Michigan Technological University

NSF Awards · FY 2025 · 2025-06

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry, Professors Shiyue Fang and Yinan Yuan of Michigan Technological University are studying the development of new chemical technologies for the synthesis of long oligonucleotides (ONs), including oligodeoxyribonucleotides (ODNs) and oligoribonucleotides (ORNs). ODNs and ORNs are segments of DNA and RNA molecules, respectively. Long ONs are essential in a wide range of fields, including synthetic biology, protein engineering, gene editing, mRNA therapeutics, gene therapy, nucleic acid vaccines, and computational digital data storage. These fields are closely related to numerous sectors of our society, encompassing energy, environment, agriculture, medicine, and national security. The successful development of the proposed long ON synthesis technologies will enhance the feasibility and cost-effectiveness of projects in these fields. This multidisciplinary research requires a wide range of technical skills such as organic synthesis, surface chemistry, automated DNA and RNA synthesis, NMR, MS, capillary electrophoresis, HPLC, cloning, and DNA and RNA sequencing. During the funding period, Ph.D. students and undergraduate researchers will have the opportunity to acquire these skills through their involvement in the project. The project aims to develop technologies for the de novo synthesis of long ONs. Currently, the longest chemically synthesized ODNs and ORNs are limited to approximately 200-mers and 120-mers, respectively. Longer ONs must be produced using biological methods such as PCR assembly, ligation, and in vitro transcription. These biological methods, however, suffer from drawbacks including long turnaround times, high labor demands, and error susceptibility. More seriously, they are unable to synthesize ONs containing difficult elements such as long repeats, unusually stable higher-order structures, high or low GC content, and site-specific modifications. Recently, the Michigan Tech team achieved direct de novo synthesis of 400-mer ODNs. Building upon this foundation, they will extend the boundaries of chemical ON synthesis to beyond 2,000-mer ODNs and 200-mer ORNs. To achieve these objectives, they will explore novel solid supports for ODN and ORN syntheses and the use of the powerful catching-by-polymerization (CBP) method for the isolation of the low-percentage, yet sufficient quantities, of ONs. The ONs will be rigorously characterized involving techniques such as HPLC, MS, gel electrophoresis, enzymatic digestion, PCR, cloning, and DNA and RNA sequencing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-05

Project Summary The instrument requested is an Asylum Research MFP-3D-Bio Atomic Force Microscope (AFM) from Oxford Instruments mounted on a new Olympus IX73 inverted fluorescent microscope into one integrated system to be located at Michigan Tech. An AFM is extremely versatile equipment that can measure nanoscale topography, force between different tip chemistry and a sample, and mechanical properties with nanoindentation. When combined with a microscope, cells and other micron-size objects can be visualized and precise locations on cells can be probed with AFM and simultaneously capture either brightfield or fluorescence microscopic images, thus the integrated system is called a Bio-AFM. Michigan Tech has the expertise and facilities for training, data analysis, and complex analysis using the AFM from researchers and technical staff. The Bio-AFM will provide three main advantages that are not available in the AFM equipment currently at Michigan Tech: with the addition of a microscope and heated stage (i.e. petri dish heater with environmental control), cells can now be used in conjunction with the measurement capabilities of the AFM; the BSL-2 cleaning protocols that will be implemented around the Bio-AFM and the isolation in one room will allow a larger range of biomolecules to be studied; and the z-extender will allow for pulling experiments of proteins and polymers for expanded experimental capability. These additional accessories are what propel this instrument to be of extreme use to NIH funded researchers on campus. The researchers will explore how cells move and cancer metastasizes on different biomaterials, how metabolism of different sugars and metabolites can affect cell morphology and elasticity, and how viruses change structure and have different chemical properties during inactivation and manufacturing. The large, proposed user base demonstrates the excitement this instrument has garnered at Michigan Tech and another regional university. We want Michigan Tech to become a regional hub for instrumentation, facilities, and expertise in biomedical research. By bringing together these things in the rural upper mid-west, we can leverage our unique location, diverse portfolio of research, and our diverse student body, many who come from lower socio-economic backgrounds, to provide a vibrant location for biomedical training. This Bio-AFM at Michigan Tech will help NIH to increase the diversity of trainees, as well as diversity in thought in ways to apply the instrument to biomedical research.

