the mechanism of action of Smc5/6, including its interactions with branched DNA structures is poorly understood. This study will provide critical insights into the molecular functions of Smc5/6. Specifically, the two research teams will examine how Smc5/6 recognizes Holliday junction recombinational repair intermediates and R-loop RNA-DNA hybrid structures, using complementary biochemical and high-speed atomic force microscopy (HS-AFM) methodologies. In addition, HS-AFM imaging will be used to directly observe the sequential steps whereby DNA helicases resolve these structures and how Smc5/6 affects these steps. By integrating dynamic HS-AFM data with biochemical data, the project has potential to provide novel insights into DNA repair. Given the importance of Smc5/6 in genome maintenance and its evolutionary conservation in eukaryotes, this new knowledge could illuminate DNA repair mechanisms in diverse organisms. 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

Modern applications of statistics aim to solve complex scientific problems involving high-dimensional unknowns. One feature that these applications often share is that the high-dimensional unknown is believed to satisfy a complexity-limiting, low-dimensional structure. Specifics of the posited low-dimensional structure are mostly unknown, so a statistically interesting and scientifically relevant problem is structure learning, i.e., using data to learn the latent low-dimensional structure. Because structure learning problems are ubiquitous and reliable uncertainty quantification is imperative, results from this project will have an impact across the biomedical, physical, and social sciences. In addition, the project will offer multiple opportunities for career development of new generations of statisticians and data scientists. Frequentist methods focus on data-driven estimation or selection of a candidate structure, but currently there are no general strategies for reliable uncertainty quantification concerning the unknown structure. Bayesian methods produce a data-dependent probability distribution over the space of structures that can be used for uncertainty quantification, but it comes with no reliability guarantees. A barrier to progress in reliable uncertainty quantification is the oppositely extreme perspectives: frequentists' anathema of modeling structural/parametric uncertainty versus Bayesians' insistence that such uncertainty always be modeled precisely and probabilistically. Overcoming this barrier requires a new perspective falling between these two extremes, and this project will develop a new framework that features a more general and flexible perspective on probability, namely, imprecise probability. Most importantly, this framework will resolve the aforementioned issues by offering new and powerful methods boasting provably reliable uncertainty quantification in structure learning applications. 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

DNA simple sequence repeats (SSR) within the genome are unstable and tend to expand after sequence-specific thresholds. SSR expansion is a major cause of over fifty neurological and neuromuscular diseases. The trigger for expansion occurs during fundamental cellular processes,such as replication, in which DNA forms structures that are different from the standard Watson-Crick, double-stranded helix. This project seeks to uncover mechanisms that underlie SSR expansion, including the destabilizing role played by nonstandard DNA and RNA structures as well as the role of the DNA mismatch repair (MMR) system that, instead of repairing the mutations, paradoxically enhances their toxic expansions. In addition to its scientific impact, this collaborative research project will train several students. The larger Biophysics community will also benefit through continued development and support of our freely available simulation software as released via the national AMBER simulation package. Additional goals include fostering minority student education and enhancing undergraduate physics education. This collaborative project amongst three researchers will combine single-molecule fluorescence resonance energy transfer (smFRET) experiments with atomistic molecular dynamics (MD) investigations focusing on the structural, mechanistic, and dynamical aspects of nucleotide and mismatch repair (MMR) complexes to elucidate their role in SSR expansions, and the role of atypical nucleic acid structures that hijack the MMR system. Surprisingly MMR, which acts after DNA replication to maintain genomic stability, has been associated with mutagenic action related to SSRs. To understand the crucial role played by atypical nucleic acid secondary structures, the combined computational and experimental investigations will focus on DNA three-way junctions and R-loops that form during transcription. The project will map out possible dynamical pathways for R-loop generation through the interplay of negative superhelicity and relative stability of the atypical structures, such as hybrid triplexes that can form during bidirectional transcription. To unravel the workings of the DNA MMR system, the project investigates the structure and function of the complexes formed between MMR proteins and SSR DNA. 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

