University Of Houston

NSF Awards · FY 2024 · 2024-12

Metabolomics is an indispensable approach in systems biology that uses analytical techniques to measure metabolites in cells, tissues, and biofluids and provides direct information of the biological phenotype. Nuclear magnetic resonance spectroscopy (NMR) is a powerful tool for metabolomics due to its excellent analytical reproducibility and ability to detect numerous metabolites in a single measurement. NMR metabolomics is conventionally performed on a high-field (HF) spectrometer, but the recent development of benchtop spectrometers has led to a resurgence of interest in low-field (LF) NMR due to its accessibility, low cost and small footprint compared to HF. NMR metabolomics at both HF and benchtop LF, however, require time-consuming, user-dependent processing and expertise for metabolite identification and quantification. Due to these limitations, both HF and LF NMR are underexplored for metabolomics research in the biological community. This project will fill these critical gaps by developing, validating, and disseminating real-time, high-throughput NMR metabolomic techniques for both HF and benchtop LF NMR for advancing biological infrastructure and research. This interdisciplinary project will prepare the next generation of women and minorities to pursue bioengineering and bioinformatics career—currently an under-represented discipline. The project will also integrate research with educational objectives to target the broader community from K–12 to graduate students and the general public: (a) coursework to strengthen biosignal processing & analysis skills in undergraduate and graduate curricula; (b) internships, targeted to talented women, minority, and low-income college students; (c) hands-on STEM projects to motivate high-schoolers through collaboration with school teachers; (d) disseminate bioengineering research to support K–12 learning through the Society of Women Engineers, West TN STEM Hub, and Girls Experiencing Engineering programs; and (e) educational exhibits at local museums to enable public outreach and exposure to NMR applications. This early hands-on exposure will benefit students of all ages to understand fundamental concepts and realize NMR applications in a broad range of fields—including molecular biology, biomedical engineering and chemical engineering—and ultimately motivate them to pursue a STEM career. The project will develop, validate, and disseminate open-access metabolomic techniques that will automatically quantify the metabolites in complex biological spectra obtained from high-field (HF) and benchtop low-field (LF) NMR via the following objectives: 1) investigate high-throughput metabolomic methods for HF NMR using deep learning, 2) reconstruct high-resolution and high-throughput spectra from benchtop LF NMR using autoencoder, 3) investigate these techniques for inquiring biological questions, and 4) disseminate metabolomic libraries and techniques for biological research and education via an open-access software. This research will provide a breakthrough in the field of NMR metabolomics by eliminating a major processing barrier for both HF and benchtop NMR, thus making NMR an accessible and effective analytical tool to the biological community. The results of this project will be available at the institutional website: https://www.memphis.edu/mrisl/projects/index.php 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 · 2024-11

Industrial control systems (ICSs) are commonly utilized in critical infrastructures, including power, water treatment and distribution, and transportation. However, the increasing digitization of ICSs, involving sensing, communication, and control, brings advanced features but also exposes vulnerabilities to malicious cyber-attacks. Protecting ICSs from such attacks is crucial due to the potential catastrophic physical damages they can cause. The project aims to develop a comprehensive solution, integrating human-on-the-loop explainable machine learning (ML), detection, and recovery control in an operator-automation shared protection framework, to provide security and safety-assured ICSs against malicious cyber-attacks. Moreover, the project will incorporate engineering research and education to train students, particularly those from Under-Represented Minorities (URM), and cultivate a diverse, globally competitive cybersecurity workforce. With the goal of lowering barriers to ICSs security research and education, this project aims to have a significant impact by providing accessible testbeds for a diverse population of beginning and expert cybersecurity students and engineers to learn and practice. The underlying concept of process anomaly detection, which is used for detecting cyber-attacks, involves comparing observed and expected behaviors based on physical invariants. The data-driven approach has the advantage of automatically discovering these physical invariants without requiring domain expertise. However, existing approaches based on black-box Machine Learning (ML) often overlook the role of system operators in safety-critical ICSs. The lack of insightful explanations in detection results hinders system operators from conducting troubleshooting and isolating anomalous sensors and actuators under attack, which is necessary for scheduling online recovery. To address this issue, the PI's team proposes to develop an operator-automation shared protection framework that unifies human-on-the-loop explainable ML, detection, and recovery control. This framework aims to enable real-time decision-making using cutting-edge ML and control techniques while valuing the feedback of human operators to prevent over-trust in autonomy in a safety-critical system. The research project has three major objectives: 1) The PI's team will develop insightful hybrid automata learning that captures physical invariants in a way that system operators can understand the model and the detection results, verify and correct the model, and localize anomalies; 2) A real-time provably safe control under uncertainty will be designed to restore the system to normal operation without violating safety constraints; and 3) the PI’s team will evaluate, demonstrate, and disseminate best practices of the proposed framework on 3D simulated and real testbeds with portability across a wide range of ICSs for lowering the barriers to ICSs security research and education. 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 · 2024-10

Watershed boundaries, or drainage divides, control the flux of water and sediment in a landscape and drive evolution and speciation of aquatic organisms. Over geologic time, drainage divides move in response to changes in climate and tectonics. This work explores how the motion of divides differs between gently sloping, soil-mantled landscapes and steep, mountainous, rocky areas. Specifically, the researchers will compare landscapes where soil creeps along hillslopes with those where material is primarily transported through landslides. They will use digital mapping techniques, field measurements of soil and sediment characteristics, and theoretical models of landscape evolution to investigate the ingredients that affect divide motion. Unlike previous research, which focuses on river channels, this work emphasizes processes that occur on hillslopes. Topographic analysis, field measurements of grain size, and erosion rates from cosmogenic nuclides illustrate the role of non-fluvial processes in modulating topographic change of drainage basins. To capture hillslope and colluvial transport regimes in landscapes ranging from soil-mantled to rocky, this work leverages measurements from field sites across a range of regional erosion rates (Ozark Plateau, Oregon Coast Range, San Gabriel Mountains). In addition to testing existing topographic metrics for divide instability, this work seeks quantitative methodological improvements to better incorporate steep landscapes into theoretical frameworks that describe and predict divide instability. 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 · 2024-10