NSF Awards · FY 2025 · 2025-05

This project aims to develop a wearable device capable of continuously sensing a wide range of physiological signals for use in biometric authentication and health monitoring. As the society sees increasing use of digitally interconnected devices and systems, the need for secure and user-friendly authentication methods is more critical than ever. Existing methods, such as passwords, fingerprints, and facial recognition, are incompatible with wearable technologies. This project investigates a new approach using physiological signals to enable a continuous time-efficient authentication method that will facilitate a secure and seamless user experience. The broader impacts of this work are substantial: it will advance cybersecurity, protect user privacy, and support smart healthcare by greatly improving the reliability and accessibility of wearable technologies. Moreover, this project includes a plan of extensive education and workforce development activities. The investigators will create advanced interdisciplinary learning opportunities at both Michigan Technological University (MTU) and Louisiana State University (LSU) to engage undergraduate and graduate students in cutting-edge research across electrical engineering, biomedical engineering, signal processing, machine learning, intelligent systems, and data science. By training students with the interdisciplinary technical skills for high-tech industries, this project will contribute to the development of the nation's future STEM workforce. The research of this project will explore the use of multispectral photoplethysmography (PPG) signals for biometric authentication through custom-designed wearable devices. Unlike prevalent biometrics such as fingerprints and facial images which are not suitable for continuous data acquisition, PPG signals can be collected in real time through tiny skin-contact sensors. This feature enables the development of smart biometric systems for continuous and unobtrusive operations, making them well-suited for wearable applications. The goal of this project is to design novel, accurate, reliable, and secure authentication mechanisms using short-duration transient physiological signals collected from wearable devices. The research will investigate new signal processing and machine learning approaches to overcome key challenges, such as limited training data, time-varying signal quality, and the need for continuous classification with temporally evolving data streams. The research tasks include: (1) modeling and analysis of multispectral transient PPG signals; (2) design of novel algorithms for user identification and authentication in real time; (3) creation of adaptive learning models for continuous user-independent/user-dependent classification, and (4) integration of these models in a wearable prototype platform for evaluation in real-word scenarios. The investigators will also address fundamental challenges in signal processing, such as how to effectively identify, extract, and characterize short-duration transient biological signals and determine the signal qualities of sparse and/or noisy data and how to identify the motion artifacts and process them appropriately. The expected outcomes of this research project include new theoretical insights, algorithms, and system architectures that will advance the state of the arts in biometric authentication, signal analysis and processing, and wearable sensing. These outcomes will contribute to a wide range of applications including cybersecurity, health monitoring, and human-computer interaction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This three-year REU Site: InSPiRES: Integrated Circuits and Semiconductor Processes to Promote Responsible Engineering and Sustainability focuses on integrated circuits and semiconductor processes, emphasizing responsible engineering and sustainability, featuring projects drawn from biomedical engineering, civil, environmental, and geospatial engineering, chemical engineering, and electrical and computer engineering. A unique partnership with the Keweenaw Bay Indian Community will provide environmental and cultural stewardship lessons for the REU students, along with various outreach activities. Eight students each year will engage in research projects featuring semiconductor processing with environmental sustainability through the remediation of contaminated wastewater from electronic manufacturing. The program will allow students to explore critical issues in areas such as sustainable manufacturing, wastewater remediation, resource conservation, and environmental stewardship. Students will work on research projects that aim to improve the efficiency and sustainability of semiconductor manufacturing processes, recover valuable materials like copper from industrial waste, and develop innovative methods to treat and reuse contaminated water. These projects align with national priorities, such as advancing clean technologies, ensuring sustainable resource use, and promoting environmental resilience. The program addresses key issues such as technological advancement. As semiconductors are foundational to technologies like renewable energy systems, electric vehicles, and advanced communications, this project focuses on developing more sustainable and efficient manufacturing practices that support growth in these sectors. Integrating sustainability into engineering research, participants can learn about the environmental impacts of manufacturing, reducing waste, conserving resources, and ensuring cleaner water systems. The program goes beyond technical training by fostering cultural competence and environmental awareness by involving students in service-learning activities and partnerships with a tribal nation with deep commitments to land and water stewardship. This collaboration introduces students to Indigenous knowledge systems and underscores the importance of community- oriented research. This project provides students with opportunities to conduct meaningful, applied research, preparing them for graduate studies and professional careers in these STEM areas. This Site is supported in part by funds provided to the National Science Foundation by the Semiconductor Research Corporation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This I-Corps project is based on the potential commercialization of a more effective method for manufacturing a viral gene therapy. The technology could also be applied to the manufacture of viral vaccines. This technology seeks to accelerate the manufacturing rate and reduce the costs associated with gene therapies for the treatment of life-threatening diseases, like melanoma, sickle cell anemia, and muscular dystrophy. More specifically, this continuous purification technology could reduce capital manufacturing costs by 90% and operating costs by 50%, improving the competitiveness of the U.S. biotherapeutic market. By bringing this solution to market, this technological advancement could increase the accessibility and affordability of gene therapies for patients with both common and rare diseases, reducing overall healthcare costs in the U.S. while improving patient quality of life and life expectancy. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a continuous manufacturing process for viral gene therapy. The technology may reduce the costs of these biotherapeutics. Viral vector processing still uses batch processes, which are time consuming and expensive. This new solution is a continuous manufacturing process based on liquid-liquid extraction, which offers robust recovery (between 47-86%) for a diverse set of viral products, including adeno-associated virus (AAV) serotypes 2 and 9, herpesvirus, and lentivirus. This technology includes a large range of enveloped and non-enveloped viruses that can be purified with the same system. The continuous manufacturing approach has the potential to become a platform process for all viral particles, meaning that very little change in conditions is needed to purify a new viral particle. The ability to purify a wide range of viral particles with minimal adjustments makes the system highly scalable and adaptable, reducing the time and cost needed to develop purification processes for new viral therapies, vaccines, ande/or gene therapies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This research project aims to explore the history and behavior of Earth's magnetic field over millions of years by investigating volcanic lava flows in Iceland. A key objective is to obtain precise ages from a representative set of these flows, including the oldest flows in Iceland. Understanding how Earth's magnetic field has changed over time is crucial for improving our knowledge of the Earth's inner workings and its geodynamo system, which generates the magnetic field. By studying lava flows that span from 8.5 to 16 million years ago, the project will help fill significant gaps in the global database of Earth's magnetic field strength. The data gathered will significantly extend accurate models of Earth's magnetic field by several million years, offering new insights into geomagnetic field reversals and the geological processes that drive these changes. Additionally, this research will shed light on the tectonic and volcanic activity in Iceland, enhancing our understanding of Earth's geological processes and their timescales. The research also includes an important educational component, engaging undergraduate students in hands-on research and offering outreach opportunities to local communities, including the Keweenaw Bay Indian Community. These efforts will encourage broader public engagement with science, inspire and train the next generation of scientists, and help raise awareness of Earth's magnetic field and the global geological processes shaping our planet. Nearly continuous volcanic activity in Iceland over the last ~16 Ma presents an excellent opportunity to study the behavior, geometry, strength, and variability of Earth’s magnetic field, crucial for understanding geodynamo mechanisms and the evolution of the Earth's deep interior. While the paleodirectional record from the Icelandic lava flows is relatively well established, there are still significant gaps in the absolute paleointensity database, especially for the Miocene epoch. Additionally, the number of reliable age determinations for Icelandic lavas is limited. The project will conduct a comprehensive paleointensity, paleodirectional, and geochronological investigation of the Miocene lava flow sequences (~8.5 to 16 Ma) exposed in the Vestfirðir peninsula of northwestern Iceland. To do this, the team will use several paleointensity determination methods, including the IZZI version of the Thellier-Thellier method and the LTD-DHT-Shaw method, along with thorough rock magnetic analyses. 40Ar/39Ar incremental heating method will determine precise ages for a representative set of basalt samples, including some of the oldest lava flows found in Iceland. Filling the existing gaps in Iceland’s paleointensity database and improving data quality will advance our understanding of the spatiotemporal trends of the time-averaged geomagnetic field over millions to tens of millions of years. The results will provide vital insights into geodynamo mechanisms and contribute to the development of robust geomagnetic field models for the last 16 Ma. The results aim to refine the paleosecular variation estimates for Iceland and to probe the existence of long-term non-dipole features of the geomagnetic field. In addition, the new data will enhance understanding of the tectono-magmatic evolution of Iceland, including the duration of the magmatic hiatus associated with the transition from the Northwest Iceland rift zone to the Snæfellsnes-Húnaflói rift zone at ~15 Ma, and will improve the correlation of lava flow sequences across the Vestfirðir peninsula. The project will support a postdoctoral researcher and several undergraduate research assistants. 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