Autonomous Driving Systems (ADS) are software systems designed to reduce or replace human involvement in driving vehicles. Improving ADS safety is critical to achieving road safety. Physical testing of ADS-driven vehicles, albeit important, is limited. Testing autonomous vehicles on city roads does not scale and, more importantly, can be unsafe. For these reasons, simulation-based testing has been shown to be of fundamental importance to ADS quality assurance; it enables developers to assess ADS robustness before deployment. Simulation-based testing relies on high-fidelity simulators. Developers “plug” their ADS into a simulation platform and run tests largely consisting of a route definition within a map with static and dynamic obstacles. Despite recent advances in techniques for simulation-based ADS testing, three important challenges remain. First, finding failure-revealing inputs using simulation is costly. Second, existing techniques often report duplicate infractions that do not contribute to information gain. Third, existing techniques make unrealistic assumptions about the environment. For example, they assume a single ADS-driven vehicle in the simulation. This project proposes novel approaches to mitigate these fundamental challenges, advancing the state of the art in simulation-based ADS Testing. In this project, machine learning-based techniques will be used to address the fundamental limitations of simulation-based testing for ADS. One research goal is to improve the efficiency and effectiveness of simulation-based testing by leveraging the risk signals produced by anomaly detectors during a simulation to improve the ability of simulation-based testing to detect infractions within a given time budget. A second research goal is to improve the relevance of testing by leveraging semantic information embedded in parts of a simulation to deduplicate infractions that testing techniques report. A third research goal is to improve the realism of simulation-based testing by using real-life data and human input. 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

The metaverse ecosystem, an integrated fusion of physical, digital, and virtual realities, presents a plethora of advantages for both research and industry, including accelerated system development, enhanced productivity, and global real-time interactions. Despite extensive research efforts toward implementing metaverse systems across various domains, a notable deficiency exists in addressing the critical security and privacy issues within this immersive mixed-reality landscape. The intersection of virtual and physical realms gives rise to distinctive security concerns, encompassing inter-realm adversarial attacks creating counterfeit representations, the risk of information leakage across distributed virtual reality devices, and the exposure of user privacy at both the network and application levels. These less-recognized threats arise from the intricate interplay and added complexity of the digital realm, coupled with the reliance on cutting-edge technologies like digital mapping, machine learning, and data analysis in mixed environments. In response to these challenges, the project’s novelties are pioneering strategies to fortify the metaverse ecosystem, with the goal of creating secure, resilient, and privacy-enhanced digital world experiences. The project's broader significance and importance span digital twin networks, manufacturing, and automation testing, where the developed security and privacy prevention techniques can be employed in various data analysis tasks, including medical data processing, road traffic prediction, user mobility, and trajectory predictions. The project integrates the research insights into new modules for computer security and privacy courses and hosts outreach activities like Data Privacy Week, with the vision of advancing the participation of all students in STEM fields and improving STEM education. This project addresses security and privacy challenges in emerging metaverse ecosystems, drawing from interdisciplinary knowledge in cyber-physical systems, machine learning, traffic analysis, and usable privacy. The project places emphasis on advancing three interconnected facets: 1) Developing innovative defense mechanisms to protect digital mapping and synchronization against adversarial manipulation, including a resilient digital representation approach and a mask-and-trim strategy to replace adversarial inputs during the synchronization process. 2) Creating novel privacy-preserving distributed training methods that enable collaborative ML model training for virtual reality users. This involves quantization-based federated learning methods to optimize learning accuracy, data leakage, bit quantization, and energy consumption. 3) Introducing novel privacy controls for virtual reality end users. This entails developing techniques to thwart behavioral inference both at the network layer (through traffic) as well as the application layer (through embedded sensors) using obfuscation techniques such as differential privacy. The overall design aims to establish a safe and trustworthy digital environment for users worldwide. 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

Proliferating blue technology systems, such as offshore subsea energy systems, remotely-operated and autonomous underwater vehicles, ocean exploration systems, and subsea oil extraction, greatly benefit from electric power processing units positioned underwater close to electrical loads. The use of thick metal Pressure-Tolerant Cylinders (PTCs) is the prevailing method to enclose electronic circuits and shield them from hundreds of bars of surrounding hydrostatic pressure while maintaining one bar inside. This approach has numerous weaknesses, including high cost, buoyancy problems, complicated cooling, frequent maintenance, and implosion or leaks due to penetrators and connectors. Exposing bulky power electronic components and systems to the surrounding pressure would solve these drawbacks, but the present knowledge of electronic components and systems operating under extreme hydrostatic pressure is insufficient and unsystematic. Moreover, pressure-instigated high failure rates and parameter drifting of critical filtering devices (electrolytic/film capacitors and inductors) require rethinking the traditional power converter topologies for pressure-tolerant operation. This research proposes a systematic fundamental study of electrical components and system behavior at high hydrostatic pressure. Researchers will focus on developing a test methodology, understanding fundamental electrical, thermal, and pressure relationships, and establishing critical models to describe the impact of pressure and pressure-compensated environments on power components and systems. The study will jointly treat electrical, thermal, and pressure-related factors and utilize derived models to design, build, and test three exemplary power conversion units in a pressure-compensated environment. The research focuses on critical passive power components (inductors and capacitors) and switches, particularly emerging high-power Wide Bandgap semiconductors. This will reduce the cost of underwater energy systems, improve reliability and efficiency, and simplify cooling and maintenance, simultaneously benefiting marine exploration by accelerating the deployment of reliable and lower-cost underwater test stations, habitats, vehicles, and microgrids. Four critical research questions will be addressed: i) identifying hardware resources and critical electric test procedures for component parameter evaluation at hydrostatic pressure up to 10,000 psi (1 psi = 6895 Pa), ii) deriving analytical and empirical electro-thermal-pressure models of selected power electronic components, iii) understanding pressure-induced deterioration and fault mechanisms, and iv) setting guidance for component packaging, material selection, and converter topologies with a practical demonstration on selected power conversion systems. Experimental validation will be at pressures up to 10,000 psi, effectively covering the operating conditions of ~99% of all Earth's oceans, seas, and lakes. 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