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Texas at El Paso (UTEP) and the University of Houston (UH). Both UTEP and UH are members of the Alliance of Hispanic Serving Research Universities (HSRU), an association of Hispanic-Serving Institutions with high research activity, affordability, strong community engagement, and commitment to serving first-generation and diverse students. UH is also an Asian American, Native American, and Pacific Islander Serving Institution. Over its one-year duration, this planning project will establish the necessary infrastructure and collaborations to lay the foundation for a future Track 3 S-STEM proposal to award scholarships to talented, low-income students pursuing a graduate degree in biomedical engineering or engineering technology with foci on machine learning (ML) and artificial intelligence (AI). Strategies will be developed to support student academic and career pathways, aligned with each institution's contexts and resources. This project will also develop a research plan to investigate the experiences of S-STEM scholars through an asset-based framework meant to recognize and leverage students' individual strengths. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. The project will focus on recruiting and supporting scholars in biomedical engineering (UTEP), computational health informatics (UH), and biotechnology (UH) to meet the national demand for professionals who understand and can apply ML and AI to biomedical problems. The central goal of this planning effort is to develop the basis for a multi-institutional project by: (1) identifying and recruiting faculty to participate in the collaborative partnership; (2) identifying institutional, systemic, and programmatic barriers for potential scholars; and (3) developing an asset-based graduate-level training framework. This project will identify evidence-based curricular and co-curricular activities for future scholars; engage the respective collaborating institutions' Financial Aid Offices to determine each institution's definition of low-income status; and establish inter-institutional agreements to benefit scholars at both institutions. Results will be disseminated among Texas minority-serving institutions and the Alliance of Hispanic-Serving Research Universities. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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 · 2024-10

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need pursuing degrees in chemical engineering at Prairie View A&M University, the University of Houston, and the University of Kentucky. Despite efforts to improve retention and graduation rates in engineering, challenges persist across these three institutional contexts (a Hispanic-serving Institution, a Historically Black College & University, and a Predominantly White Institution in an EPSCOR jurisdiction), as well as in the broader engineering community. Data from these institutions show a relationship between financial stress, mental health issues, and reduced academic performance among engineering students. This planning grant will use student focus groups to enable the identification of services that would provide financial, engineering identity, and wellness support for students enrolled in chemical engineering programs. Interventions will effectively account for the culture of chemical engineering as a course of study and incorporate students as co-creators of knowledge around what it takes to support student success, well-being, and retention. The planning activities will inform a future Track 3 S-STEM proposal that will support scholars across the three collaborating institutions. The overall goal of this project is to understand how to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Over its one-year duration, this Collaborative Planning Grant project will identify interventions to support the financial stability, engineering identity, and wellness of undergraduate students enrolled in chemical engineering programs. Existing interventions to improve student support are often institution-centric, lack supporting evidence, or do not consider the unique aspects of disciplines. They also tend to overlook student insights as an important part of developing new practices and generating knowledge. This project will enable the development of the infrastructure, programmatic supports, and campus-level relationships necessary to facilitate the development of a robust student support network. Action research will be used to identify and develop stakeholder-driven interventions to support student success and sense of belonging. These interventions will be integrated into a future Track 3 S-STEM proposal that will provide financial, engineering identity, and wellness support for students. This project is funded by NSF’s S-STEM program, which seeks to increase the number of low-income, academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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 · 2024-10

Federated Learning (FL) has emerged as a popular distributed machine learning paradigm in a wide range of sectors (e.g., healthcare, fintech, and autonomous driving) because of its potential of protecting people’s privacy - it does not require gathering all the data in one place for operation. Meanwhile, driven by the increasing ubiquity of mobile devices, FL applications are shifting from wall-plug powered artificial intelligence (AI) devices to battery-powered mobile AI systems (e.g., smartphones, tablets, wearables). Existing research largely ignores the role battery energy awareness plays in efficient FL training over mobile AI systems. This project addresses this challenge and innovates on developing an energy-efficient FL framework for mobile AI systems, making these systems more suitable for execution on everyday mobile devices without draining their batteries quickly. The project's broader significance and importance are its potential to advance mobile computing and AI technologies, ensuring both are energy-efficient and privacy-preserving. Furthermore, this project shares its research artifacts and results with the community and includes educational activities targeting under-represented groups in computing. This project investigates the efficiency, quality, and robustness of FL systems from an energy perspective, aiming to develop a comprehensive energy-efficient FL framework for mobile AI systems. The research is structured around three synergistic objectives. First, the project develops a universal energy estimation methodology applicable across a variety of devices engaged in FL training, incorporating Deep Neural Network (DNN) models with diverse architectures. Next, utilizing insights into energy consumption, the project explores strategies to enhance the energy efficiency of FL, particularly in high-speed communication scenarios such as autonomous driving and augmented/virtual reality. Additionally, the project integrates learning performance metrics, such as accuracy and latency, with energy parameters—including energy consumption and battery life—in the FL participant selection process. This integration aims to create a balanced and optimized learning environment. To support these goals, the project establishes a mobile AI testbed and energy measurement setup, equipped with real-world FL benchmarks and workloads. 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 · 2024-10

The central nervous system includes both neurons and glial cells, which consist of astrocytes and oligodendrocytes. Glial cells are essential for the function and maintenance of the nervous system, and they play critical roles in tissue repair following neural injury. Surprisingly, how these cells originate during development has remained poorly understood. The proposed studies investigate the molecular signals required for the formation of glial cells during the development of the nervous system in the African Clawed Frog, which is an important animal model system for studies of embryonic development, cell biology, and neuroscience. Initial studies have delineated similarities and specific differences in glial development between frogs and mammals, and these differences may reflect new routes for the activation of genes required for glial development. These studies should reveal new mechanisms for the establishment and maintenance of glial cells. Since altered gene expression in glial cells can lead to forms of cancer or neurodegenerative diseases, the results of this work should expand the current view of how such alterations could arise in humans. They may therefore identify new opportunities for the development of therapies to reverse these altered patterns of gene expression and thus prevent the appearance or progression of disease. These studies will also provide a foundation for the development of instructional modules in science education through a collaboration with TeachHOUSTON, a University of Houston program that trains undergraduate STEM majors to become science teachers. The proposed work investigates transcriptional regulatory mechanisms underlying glial development in Xenopus laevis. The response of glial cells to injury and their roles in neural regeneration and repair suggest that glial lineages may have distinct developmental mechanisms to retain plasticity, and possibly even developmental potency, long after differentiation. Moreover, embryonic radial glial cells perform key glial functions (e.g., glutamate uptake) while retaining the capacity to initiate neurogenesis, a striking exception to the conventional view of cell fate specification. Preliminary results reveal divergence from the mammalian model of gliogenesis, anteroposterior regionalization of glial development, and a key role for retinoic acid, which mediates anteroposterior regionalization within the neural ectoderm. The proposed studies will examine specific roles for retinoic acid in the initiation of gliogenesis in the anterior spinal neurectoderm and the expansion of gliogenic gene expression throughout the embryonic brain and spinal cord. They will also identify genes and cis-regulatory regions associated with the emergence of glial lineages through a multi-modal analysis incorporating single-nucleus profiling of gene expression and chromatin accessibility. These results will advance current understanding of vertebrate glial development and the transcriptional regulatory networks underlying the emergence and maintenance of glial lineages. 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 · 2024-10