Mathematical Opportunities in Student Thinking (MOSTs) are high-leverage instances of student mathematical thinking that emerge in whole-class discussions. The challenge for teachers is to build on these opportunities to help the whole class understand the mathematics underlying these student contributions. To help teachers learn how to build on MOSTs, there is a need for professional development resources and tools that facilitators can use. There is also a need for research about how teachers use what they learn in professional development in their teaching. This project is developing a teacher learning sequence that will support teachers in learning to productively use student thinking that surfaces in-the-moment during their instruction—that is, in learning to build on MOSTs. This project builds on prior work that developed a framework for recognizing MOSTs and conceptualized the building practice teachers use to effectively capitalize on MOSTs. The overarching research question for the project is: to what extent does the professional learning sequence help teachers understand and enact the teaching practice of building? As part of this investigation, the project also considers factors that might mitigate teachers’ learning, such as teacher attributes (knowledge, practices, or experiences) and contextual factors. The study uses a design research framework to document how teachers take up aspects of building on MOSTs from the professional development, the process of teachers’ learning, and changes in their classroom practice. The study relies on data from the professional development activities, teacher surveys and interviews, and classroom data. The project sites include secondary schools in urban and rural settings. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This 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 planning project aims to increase understanding of the barriers to effective research administration service provision in U.S. Emerging Research Institutions, (ERI) Hispanic Serving Institutions, (HSIs) and Latin American ERI universities. These barriers are slowing science productivity across the U.S. and in Latin America. The project will plan the development of theoretically-grounded strategies to assist in overcoming these barriers. These include workshops, webinars, and other staff training materials. The project will also build an international network of research administration leaders across the Americas that will assist in the development, assessment, and deployment of these strategies while enhancing capacity for international research project development. Assessment of research administration capacity enhancement strategies is limited by the lack of studies that are grounded in organizational change theory. This gap is particularly important within the ERI HSI context because these institutions are critical to educating Latino students in STEM fields. Because Latinos comprise about 20% of the U.S. population (25% of U.S. children) low capacity in ERI HSI research administration offices creates barriers to the development of Latino scientists and engineers and, ultimately, slows U.S. success in critical STEM fields. This project aims to begin to address this problem in partnership with the Hispanic Association of Colleges and Universities (HACU), the NSF-funded VOLARÉ project, and the Association of Public and Land-grant Universities' Council on Research (CoR) through a theoretically-grounded approach to developing capacity enhancement strategies and assessing their impacts. 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 highly reflective nature of snow means that it plays a critical role in the climate system; snow reflects solar energy and regulates global temperatures. Snow processes are especially relevant in the Arctic system, where temperatures are rising more rapidly than the global average, partially because of feedback processes that take place as snow melts. As air temperatures increase, snow begins to melt, which lowers the snow’s reflectivity and increases the amount of sunlight it absorbs. The absorbed light leads to further temperature increase, and this warming process can have far-reaching implications for our climate. Although snow is one of nature’s most reflective materials, the exact reflectivity can be quite variable. Several factors darken snow, such as larger snow grain sizes and impurities in the snow like dust, soot and algae. One factor that is not well understood is how the liquid water content in snow reduces reflectivity. This presents significant uncertainty in determining how changing snowpacks will impact the climate system, particularly in the Arctic, as wet snow becomes more prevalent due to more frequent rain on snow events and larger extent and duration of surface melt on ice sheets and glaciers. This project will enhance our understanding of wet snow reflectivity through field measurements in Minnesota and Colorado, lab experiments, and modeling. Our results will probe fundamental physical relationships and therefore, broadly apply to cold regions. As part of this work, undergraduate students will be engaged in the research projects. They will collect and analyze data in an Engineering Thermodynamics class and will design and build instrumentation for this work and to support other faculty projects in a new Engineering Fellows Program. The investigator will also share teaching materials about snow reflectivity and climate online and at a workshop for faculty at minority serving institutions. The naturally high albedo (or reflectivity) of snow provides a strong control on earth’s surface temperatures. Because of this critical role, accurately reproducing snow albedo is essential for effective climate modeling. Even in the Arctic, the already prevalent periods of wet snow are increasing because of more frequent rain on snow events and increased extent and duration of glacial surface melt; however, nearly all existing snow albedo models employ albedo schemes designed for dry snow. These models play a key role during snow melt because of the amplifying effects of the snow albedo feedback process, where melting snow leads to lower albedo, higher temperatures, and further snow melt. Therefore, explicitly incorporating the effects of liquid water content on snow albedo is a critical next step in improving model accuracy. The proposed work aims to quantify the effect of liquid water content on snow albedo, combining several approaches. 1) The investigator will conduct field-based measurements of albedo, liquid water content, grain size and snow impurities in Minnesota and at Niwot Ridge in Colorado to determine the effects of individual physical properties on the overall snow albedo. Wet snow conditions at these locations represent those that are increasingly common in the Arctic. 2) Through a new course-based undergraduate research experience (CURE) implemented in the Engineering Thermodynamics class, students will conduct laboratory measurements of the reflectance of artificial snow with controlled grain size and liquid water content. 3) The investigator will complement this work with two different modeling approaches to calculate wet snow albedo to investigate an array of snow conditions and inform potential changes to physically based snow albedo models. Studying these phenomena in different landscapes, in the laboratory, and in simulations will allow us to extrapolate our understanding of wet snow albedo to cold regions more broadly, particularly in the rapidly changing Arctic. This project will support the development of students through multiple avenues by providing opportunities to engage in research and build their scientific identities. The investigator will develop an Engineering Fellows Program in which students work with faculty over the course of the year on a design project, in addition to enrolling in a professional development seminar course. The investigator will also partner with the Ice Drilling Program Education team to serve as a visiting scientist in the School of Ice workshop and create an online Virtual Field Lab on snow albedo. 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