Development is a fundamentally conserved and constrained process, but major changes in development mode have evolved, even across closely related species and short evolutionary time scales. This research investigates the genomic differences that shape developmental evolution by using closely related marine annelid species that exhibit different development modes. The project combines comparative genomics, gene expression, and hybridization assays to uncover the relationship between evolutionary divergence and development. This approach will inform our understanding of how speciation and developmental differentiation begin at the molecular level. The research is integrated with an education program designed to expand retention of undergraduate students in STEM fields as well as enhance access to early research and mentoring experiences. The education goals include establishing new faculty/student and peer-mentoring opportunities to increase participation of students in evolution, genetics, and developmental fields and providing new opportunities for research in coastal habitats. Finding the genetic basis of developmental change is a critical component of understanding diversification and evolution. This project identifies the regulatory differences that create phenotypic change during development using a unique set of species, including the emerging developmental model Streblospio benedicti. This marine annelid produces two distinct offspring types that differ in egg size, embryogenesis, and larval life-history within a single species. By identifying genes that are expressed differently based on life-history mode, within and across species, gene expression changes will be linked to developmental consequences. This comparative framework will reveal both the general rules of developmental evolution, and the specific genes involved in life-history switches. The research approach allows for separation of genetic differences that are species-specific from ones that are functionally associated with changes in embryology and life-history. Dissecting gene regulatory evolution at such a fine evolutionary scale will lead to a detailed understanding of how development evolves. 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 · 2024-04

Peptides are used in numerous applications: from discovery of biochemical threats to the public, to detection of molecular markers of disease, to purification of therapeutics. The goal of this project is to develop algorithms that could be used to design peptides to bind to a single target protein. To help test the algorithms and tailor them to the needs of a variety of researchers, the team will partner with scientists and engineers to perform experiments to see if the designed peptides work. The projects include designing peptides for use in separations, counteracting bacterial infections, and early detection of cancer. The methods developed should provide fast and effective guidance to industry and academia on which peptides to choose for which applications. The PI will continue to promote inclusion of underserved groups in science by meeting with women graduate and undergraduate students. A video presentation about peptide design aimed at general audiences will be created and posted on YouTube. A fun iPad app/game/puzzle to illustrate the challenges associated with binding between peptides and proteins in drug development will be crafted for use by high school and college teachers. This Computational and Data-Enabled Science and Engineering (CDS&E) project will provide new software and data analysis methods to accelerate discovery in a variety of engineering and bioscience disciplines that have peptides as their common thread. The goal is to develop user-friendly software that could be used to design short flexible linear peptides to bind to a single target protein. The algorithm will be tailored to modern problems requiring peptides: that are designed de novo (without reference peptide), that mimic antibody tips, that bind to surfaces, that bind to one protein but not another, that bind to two similar proteins, or that link two different proteins. To help test the code and tailor it to the needs of a variety of researchers, the team has partnered with four members of the engineering/science community, each of whom has conceived a “demo project” --- the Hall lab will design key peptides needed in the partner’s research, and the partner will do experiments to see if the designed peptides work. The projects include designing peptides: to attach to resins in chromatography, act as molecular glues to precipitate antibodies, deactivate toxins in bacterial infection, bind membrane-spanning proteins on exosomes, and mimic antibodies that bind to closely related variants. The software will include a machine-learning component to predict peptide affinity based on the amino acid sequence and the structure of the peptide-protein complex. This work should have a transformative long-term impact on researchers in a variety of fields who need specialized peptides for their applications but are not modelers, or not comfortable with modeling. 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.

Other NSERC · FY 2024

Soft matter physics, Fluid dynamics, Condensed matter physics

Other NSERC · FY 2024