The number of STEM postdoctoral researchers has increased more than threefold over the past 40 years due to the need for the United States to develop a well-equipped STEM workforce. While research training and publication have traditionally been standard in STEM postdoctoral research training programs, these programs often lack structured and formal community support. Because many postdocs transition into future positions involving various amounts of teaching, research, and service that involves their local and connected communities, it is important for postdoctoral training programs to provide effective training in all these areas. To address the need for improvements in postdoctoral training programs, the project team seeks to develop three independent STEM education researchers equipped with distinctive skills in building community-engaged research-practice partnerships. The project aims to prepare postdoctoral researchers through structured cohort-based training designed to enhance the six core competencies outlined by the National Postdoctoral Association as important for postdoctoral success. These core competencies include discipline-specific conceptual knowledge, professionalism, enhanced research skills, responsible conduct of research, communication skills, and leadership and management skills. The project team plans to recruit postdoctoral researchers from a large network of scholars by recruiting at academic conferences, virtual recruiting events, and through electronic databases. Recognizing that some applicants will have had access to more resources and opportunities than others, the project team plans to prioritize applicants’ potential for future success based on the information provided in their cover letter coupled with the recommendations provided by experts in their field. To achieve the goals of this project, the project team plans to leverage existing collaborations between the project team and partners in the surrounding community, Houston’s Historic Third Ward, to provide postdoctoral scholars with community-engaged research opportunities. The project design is grounded in theoretical and conceptual frameworks that facilitate learning by doing and benefits from the expertise that each postdoctoral researcher possesses. Postdoctoral trainees will be immediately engaged in existing research projects led by the project team, attending weekly research and mentoring meetings, having the opportunity to audit research methods courses of their choosing, and enrolling in the ProQual Institute of Interpretive Research Methods. The project also aims to implement a professional development plan that includes onboarding, an individualized development plan, monthly networking, immersive teaching and mentoring experiences, and participation in training on topics such as proposal preparation and academic writing. This project is funded by the Science, Technology, Engineering, and Mathematics (STEM) Education Postdoctoral Research Fellowship Program (STEM Ed PRF) with co-funding from the Advancing Informal STEM Learning (AISL) and EDU Core Research: Building Capacity in STEM Education Research (ECR: BCSER) programs. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to enhance their ability to engage in fundamental and applied research that advances knowledge within their field. The AISL Program is committed to funding research and practice, with continued focus on investigating a range of informal STEM learning (ISL) experiences and environments that make lifelong learning a reality.  The ECR: BCSER Program is designed to build the capacity of individuals to carry out high-quality, fundamental STEM education research that will enhance the nation’s STEM education enterprise. 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 · 2024-10

This project will lead to advances in dealing with data challenges to facilitate fairness in machine learning, promote broad utilization of machine-learning algorithms in high-stake applications, and ensure a fair and transparent decision-making process for future information systems. While machine-learning methods have achieved success in real-world applications, they often suffer from biases and show discrimination towards certain demographics especially in high-stakes applications, which risks significant harm to both society and individuals. Existing work focuses on “model-centric” computational approaches that build models while overlooking the importance of data quality. To tackle the challenges raised by the lack of high quality data and the lack of a comprehensive understanding of fairness in all its respects, this project will integrate model-centric with “data-centric” modeling, which systematically engineers the data needed for a fair decision-making process. The successful outcome of this multidisciplinary research will lead to effective and efficient algorithms that enhance the generalizability and trustworthiness of learned models, and improve the fairness of algorithms deployed in real-world systems in health informatics and disaster resilience. The education programs of this project will play an integral part in training the next generation of the U.S. workforce with critical Responsible Artificial Intelligence (RAI) technologies and attract and retain diverse members of the future workforce in STEM. The research goal of this project is to develop a computational framework for tackling data challenges in fairness through data-centric fairness mitigation solutions that explore and exploit data and prior knowledge. Complementing existing studies focusing on model-centric or data-driven approaches, this project investigates a novel research direction that systematically explores a data-centric fairness mitigation framework. Specifically, the research objectives include: (1) to explore and extract data characteristics on instances, features and a representative subset of examples in terms of fairness, allowing that fairness definitions and metrics may vary across real-world applications; (2) to expand and refine prior knowledge to guide the discrimination-mitigation process via instance augmentation, feature set expansion, and measurement redefinition perspectives; (3) to leverage interpretable and interactive data and prior knowledge as a key element for further improving fairness modeling; and (4) to demonstrate effectiveness on real-world applications including healthcare informatics and disaster resilience. The educational objectives are: (1) to incorporate responsible artificial intelligence (RAI) into curriculum design via integrating research findings and case studies into current and new courses; (2) to enhance public interest in and awareness of RAI by organizing data challenges and broadcasting information on social media platforms; and (3) to attract and retain women and underrepresented minorities to ensure a diverse future STEM workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

While deep learning has achieved unprecedented prediction capabilities, it is often criticized as a black box because of lacking interpretability, which is very important in real-world applications such as healthcare and cybersecurity. For example, healthcare professionals would appropriately trust and effectively manage prediction results only if they can understand why and how a patient is diagnosed with prediabetes. The project is to investigate the interpretability of deep learning by following the fundamental elements in a data mining practice from representation, modeling to prediction. The results of the project are expected to improve the usability of deep learning in important applications, positively boosting the overall value of the deep learning based information systems. The education program that integrates data science, industrial engineering, and visualization is to train students with data analytics technologies in industrial systems, to attract and mentor members of underrepresented groups pursuing careers in STEM. The research goal of this project is to systematically explore interpretability of deep learning from a machine learning life cycle, i.e., representation, modeling and prediction, as well as the deployment of interpretability in various tasks. Specifically, this project aims to achieve the research goal by developing a series of interpretation algorithms and methods from the following aspects. It explores post-hoc interpretation methods to shed light on how deep learning models produce a specific prediction and generate a representation. It also investigates designing interpretable models from scratch, which aims to construct self-explanatory models and incorporate interpretability directly into the structure of a deep learning model. The aforementioned interpretation derived from a deep learning model is employed to promote the model performance. In addition, the applications of interpretability are utilized to debug model behaviors so as to ensure the model decision making process is consistent with human expert knowledge, as well as to promote model robustness when handling adversarial attacks. 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 · 2024-10