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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

The construction industry has the lowest digitalization level among all major industries in the US and suffers from long-standing challenges, such as low productivity, difficulty in effective collaboration and communication, and lack of digital skills in the construction workforce. Therefore, broadening the adoption of digital technologies and cyber-infrastructures (CI) in the construction industry is a key to its future success and will revolutionize its operations. Building information modeling (BIM) is considered as the most significant and promising CI in construction, which refers to a sophisticated process of generating digital representations of the physical and functional characteristics of facilities. Due to the cross-disciplinary nature of BIM, the lack of effective BIM education resources, such as instructors and curriculum materials, has become a major challenge for BIM CI training in the US. Recently, generative artificial intelligence (AI) has attracted huge attention from the public for its ability to understand and respond to human language, suggesting its potential to revolutionize education and intelligent tutoring. This pilot project aims to develop a cognitive and generative artificial AI-driven CI training platform by integrating advanced skills and knowledge of BIM into the undergraduate curriculum in construction programs. The new knowledge gained from this project will build a solid foundation for understanding how generative AI can be used for intelligent tutoring of engineering education including construction engineering, civil engineering, and electrical engineering in the US, which could help mitigate the nation's labor shortage. This project will pioneer a new paradigm of integrating generative AI into the Nation’s educational curriculum to train the future scientific community. The project will develop a BIM CI training framework for construction education by synergizing generative AI and cognitive science theories. Specifically, this project will 1) develop undergraduate-level BIM-related curriculum materials by incorporating the knowledge space theory from cognitive science to fuse these materials. Expert discussion will be employed to ensure the quality and suitability of developed materials for undergraduate construction programs in the US; 2) develop a generative design method by integrating parametric modeling and computational techniques to automatically produce BIM CI worked examples with various levels of difficulties. These worked examples will serve as a significant supplement to curriculum materials, helping instructors prepare tailored problems for BIM CI training; and 3) explore the feasibility of Generative AI for BIM CI training. This project will develop a ChatGPT-based web application as the CI training platform. The training platform developed from this project can provide personalized, adaptive, and interactive training to students, leading the transformation of the educational ecosystem across all engineering fields. Extensive experiments will be conducted to validate the developed curriculum materials, worked examples, and the training platform. By providing a personalized learning platform, this project will contribute to the establishment of deeper engagement with various institutions and underrepresented groups in Michigan, the Midwest, and the US. 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 propulsion of floating objects via self-generated surface tension nonuniformities, also known as Marangoni surfing, represents a fascinating phenomenon observed in the world of living organisms while also bearing promising potential for robotic applications. For example, in nature, this mode of locomotion is employed by water-walking insects for speedy movement in emergency situations and by certain bacterial swarms for rapid interfacial migration toward nutrient-rich regions for further colonization. In recent years, Marangoni surfers of various sizes have been engineered to perform a wide array of tasks, including environmental sensing and monitoring, microfluidic manipulation, and interfacial self-assembly. The goal of this project is to investigate the motion of Marangoni surfers at spherical interfaces, which are difficult to generate on Earth but achievable in zero gravity aboard the International Space Station (ISS). The propulsion of these interfacial surfers will be studied, with a specific focus on the importance of both the global interfacial curvature of the spherical water droplet and the local interface curvature around the surfers. Additionally, this project will have broader societal impacts through its integrated educational initiatives, which include outreach to underrepresented middle and high school students, research mentorship of community college and graduate students, and curriculum development. The principal objective of this project is to investigate the individual and collective hydrodynamics of Marangoni surfers that self-propel on spherical interfaces. This research aims to generate new knowledge by establishing a computational-experimental framework that includes both ISS- and ground-based measurements. The framework is designed to capture the complex interactions between the motion of active particles, the transport of released species, and the effects of interface curvature and confinement. Notably, performing experiments on a levitating spherical drop in microgravity allows us to probe the importance of interface curvature on particle motion and assembly while simultaneously eliminating the local gravitationally-induced interface curvature effects from around the active particle that have been shown to play an oversized role in inter-particle interactions. The insights gained from this project are expected to define the foundational principles for designing self-propelled surfers optimized for curved interfaces, potentially leading to transformative advancements in robotics and microfluidics. Also, the results of this research will enhance our understanding of self-assembly processes, facilitating the rapid production of small-scale structured materials. Moreover, this study will shed light on the role of Marangoni stresses in the colonization of antibiotic-resistant bacteria at fluidic interfaces, offering new strategies for tackling infectious diseases by elucidating bacterial colonization and survival mechanisms in adverse conditions. 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

Developing software, that is, writing code that can be executed to accomplish a desired task, is becoming increasingly intertwined with engineering practice. The process of developing software involves not only writing code, but designing and maintaining code over longer time scales as its users, developers, stakeholders, requirements, and underlying technologies change. Thus, in the professional practice of engineering, there is a need to not only learn programming (writing code), but software engineering practices and tools (SEPTs), which include concepts such as writing software tests to maintain code correctness across updates, or using tools to manage the installation and update of a project's software dependencies. This project will study the use of these SEPTs among undergraduate students and industry practitioners in three different engineering disciplines (mechanical, electrical, and civil), with the aim of understanding (1) what SEPTs are currently being used in the teaching and practice of these disciplines, (2) the factors that lead students to adopt these SEPTs, and (3) the extent to which students and practitioners agree on how they value these SEPTs in their work. The insights from this project will shed light on how students develop their professional skills and identities as engineers, particularly how they view software engineering within the context of their own disciplines. Comparing the ways in which students and practitioners value these skills will also illuminate the shifts that occur as students develop in their disciplines and careers. Finally, the understanding from this project may help us identify opportunities to improve the way we teach SEPTs in engineering, and better articulate the impact that these improvements will have in the professional formation of engineering students. Specifically, in this project, we will conduct an explanatory mixed methods study to answer the following research questions: (1) What software engineering practices and tools do undergraduate engineering students and practitioners currently use in their work? (2) What factors influence undergraduate engineering students' adoption of SEPTs, and to what extent? (3) In what ways do student perceptions of the value of software engineering practices and tools align with those of practitioners in their field? To answer Q1, we will develop a survey to assess the SEPTs currently used by students and practitioners, identifying differences in SEPT use by factors such as student/practitioner status, discipline, or previous computing experience. Our survey will adapt instruments from prior research, and will be grounded in professional and educational standards in software engineering, namely, the Software Engineering Book of Knowledge (SWEBOK) Guide and the SE2014 Curricular Guidelines. To answer Q2 and Q3, we will then select SEPTs of particular interest identified in the survey (e.g., those widely used by practitioners but rarely used by students within a discipline), and conduct follow-up interviews with students and practitioners. Specifically, our interview protocol will be grounded in Expectancy-Value Theory and the Unified Theory of Acceptance and Use of Technology frameworks (UTAUT/UTAUT2). In answering Q2, we will focus on the factors motivating students to adopt (or not adopt) certain SEPTs, while in answering Q3, we will focus on value perceptions of a SEPT and how these differ among students and practitioners. We expect our findings to result in a list of SEPTs that can be assessed across different engineering disciplines, as well as a revised model of factors (e.g., usefulness for engineering tasks, contribution to professional engineering identity) affecting the adoption of SEPTs. In addition to laying groundwork for further research in the above topics, our project will also inform the development of new interventions to better teach SEPTs in their relevant engineering contexts at the undergraduate level, shaping the software development skills of future professional engineers. 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 research studies ultra-high energy cosmic rays, which are elementary particles and atomic nuclei from outside of our Galaxy which interact in the Earth's atmosphere and produce a cascade of particles called an air shower. The study uses the Pierre Auger Observatory (PAO) in the Mendoza province of Argentina, the world's largest operating cosmic ray observatory. PAO has 1,660 surface detector stations spread over 3,000 square kilometers, with four fluorescence telescopes. PAO is currently completing a significant upgrade, called AugerPrime, which will simultaneously measure both muonic and electromagnetic (photon, electron, and positron) components of extensive air showers. PAO detection of ultra-high energy photons or neutrinos traveling from their source without decay or deflection will elucidate the nature of the acceleration sites and improve our understanding of the extreme universe. This work is a key component of a broad multi-messenger approach for understanding the very high energy universe. The project involves students extensively and hosts public outreach both at the home institution and at the Visitor Center in Argentina. The present work uses the enhancements of AugerPrime to increase sensitivity for detecting ultra-high energy neutrino and photon-induced air showers. This makes detection more likely, and furthers PAO’s contributions to multi-messenger astrophysics. The research builds on previous accomplishments, moving from characterizing promising trigger algorithms to implementing those algorithms in the AugerPrime surface detector electronics, installing those systems in the field, and integrating with higher-level offline analysis software. 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 · 2024-08