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is the development of an innovative, low cost, healthy and sustainable edible coating technology that can extend the shelf life of fruits and vegetables to combat the challenge of food preservation and waste management effectively and simultaneously. Among all foods, fruits and vegetables are the ones with the highest categories of losses, with up to 50-60% of spoiled produce discarded in landfills. The proposed innovation will provide a desirable and healthy protein-fortified coating for food preservation. Moreover, the coating is washable, edible, and glossy providing an all-encompassing yet desirable food preservation solution that benefits consumers, sellers, and farmers. The proposed coating technology will enhance the window for the markets of edible coatings that focuses on the growing health- and sustainability-focused retail stores and health-conscious consumers. Other than reducing food waste and economic losses, the anticipated technology could have significant environmental impacts. For example, reducing food waste can save 25% of the freshwater supply, 3.9 billion tons of fertilizer, 300 million barrels of oil, and 135 million tons of greenhouse gases every year in the USA alone. This project will also benefit the education and training of students related to research translation. The proposed project is based on the development of an eco-friendly and biodegradable protein-based nanocomposite coating that can be applied to the surface of perishable fruits and vegetables of any shape. On the lab scale, egg and soy-protein composite coatings have significantly extended the shelf life and cosmetic appearance of various fruits. However, testing on a pilot scale will provide a detailed understanding of performance and cost matrices. To fully understand the technological viability of commercializing the proposed coating, the optimum combinations of various proteins with cellulose nanomaterials, chemical modifications of proteins via selected plasticizers and crosslinkers, and easily scalable mechanisms for controlled coating methods will be explored. The coating will extend the shelf life by reducing maturation, senescence, dehydration, and microbial growth rate. The deliverables of this project will be a minimum viable product via pilot-scale studies. Overall, with an end goal of improving the environmental performance and sustainability of the food industry, the outcomes of the research will lead to the development of a protein-based inexpensive, and green coating that will enhance the shelf-life of fruits with the added benefits of biodegradability, washability, edibility, and cosmetic appearance and provide a business feasibility analysis for the potential commercialization of the product. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

People with HIV/AIDS (PWH) are twice as likely to smoke cigarettes compared with the general population. Further, PWH are less likely to quit smoking, likely due to barriers including co-occurring behavioral risk factors, stigma, limited resources, and diminished access to health care. Importantly, cigarette smoking is a significant risk factor for both HIV-related and non-HIV-related morbidity and mortality among PWH. Health disparities observed among smokers with HIV are compounded by the tendency for PWH to have lower incomes, lower education attainment, and lower access to healthcare. Suboptimal rates of smoking cessation and HIV disease management among Black PWH who smoke appear to be related to increased exposure to interoceptive-stress symptoms (e.g., anxiety, bodily sensations, stress-related burden due to being treated unfairly or HIV diagnosis). Consequently, smoking cessation interventions and HIV disease management directed toward Black PWH who smoke might benefit from a specific focus on decreasing emotional reactivity to interoceptive stress. Anxiety sensitivity (AS) is a candidate mechanism pertaining to the expectancy that interoceptive sensations are personally dangerous, which escalates emotional reactivity. Yet, only one study has leveraged the potential of AS to better understand smoking, HIV disease management outcomes, and interoceptive stress relations among Black PWH who smoke. Our group, along with collaborating colleagues, has developed smoking cessation interventions for PWH that engage AS to increase smoking cessation success. Early work included in person smoking cessation interventions. Our more recent efforts have focused on developing and testing an integrated mHealth intervention for smoking cessation, AS reduction, and HIV disease management improvement for Black PWH who smoke (MASP+). MASP+ targets multiple health conditions that interfere with successful aging: smoking, mental health, and HIV disease management. To date, MASP+ has only been available to patients receiving HIV care within a single, urban community clinic. This proposal aims to test MASP+ in a national sample of participants with treated and untreated HIV. First, we propose to review already developed MASP+ materials with 30 members from our priority population to ensure their appropriateness and therapeutic fit. Next, we will recruit and enroll 300 Black PWH who smoke to participate in a randomized controlled trial (RCT). Participants will be randomly assigned to: (1) MASP+; (2) the National Cancer Institute (NCI) QuitGuide smartphone app for standard mobile smoking cessation treatment; or (3) an assessment only control. Participants will complete a baseline assessment, daily ecological momentary assessments, and follow-up assessments at weeks 1, 2 (quit date for MASP+ and QuitGuide), 3, 4, 5, 6 (week 6 includes a qualitative interview for a subset of participants), 28, and 54 via our InsightTM app. All participants will have the option to receive nicotine replacement therapy. If the efficacy of MASP+ is established, it would serve as a low-burden and highly accessible treatment option for smoking cessation, improved mental health, and improved HIV care adherence/engagement, which all serve to support successful aging and improved well-being.