PROJECT SUMMARY Cardiac resynchronization therapy (CRT) is a standard treatment for heart failure (HF) by coordinating the function of the left and right ventricles. However, 30-40% of CRT recipients do not have improved clinical symptoms and cardiac functions. The main reasons for CRT non-response include: (1) Selection of patients based on electrical dyssynchrony measured by ECG under current guidelines is not optimal. (2) Mechanical dyssynchrony is proven effective but is not fully explored. (3) The CRT left ventricular (LV) lead may not be placed in an appropriate position in a significant number of patients. Due to the complexity of HF and the mechanism of CRT pacing, the advancement of image-guided approaches for CRT is still limited: existing predictors that measure electrical dyssynchrony and mechanical dyssynchrony are insufficient to characterize the severity of electrical/mechanical dyssynchrony in all ventricular segments; on the other hand, numerous complicated inter-correlated predictors entangle multi-stage clinical decision making for CRT delivery. The objective of this research is to improve CRT patient selection and LV lead pacing by integrative analysis of electrical dyssynchrony on ECG and mechanical dyssynchrony on gated SPECT myocardial perfusion imaging (MPI). Different from existing studies, which use supervised machine learning (ML) to combine all clinical factors to predict CRT response, this translational approach is dedicated to knowledge discovery and clinical decision-making support; we will use unsupervised machine learning to find patient subgroups that have a higher likelihood to respond to CRT and use reinforcement learning (RL) to both optimize and explain the multi-stage clinical decision-making process of CRT patient selection, and design a new method incorporating electrical dyssynchrony, myocardial viability, and mechanical dyssynchrony to recommend the LV pacing sites. Completion of this proposed project will result in the discovery of new clinically interpretable knowledge and computer techniques to improve CRT response in clinical practice. It is important to note that all the new algorithms and knowledge will receive rigorous validations. The proposed research shows our continuous effort and innovative methods to investigate this long-lasting and significant cardiovascular problem. It utilizes state-of-the-art computer algorithms and techniques to analyze cardiovascular images for improved medical treatments, and will greatly benefit our students, offering opportunities for them to engage in cutting-edge cardiovascular research. It will thus diversify university research by introducing clinical cardiology practice to our well-established computing programs and promoting integrative education and discovery-based learning for undergraduate students. The preliminary data, the PI’s experience in developing innovative computer algorithms on medical image analysis and machine learning and supervising undergraduate research students, and our interdisciplinary collaboration, have fully prepared the team for the execution of this project.

NIH Research Projects · FY 2024 · 2024-08

Project Summary: Neuroinflammation is a condition that can cause damage to the brain and lead to diseases like Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis. It is caused by inflammation in the central nervous system (CNS) and is linked to issues with mitochondria. Unfortunately, treating neuroinflammation is challenging due to the blood-brain barrier (BBB), which makes it difficult to deliver drugs to affected areas and target specific neuronal populations. Additionally, drugs that work for peripheral inflammation may not be as effective in the CNS due to its unique microenvironment and cellular interactions. Our team is exploring ways to reduce neuroinflammation using exosomes, which are small vesicles that can deliver molecules to recipient cells. We plan to use exosomes to deliver anti-inflammatory agents directly to the CNS, creating mitochondrial-targeting exosomes (MTEs) that carry anti-inflammatory microRNA (MTE-miRNA) to treat neuroinflammation. This method is expected to restore mitochondrial function, reduce oxidative stress, and modulate neuroinflammatory responses. MTE-miRNAs have the potential to revolutionize the treatment of neuroinflammatory disorders. To achieve this, we will prepare and evaluate MTEs containing microRNAs with anti-inflammatory properties, assess the impact of different dosages on reducing neuroinflammation in rats, and evaluate the effectiveness of MTE- miRNA treatments over different periods. Our primary outcomes are to improve mitochondrial function and inhibit inflammatory mediator production levels in rats with neuroinflammation. We will also gather crucial data on potential side effects and an appropriate treatment duration that balances efficacy and safety, informing future human studies. Another focus of this project is to train students extensively on exosome-based therapy and its potential use in treating neuroinflammation. Our educational goal is to equip students with the necessary knowledge and skills to make valuable contributions to biomedical research, and potentially create new and effective treatments for neuroinflammatory disorders.

NSF Awards · FY 2024 · 2024-07

The project team will deploy the University of Wisconsin-Madison Space Science and Engineering Center Portable Atmospheric Research Center (SPARC) in L’Anse and Houghton, Michigan from October 1 – 21, 2024 for a Track 1 Facilities for Atmospheric Research and Equipment education and outreach project. The project will focus on observing and understanding the extent to which lake-induced temperature inversions and lake breezes impact the dispersion of locally derived air pollution in L’Anse, Michigan, located on the southern shore of Lake Superior. Undergraduate and graduate students enrolled in atmospheric sciences courses at Michigan Technological University and the University of Wisconsin-Madison will gain field measurement and data analysis experience, and outreach will be conducted to K-12 students and the wider community. The research objectives of this project are to characterize the atmospheric boundary layers over by measuring (1) vertical profiles of tropospheric temperature and water vapor; (2) wind speed and direction profiles; and (3) mixing height of the boundary layer using. The SPARC will also be used to measure vertical profiles of tropospheric aerosols and their physical properties, and to indicate periods of inversion-based aerosol trapping. The primary observation location for this project, L’Anse, is situated in Keweenaw Bay along the southern shore of Lake Superior, adjacent to the reservation of an Ojibwe tribe, the Keweenaw Bay Indian Community. The L’Anse Warden Electric Company, a wood, paper, plastics, and tire-derived fuel waste-to-energy plant is located in L’Anse. Additionally, community households burn wood for heating in the colder months of the year. Community members have expressed concern that the emissions from the facility and wood burning adversely impact their air quality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2022-06

Our proposal endeavors to secure the Shimadzu LCMS-2050 system, aiming to propel our research in fluorescent probe synthesis for biomedical applications to new heights. This cutting- edge instrument is poised to redefine our approach by offering real-time monitoring capabilities, enabling the exploration of innovative reaction conditions, optimization of synthetic methods, and rapid identification of lead candidates. The system's technical excellence lies in significantly enhanced efficiency, streamlined processes, and reduced resource consumption. Beyond its technical merits, this proposal underscores our commitment to spearheading impactful and resource-efficient scientific innovation in our field. By investing in state-of-the-art technology and adopting sustainable, responsible resource management practices, we are dedicated to advancing our research at the forefront of the discipline. The strategic acquisition of this advanced instrumentation positions our research group to pioneer groundbreaking fluorescent probes using more efficient and eco-friendly synthetic methodologies.

NIH Research Projects · FY 2025 · 2022-04

SUMMARY Hypertension is a major risk factor for cardiovascular disease, with salt sensitive hypertension (SSH) accounting for 51% of all cases. As augmented sympathetic nerve activity (SNA) and dysregulated neuroendocrine secretion are known to play critical roles in the development of SSH, w e propose a study to investigate the mechanisms whereby hyperactivity of the brain orexin system, may critically influence SNA and neuroendocrine dysregulation, thus contributing to SSH development. Orexin A (OXA) is a multifunctional neuropeptide produced by hypothalamic neurons that plays various physiological roles, including cardiovascular function, through binding with its receptors. Recent studies show that upregulation of brain orexin signaling occurs in several animal models of hypertension suggesting implications for overactive brain orexin signaling in hypertension development. However, the impact of orexin system in the development of SSH and detailed molecular mechanisms underlying orexin system mediated hypertension development are poorly understood. Our preliminary data shows that a high salt (HS) diet results in hypertension, and significantly increases expression of OXA and orexin 1 receptor (OX1R) in the hypothalamic paraventricular nucleus (PVN) of Dahl salt sensitive (Dahl S) rats, an animal model for human SSH, but not in salt resistant rats. Interestingly, HS diet intake in Dahl S rats also increases OX1R expression in adrenal glands, a component of hypothalamic-pituitary- adrenal (HPA) axis which is a central neuroendocrine response system to combat stress and regulate multiple physiological functions. The PVN controls SNA outflow and neuroendocrine hormone, such as vasopressin (AVP) and corticotropin-releasing hormone (CRH), production. Central administration of OXA increases SNA outflow and PVN AVP expression as well as plasma corticosterone levels, a hallmark of HPA axis activation. Hyperactivity of HPA axis is involved in multiple diseases including hypertension. Lesions in the PVN prevent high salt induced hypertension in Dahl S rats. This body of evidence suggests that PVN OX1R activation is involved in SNA and HPA axis regulation, and increased PVN OXA-OX1R signaling induced by high salt intake may stimulate neuroendocrine systemic secretion, trigger long-term adaptive changes through regulating downstream gene expression, producing excessive excitatory neurochemicals, resulting in augmented PVN sympathetic tone, and leading to SSH. The objectives of this project are: (i) investigate whether chronic knockdown of PVN OX1R expression decreases plasma corticosterone levels, attenuate HS induced increase in SNA, and prevent SSH; (ii) elucidate the molecular mechanism underlying the relationship between OXA-OX1R and its augmentation of SNA and dysregulation of HPA axis. A combination of in vivo gene transduction, electrophysiological recordings, and molecular and biochemical approaches will be employed to answer our questions using salt sensitive and normotensive rat models. Our studies may identify new targets for therapeutic intervention in hypertension.