NSF Awards · FY 2024 · 2024-09

Despite ongoing efforts to broaden participation in engineering in the United States, Black men remain significantly underrepresented, with only 2.8% of engineering bachelor's degrees awarded to them in 2020. These statistics indicate that there is a disconnect between cultural, institutional, or academic factors in engineering education settings and the expectations and experiences of Black men resulting in this lack of representation. Moreover, within both engineering education and professional engineering work contexts, complex projects are formulated and executed by teams. Given the critical role of teamwork in engineering in both industrial and academic settings, understanding the social interactions between Black men and their peers within these teams is vital. Consequently, this project will investigate the experience of Black men in undergraduate engineering student teams. The project aims to produce results that will be used broadly to support Black men’s sense of belonging and enhance their academic and professional success in engineering. To address these issues, this project focuses on two research questions: 1) What are the experiences of Black men on student project teams? and, 2) How do Black men perceive their participation in decision-making processes within these teams? This project will expand the research available to instructors, researchers, decision makers, and policy makers to support Black men in engineering from an asset-based perspective. To achieve the goals of this project, this mixed-methods qualitative study will use Interpretative Phenomenological Analysis (IPA) and Photovoice methods. These phenomenological and participatory methods enable the prioritization of the voice of Black male engineering students in constructing study findings and co-constructing future scholarly work with student-driven strategies for increasing a sense of belonging and academic success. This project will address three key gaps in the current literature. First, in the past 5 years only one research study has explored the experiences of Black men on student project teams. Second, there is a lack of research on how Black men participate in decision-making processes on student led teams. This is critical because researchers have suggested there is a strong connection between identity production processes and the construction of engineering judgments among team members. By cross-fertilizing these literatures, the research team will investigate the ways that Black male experiences illustrate how identity processes directly impact engineering work practices among undergraduates. Third, this study will adopt an assets-based approach, focusing on the positive aspects of Black men's experiences in engineering rather than individual deficiencies. The participatory aspect of the photovoice methods will facilitate the development of student-driven strategies that have the potential to foster positive cultural change at the institutional level. The research may result in tangible recommendations for supporting and retaining Black men in engineering fields nationwide. To broadly share the student-driven strategies co-created with study participants, the project will include co-creation of a photovoice exhibit to share participants’ strategies, resources, and experiences. Disseminating project findings through this photovoice exhibit will make the research accessible to a wider audience, including community stakeholders, students from other institutions and disciplines, university researchers, administrators, and the general public. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Cigarette smoking is the leading cause of preventable death and disability in the United States. Smoking cessation rates have stagnated in recent years, underscoring the need for innovative, accessible methods to support cessation. Emerging neurobiological and behavioral research indicate that the intentional sniffing of olfactory stimuli (OS) can fundamentally alter emotional states and reduce cigarette craving. Yet, no work has evaluated the potential for OS as an adjunctive intervention to facilitate successful smoking cessation. The proposed project aims to refine and test a novel biobehavioral smoking cessation intervention that integrates the strategic application of OS to reduce cigarette craving with an established smartphone-based smoking cessation application. Our previously published work indicated that OS can relieve cigarette craving, and our recent pilot data showed that these effects may hold in the natural environment to help smokers resist smoking. This work provides the rationale for conducting the proposed clinical trial. Still, research is needed to determine the precise methodology for OS administration in the context of a smoking cessation attempt and to preliminarily evaluate the potential of OS when integrated with an established phone-based smoking cessation app before conducting a full-scale R01 smoking cessation trial. Building on our prior work, the current proposal aims to (1) refine the design and methodology for our novel, first-generation olfactory stimulation delivery system (OSDS; Phase I), and (2) determine the adjunctive benefit of the OSDS when integrated with a smartphone-based smoking cessation application (Smart-T; Phase II). This trial integrates theory-driven basic research derived from three disciplines independently related to smoking, but not jointly examined in the context of applied smoking research (olfaction, emotion, and cognition). Phase I will consist of a crossover, micro-randomized controlled trial in which treatment-seeking smokers (N=32) will receive the nicotine patch and engage in a self-guided quit attempt for 14 days. During the 14-day trial, participants will use our OSDS daily and complete daily ecological momentary assessments (EMAs). Phase I participants will complete a qualitative interview and quantitative survey at the end of the trial. Phase II will consist of a randomized controlled trial, in which participants (N = 100) will be randomized to our smartphone app for smoking cessation (Smart-T) with nicotine patch or the Smart-T app with nicotine patch and OSDS as an adjunctive feature (Smart-T+O). Participants will complete a baseline assessment, daily EMAs for 13 weeks (1-week pre-quit and 12-week post-quit), and a follow-up assessment at 12 weeks post-quit via the app. The proposed project will provide vital data to refine a protocol for assessing a phone-based cigarette smoking intervention that integrates an OS approach to craving relief. Accordingly, it will provide the data needed to successfully conduct a full-scale clinical trial evaluating this novel approach to smoking cessation. Irrespective of the outcome, the proposed research will advance knowledge regarding the integration of OS-based craving relief with a mobile health approach to smoking cessation.

NIH Research Projects · FY 2025 · 2024-09

1 ABSTRACT 2 Obesity and related chronic diseases that result from years of poor diet and physical activity (PA) may be 3 prevented by wellness programs for toddlers (18–36 months) and their parents. It is difficult to engage families 4 in such programs, but we successfully engaged a diverse (>70% African American or Hispanic) sample of parent- 5 toddler dyads in our FUNPALs Playgroup pilot study. Typical toddler obesity prevention programs instruct 6 parents to improve the nutritional quality of meals and snacks and to find ways to increase toddler physical 7 activity (e.g., WIC, SNAP Ed). However, attempts to improve toddler health behaviors are less effective if not 8 delivered within a warm and nurturing parent-child relationship, and parents are often uncertain how to feed their 9 children a healthy diet and encourage physical activity when toddlers resist (e.g., food refusal, tantrums, whining, 10 etc.). To address this need, our team, using the Intervention Mapping Framework, created the FUNPALs 11 Playgroup, which includes educational, but fun interactive programming to help parents from mostly African 12 American and Hispanic backgrounds reduce obesity risk among toddlers by addressing general parenting skills 13 in conjunction with traditional nutrition and physical activity content. Playgroups—parent-child groups that meet 14 regularly for social, physical, and educational play—present an opportunity to engage families in obesity 15 prevention efforts while offering positive parenting instruction. Our pilot study (n=50) showed toddlers in the 16 FUNPALs Playgroup, particularly those with overweight/obesity (OW/Ob; 52%), experienced desirable changes 17 in weight, diet, sleep, and parenting. The FUNPALs Playgroup was feasible and as effective or more effective 18 (e.g., reducing sugar sweetened beverages) than the standard health education control group on most 19 outcomes. Further, the FUNPALs Playgroup was superior in terms of acceptability and engagement. This study 20 will test the efficacy of the FUNPALs Playgroup to reduce obesity risk among toddlers from ethnically diverse 21 backgrounds. The central hypothesis is that the FUNPALs Playgroup will reduce obesity risk among a diverse 22 sample of toddlers more than no treatment or standard parent health education. In this multi-methods 3-arm, 23 randomized controlled trial (RCT), 340 toddlers (18-36 months) and parents from an underserved community 24 will be randomly assigned to receive: 1) 10-weekly sessions of the FUNPALs Playgroup, 2) 10-weekly sessions 25 of a standard parent health education class, or 3) no treatment. Weight status, diet, and activity will be assessed 26 at baseline (T1), 12-14 weeks after baseline (immediate post, T2), and 24-weeks after baseline (follow up, T3). 27 The RE-AIM framework will be used to determine key elements for maximizing external validity and 28 implementation. 29

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY Black men experience extremely high levels of stress from unfavorable social and economic circumstances resulting from structural racism, institutional discrimination, and unfair treatment. The accumulation and manifestation of stressors can vary by the spatial contexts in which Black men live, play, work, and worship. Residential areas are complex constructs that include physical and social attributes that influence health and a small but growing number of researchers have examined the association between place and cognitive health. Residential segregation can have implications for the health and well-being of Black men because place can influence interactions between social environment, cumulative stress, and biological responses. Few studies have examined Black men and cognitive impairment and no studies to our knowledge consider place, stress, and their implications for cognitive status among this population. Our proposed study addresses this gap by leveraging resources from the Johns Hopkins Alzheimer’s Disease Resource Center for Minority Aging and by building on collaborations with its leaders to advance an emerging program of research founded on the NIA Health Disparities Framework, Seeman and Crimmins biopsychosocial framework, and Diez Roux and Mair’s neighborhood and health heuristic model. The objective of our study is to introduce and evaluate a theory- driven conceptual model specifying how place has implications for the association between stress and cognitive impairment among middle age and older Black men using data from the Health and Retirement Study and U. S. Census. Knowledge gained from this small study can provide evidence for observational cohort studies investigating the synergistic effects of individual and geographic factors on cognitive function, decline, impairment, and premature mortality among Black men. Results from this work can inform future, risk reduction interventions designed to preserve cognitive function, prevent cognitive impairment, and extend longevity among middle age and older Black men.

NSF Awards · FY 2024 · 2024-09

Minerals that are insoluble in water tend to deposit on the interior walls of water pipelines, forming mineral scales that are extremely difficult to remove. Consequently, mineral scale presents a costly challenge for industrial processes such as membrane desalination, heating and cooling systems, natural resource production, and papermaking. The most common mineral scale is calcium carbonate, found in eggshells, chalk, and the skeletons of shellfish. Current methods to remove this and other water-insoluble scales involve harsh acids or other environmentally unfriendly chemicals. Thus, there is a need for more benign and effective treatments. However, developing such treatments requires understanding the chemical and physical mechanisms by which these treatments dissolve scale. To address this knowledge gap, this research project will test whether a class of organic molecules called citrates, often derived from lemon juice, combined with inorganic salts can serve as environmentally friendly treatments to remove calcite, the most stable and problematic form of calcium carbonate. This research builds on the team’s history of collaboration and expertise in identifying the mechanisms by which sustainable biopolymers dissolve other types of mineral scales. The expected outcomes of this project include the development of design rules for environmentally friendly treatments for calcium carbonate and other insoluble mineral scales, ultimately leading to reduced costs for scale treatment across multiple industries. The project team will disseminate these results at national meetings and conferences in Houston, where scale control poses a significant problem for local industries. Additionally, they will develop new activities on scale dissolution to complement their ongoing outreach efforts for K-12 students and their parents through the University of Houston STEM Zone Saturday event and the public at the annual Houston Energy Festival. This project aims to elucidate the mechanisms that control modifier-driven dissolution of mineral scale under quiescent and flow conditions. As a model system, the team will study the dissolution of calcite, the thermodynamically stable form of the ubiquitous calcium carbonate mineral scale. The research plan integrates the team members’ complementary expertise in microfluidics, transport phenomena, crystal engineering, and molecular simulation to test the overarching hypothesis that careful selection of modifier chemistry for scale control can drive calcite dissolution. The plan includes three specific aims: (1) determine the mechanisms by which organic and inorganic modifiers dissolve calcite; (2) examine the effects of flow on modifier-calcite interactions and scale dissolution; and (3) identify cooperative effects between modifiers on dissolution. Optical and atomic force microscopy experiments will be used to quantify bulk and mesoscopic dissolution of calcite in the presence of two classes of modifiers, citrate derivatives and salts, and in quiescent and flow conditions relevant for practical applications. Complementary molecular simulations will characterize the modifier-calcite interactions and their effects on the dissolution mechanisms and kinetics. Finally, these methods will be applied to examine how interactions between the organic (citrate-based) and inorganic (alkali and alkaline earth metals) modifiers enhance or hinder calcite dissolution. Together, these studies will develop a fundamental understanding of the chemical factors that control calcite dissolution. This project will thus pave the way for new environmentally friendly chemistries to dissolve this problematic scale and establish general principles for identifying and designing chemical agents to dissolve mineral scales. Further, these results may provide insight into the mechanisms controlling the formation of calcium carbonate in the environmental carbon cycle, which is relevant for biomineralization, regulation of sea alkalinity, and emerging CO2 sequestration technologies, as well as in pathological diseases such as kidney stones and atherosclerosis. Thus, the new mechanistic understanding of calcite crystal growth and dissolution may have broader relevance for natural, biological, and synthetic calcification pathways. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

By the time they enter middle school, many youth have opted out of engineering pathways due to limited exposure to engineering fields and a lack of encouragement or support. This project will address this issue by developing and testing a promising model of informal engineering programming designed to foster engineering identities among youth aged 9-12. This model will iterate on the existing work of St. Elmo Brady STEM Academy, a longstanding program which has provided engineering experiences to youth from Houston. This project will offer a no-cost, 16-week after-school informal engineering program--which includes features such as hands-on engineering design projects, opportunities to interact with professional engineers and scientists, family engagement Saturday sessions, community partnerships, engineering fairs, and support from mentors who are first-generation college undergraduates in engineering--to hundreds of youth in Houston. Research will explore how aspects of this informal engineering environment supported the engineering identity development of the participating youth. The model of informal engineering programming, and associated pedagogical materials, will be shared widely via professional networks of informal educators and engineering educators. Ultimately, this project is likely to promote engineering pathways and careers by illustrating how familial support, social supports such as mentors and community involvement, and engineering experiences can encourage all youth to see themselves in engineering. The University of Houston and Drexel University will use mixed-method research to investigate whether and how a model of informal engineering programming fosters engineering identity among participating youth. To achieve this purpose, the research team will analyze the following types of data: video-recordings of youth as they participate in the 16-week program; transcriptions of focus groups with the youth; and a pre-and post-administration of the Engineering Identity Development Scale. A subset of youth participants will engage in Photovoice in which they photograph, caption, and discuss elements of their experiences related to the program. This data source, in addition to other youth artifacts and transcripts from focus groups, will be used to ascertain whether and how particular programmatic elements foster the development of engineering practices and habits of mind among participating youth. Finally, the research team will conduct focus groups with undergraduate mentors and family members. They will analyze transcripts from these focus groups, as well as observational data, to investigate how mentors and families implement practices that foster youths' engineering identities, and how engineering programs can be designed to better support family engagement. The empirical findings from these analyses will be disseminated widely via professional networks and publications. This project will result in a field-tested, empirically-based model of informal engineering programming that fosters engineering identity and encourages all youth to consider and pursue engineering pathways leading to engineering careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT There is evidence of a growing health disparity in mental health—particularly rising depression and anxiety—in Latinx young adults. Fear of deportation is prevalent in this population, with nearly 39% of Latinxs expressing concern about the possibility of themselves or someone they know being deported. Fear of deportation has been linked to high levels of anxiety and depression among Latinx populations, yet little is known about the underlying mechanisms driving this relation. While there is a growing number of studies investigating systemic inflammation among Latinx populations, there remains a knowledge gap related to the associations between inflammation and structural racism. The proposed project aims to expand health disparity and discrimination research among Latinx immigrant young adults by examining how systemic inflammation may mediate the relation between fear of deportation and depression and anxiety. Specifically, the study will recruit 150 Latinx college students with temporary or no legal status and 150 Latinx students with permanent legal status to complete online questionnaires and provide a saliva sample. The first aim of the proposed project is to assess the multi-domain effects of fear of deportation on systemic inflammation (biological domain) and mental health outcomes (behavioral domain). It is hypothesized that systemic inflammation will mediate the relations of fear of deportation with anxiety and depression. The second aim of the proposed project is to identify multi-level risk factors that moderate the associations between fear of deportation, systemic inflammation, and the mental health outcomes of anxiety and depression. The study hypothesizes that acculturation (individual level), discrimination (community level), and skin color (individual level) will act as moderators of links between fear of deportation, inflammation, and mental health. Additionally, the third aim of the proposed project is to identify multi-level protective factors that may weaken these links. It is hypothesized that political activism (individual level), parent-child attachment (interpersonal level), and religiosity (community level) will mitigate the associations between fear of deportation, systemic inflammation, and depression and anxiety. The proposal innovates by considering, for the first time, risk and protective factors across domains and levels of influence that are operating at the biological level to exacerbate and mitigate the effects of deportation stress on mental health. Additionally, it aims to shed light on the influence of structural racism on health through biological processes. The findings of this study can inform the development of targeted interventions and policies aimed at reducing a known health disparity and promoting resilience among minoritized populations. As such, this study is aligned with NIMHD's strategic goal of investigating the health determinants that underlie resilience or susceptibility to diseases and conditions experienced by minority populations. It will also support the training of a first-generation, Latina immigrant who seeks to address health disparities through doctoral study and an independent research career in psychology.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/ Abstract Human induced pluripotent stem cells (hiPSCs) are among the top candidates for cell therapy due to their pluripotency and isogenic source. In case of skeletal muscle disorders, stem cell-based therapies can replace defective or damaged muscle tissue with healthy muscle stem cells and progenitors. Therefore, hiPSCs have been the focus of recent research for derivation of skeletal muscle progenitor cells (MPCs). However, cell therapies are generally limited by poor cell survival due to lack of oxygen, nutrients and local trophic factors. Meanwhile, endothelial cells (ECs) have a key role in regulation of muscle stem cells through secretion of trophic factors controlling their activation, function and maturation. In addition, ECs directly contribute in angiogenesis and formation of new vessels, improving local circulation. Indeed, coupled activation of MPCs and ECs supports optimal muscle regeneration and plasticity after injuries or increased physiological demand. Therefore, ECs can be considered as a potential adjunctive cell therapy to improve MPC survival and engraftment outcome in vivo. Although the interaction between ECs and MPCs have been studied using primary cell lines and mouse models, their possible cross-talk, underlying molecular mechanisms and their combined in vivo engraftment efficiency has not been evaluated in a hiPSC-derived model system. Therefore, current application aims to: A) study the effect of hiPSC-ECs on in vitro activity and function of their myogenic counterparts (hiPSC-MPCs) and to identify and validate underlying mechanisms, and B) to evaluate the therapeutic efficiency of a combined hiPSC-EC+ MPC therapy in dystrophic or injury mouse models. In Aim1, hiPSC-ECs and MPCs will be grown using a transwell co-culture system to allow paracrine interaction of the cells. The paracrine effect of ECs on MPCs will be evaluated on cell proliferation, migration and differentiation using gene expression, transwell migration assay and immunostaining methods. In addition, time-course RNA-Seq and secretome proteomics will be performed to identify differentially expressed genes and proteins, such as ligands and receptor pairs, growth factors and pathways. Top candidates will be validated by proteomics and over-expression/inhibition studies to validate their role in the predicted cellular function. In Aim2, hiPSC-ECs and MPCs will be injected into muscle of dystrophic or injury mouse models using different cell ratios and their in vivo survival and engraftment will be evaluated by live cell bioluminescence, as well as histologic evaluation for donor cell engraftment into myofibers, muscle stem cell and vessel compartment. Data will be quantified for engraftment and vascularization among different experimental conditions to determine the efficiency and appropriate cell ratio of combined hiPSC-EC + MPC therapy in the studied models. Completion of these studies will elucidate the role and underlying mechanisms of hiPSC-EC/MPC interaction, as well as defining their combined in vivo efficiency to improve donor cell survival, vascularization and engraftment in muscle disorders as a proof of principle study. Outcome of this study will likely be able to move the field toward generation of multi-cellular hiPSC models for in vitro and in vivo studies.

NSF Awards · FY 2024 · 2024-09

Water temperatures in the polar oceans can drop below the freezing point of animal tissues, resulting in the formation of ice crystals that can cause potentially fatal tissue damage. To survive in the frozen polar oceans, some fish species produce antifreeze proteins, which suppress the formation of ice crystals. These proteins must be maintained at high concentrations in the blood to be effective, but they are small enough to be filtered by the kidney and lost in urine. Their active re-absorption would be an energetically costly process. Antifreeze producing fishes have adapted to either seasonally or permanently disrupt the primary unit of filtration in the kidney, the glomerulus. Understanding how the vertebrate kidney responds to the need to maintain antifreeze proteins will address questions central to polar, evolutionary, biomedical, and developmental biology. This study aims to employ genomic techniques to discover the mechanisms that regulate seasonal changes to kidney morphology and function in several species of high latitude cod, a commercially and ecologically important group of polar fishes. Results will advance our fundamental understanding of how fishes have adapted to seasonally variable polar environments. Broader impacts of the project seek to enhance understanding of polar adaptation across multiple academic levels by improving undergraduate and K-12 STEM education and increase the participation of underrepresented populations in STEM. The study system will focus on four ecologically important species of cod; the polar cod, Boreogadus saida, pacific cod, Gadus macrocephalus, walleye pollock, Gadus chalcogrammus, and saffron cod, Eleginus gracilis. Observation of seasonal changes to kidney morphology will be accomplished through histological inspection of kidney tissues from cods kept at 0°C (winter conditions) and 8°C (summer condition) at a NOAA cold water culture facility in Newport, Oregon. Seasonal cell-type specific changes in response to antifreeze protein production will be determined through gene expression patterns in the kidney of Eleginus using single-nucleus RNA sequencing. Reconstruction of the changes to cis-regulatory regions that enabled the evolution of seasonal polyphenism in Eleginus kidney will be accomplished using a combination of ATAC-seq and comparative evolutionary genetics. Candidate regulatory networks identified by the analyses will be modeled in zebrafish (Danio rerio). Bioinformatic pipelines, histological imagery, and molecular sequence data will be publicly posted and shared with the polar research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Time-series data arises frequently in medical applications such as sensor monitoring over time. While deep neural networks have been extensively employed to analyze such medical time-series data, current networks often rely on spurious features that misalign with medical expertise. The spurious correlations between time-series data features and labels are biased in observed data, which undermines the robustness of the deep network to generalize to new patients in complex and dynamic environments. The project aims to address the need for reliable deep learning in ubiquitous medical-sensor applications with the goal of technological improvement in healthcare quality. The educational plan will foster broader participation among undergraduate and graduate students - particularly within the Hispanic community in South Texas - through dedicated research, education, and outreach initiatives. The primary goal of this project is to facilitate robustness of time-series deep-learning models via systematically defining and mitigating spurious correlations between input confounders and output decisions. Specifically, this project aims to achieve the research goal by developing robust techniques via following: 1) identifying input confounders prevalent at various levels of time-series data - including point, segment, and structure levels - to understand their spurious correlations with target labels; 2) designing knowledge-editing mitigation strategies to locate neuron groups responsible for spurious correlations so as to efficiently correct them; and 3) investigating the approach in two medical-sensor applications: monitoring for Parkinson's disease and detection of falls in the elderly. The research outcome will yield open-source tools and potentially benefit a wide range of sensor-based medical-monitoring and diagnosis tasks. 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 The proposed research study will test the efficacy of a telehealth version of the Brief Alcohol Screening and Intervention for College Students (BASICS), which is the gold standard prevention and intervention approach to target heavy alcohol use on college campuses across the United States. BASICS in an NIAAA Tier 1 intervention which involves assessment of drinking behavior followed by a single in-person session in which personalized feedback is presented by a trained facilitator in a motivational interviewing (MI) style utilizing harm reduction principles to reduce risks and alcohol-related consequences. Alternative strategies requiring less time, effort, and resources, with no face-to-face interaction with a facilitator (e.g., web-based personalized feedback), have proven less effective. The in-person delivery format of BASICS has presented barriers to wider implementation due to the time, effort, and costs of traveling to and from sessions, the need for private meeting space, and the firmly fixed scheduling of intervention sessions. In the proposed study, we will evaluate the efficacy of a tele-BASICS approach utilizing the ZOOM application compared to in-person BASICS and a lower threshold treatment as usual intervention. Three hundred mandated and 300 volunteer students who report hazardous drinking will be recruited from two large universities and randomly assigned to a condition (in- person BASICS, Tele-BASICS, or treatment as usual). Follow-up assessments will occur 1-, 3-, 6-, and 12- months post-baseline. The significance of this research lies in the potential to maximize access to the highest standard of care by establishing support for easier access without sacrificing any central features of the traditional BASICS intervention. In addition, many universities pragmatically adapted existing in-person interventions to remote-telehealth approaches in response to the COVID pandemic but now have no scientific basis for determining whether transitioning back to in-person approaches would be beneficial. In addition to demonstrating non-inferiority to traditional BASICS, we expect Tele-BASICS to significantly outperform treatment as usual. Attention and working therapeutic alliance are expected to mediate intervention efficacy. We expect Tele-BASICS to have stronger effects than in-person BASICS among women, heavier drinkers, students without co-occurring substance use, and those with greater motivation. We expect higher Tele- BASICS participants recruitment and completion rates and lower costs relative to in-person BASICS. This research brings together a team of experienced investigators with a collaborative history and supports NIAAA's strategic plan to improve strategies to prevent and reduce harmful alcohol consumption in a high-risk population. The establishment of Tele-BASICS as an efficacious alternative to in-person BASICS would allow more schools to adopt BASICS as their standard of care – and potentially engage more students in empirically- supported treatment by decreasing barriers to care.

NSF Awards · FY 2024 · 2024-08

The formation of hierarchies in task groups is a ubiquitous process, but these hierarchies are not always stable. Group hierarchies can evolve with the contributions of members. If a comparatively low status group member makes a positive contribution to the group, for example, they are likely to increase their standing in the overall group. Conversely, if a relatively high status group member makes a poor contribution, they are likely to decrease their standing. This research systematically varies moderator interventions to study their effects on the contributions of various group members to evaluate whether group moderators can enhance or flatten the emergence of hierarchies in task groups and what the effects of this are for task success. Drawing on existing research on status dynamics, moderators intervene in task discussions to enhance the standing of comparatively low status group members.   To study these processes, the research uses experimental and computational methods. First, moderator interventions are experimentally varied. In a control condition, moderators do not intervene in the emergence of the group’s status hierarchy. In two experimental conditions, moderators use cues and interventions from the extant literature on status dynamics to enhance the perceived standing of comparatively low status group members. Influence over the group is measured and used to evaluate whether moderator interventions are successful in mitigating task irrelevant characteristics from shaping the power and prestige order of the group. Also examined is what this means for task success. The research further develops a computational pipeline that uses Natural Language Processing to track the group’s status hierarchy. The group’s conversation for status-relevant information is coded and machine learning algorithms “learn” the codes that humans assign to the text. This enables automatically coding of subsequent conversations for status-relevant information. Collectively, the work evaluates new moderator interventions to make task groups more egalitarian, and develops computational tools to automate the coding of hierarchy formation in task groups. 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

Shape analysis has now become an integral component of data science as it is key to modelling and analyzing quantitatively the geometric variability within datasets for applications as diverse as computer vision, speech/motion recognition, morphogenesis or computational anatomy. Among the variety of geometric structures that are studied in this field, curves, surfaces and more generally manifolds are both very natural objects but also particularly challenging to process and analyze due to the non-canonical structure of the corresponding shape spaces. This has in part hindered the development and effectiveness of shape analysis frameworks for such data, if compared for instance to the more widely studied case of images. This project attempts to bridge a few of these important gaps, both on the theoretical and computational side and develop new scalable algorithms for morphological analysis adapted to the growing size and complexity of real datasets. The project will also promote those research topics among students at various levels of the educational system, with the creation of an upper-level undergraduate course on differential and computational geometry, training of PhD students and K-12 outreach activities through the Women in Science and Engineering (WISE) program in particular. Building up on several prior works on shape spaces and metrics, the specific research objectives of this project are (1) to advance the analysis and comparison of relaxed shape matching problems deriving from Riemannian metrics on spaces of manifolds; (2) to investigate supervised and unsupervised deep learning approaches to improve the efficiency of manifold registration algorithms; and (3) to study novel extensions of those models to account for partial or incomplete data and model joint shape/topological variations across shapes. As part of this project's outcome, Python pipelines will be developed and made openly accessible to the scientific community with the long term goal of expanding the potential scope of applications of those 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.