NIH Research Projects · FY 2024 · 2021-09

Project Summary Influenza A affects 5-30% of the world’s population annually, resulting in 3 to 5 million cases of serious illness and 250,000 to 500,000 deaths each year. Vaccination is the best way to prevent disease. >80% of the influenza vaccines are made in eggs, and this makes the process slow and not able to quickly change as the influenza virus mutates. The vaccine is optimized to grow in eggs, which makes it a less like the circulating virus, and thus reduces the effectiveness of the vaccine. One way to improve the vaccine is to use cell-based vaccines. However, cells-based vaccines are expensive to produce and the manufacturing facilities are expensive. After 10 years on the market, cell-based vaccines are not the dominate vaccine used in the US. We propose to develop a novel manufacturing process of a cell-based influenza virus like particle (VLP). The process will continuously produce the VLP, which will allow for smaller equipment and thus reduce the cost of building a new manufacturing plant. Continuous processing also reduces operating costs, allowing for the vaccine to be sold at a lower cost and likely competing in price with egg-produced vaccines. The VLP will elicit a stronger immune response than egg-based vaccines and the modular design will allow for quick adaption of the vaccine to the circulating influenza strains. Our team has designed a novel, end to end, continuous process to manufacture an influenza VLP. First, we will continuous produce the VLP in a unique, three-reactor bioreactor cascade that will allow for continuous processing using a baculovirus production system in Sf9 insect cells. There is not currently a continuous baculovirus production system in use. The continuous downstream will use aqueous two-phase extraction and other polishing steps to purify the VLP. Chromatography and other periodic operations will be avoided. The VLP will produce HA protein antigenically identical to the chosen circulating strain with no potential for selection of HA mutations and the HA protein can easily be changed to another circulating influenza strain, as needed. Process analytics will be conducted to confirm the purity and antigenicity of the produced influenza VLP. At the completion of this project, the team will run the first end to end continuous process for one month to produce an influenza VLP and the process economics will be evaluated to determine the economic feasibility of the process. The VLP will have superior immunogenicity to egg-derived vaccines and subunit vaccines. This will be the first ever demonstration of a truly end-to-end continuous VLP production process that will revolutionize biologics manufacturing for multiple products, including other vaccines and gene therapy vectors.

NIH Research Projects · FY 2024 · 2021-06

PROJECT SUMMARY Epigenetic regulation is one of eukaryotic cells' most central and complex processes. Enzymatic demethylation of N𝜀-methylated lysine residues in histone proteins is performed almost exclusively by non-heme Fe(II) and 2-oxoglutarate (2OG) - dependent histone demethylases (KDMs). KDMs are divided into six classes (KDM2-8), which differ in their specificity towards the methylation state of the lysine residues and the position of the lysine residue in the sequence of the histone proteins. Importantly, KDMs from different classes are involved in diverse steps of epigenetic regulation and are linked to various pathologic processes, including cancers and other genetic disorders. The catalytic mechanisms of KDMs with standard histone substrates containing methyl groups (HSCMGs), including the dioxygen (O2) binding, activation, and substrate oxidation, have been comprehensively explored by us during the previous award period; however, the diverse catalytic mechanisms of KDMs with histone substrates containing lysine residues with large alkyl groups (HSCLAGs) are entirely unexplored. Experimental studies show that human KDMs such as KDM2A, KDM4E, KDM6B, and KDM7B can catalyze diverse transformations with a variety of HSCLAGs implementing different chemical mechanisms such as hydroxylations, hydroxylations followed by dealkylations, and sequential hydroxylations, however, the catalytic mechanisms of these transformations remain unknown. Therefore, this proposal's overarching goal is to elucidate how KDMs perform catalysis of HSCLAGs compared to HSCMGs regarding the primary substrate binding, dioxygen transport and binding, and their diverse reaction mechanisms. Subjects of our study are four human KDMs from four different classes (2, 4, 6, and 7), which differ in their structure and substrate specificities, namely KDM2A, KDM4E, KDM6B, and KDM7B. An exciting aspect of the research plan is that it will provide motivated undergraduate students with a unique opportunity to engage in top class research using modern computational and experimental methods in line with the mission of the Academic Research Enhancement Award.

NIH Research Projects · FY 2024 · 2020-04

Project Summary Over 6 million people in the United States are afflicted by chronic ulcers and this number is expected to grow. Wound healing is impaired in these patients, who are often inflicted with other diseases (i.e., diabetes, venous disease, or arterial disease), receiving anti-inflammatory steroid treatment, or receiving chemo- or radiotherapy for cancer. Chronic ulcers can negatively affect patient quality of life and productivity and are a substantial financial burden to the health care system. Patients typically require extended periods of hospitalization and may require over 26 weeks for full recovery. Treatment of diabetic ulcers and related amputations in the U.S. totaled over $10 billion in 2011. Current treatments of non-infected ulcers are costly and have demonstrated mixed results. Most importantly, these treatments do not actively prevent infection, which can often complicate healing and affect long-term stable wound resolution. In the current proposal, we seek to manipulate a unique bioinspired redox chemistry found in mussel adhesive proteins to create a novel, multifunctional nanocomposite adhesive that can potentially promote wound healing while minimizing infection. There are three main objectives to this proposal. Objective 1: Prepare novel antimicrobial adhesive and evaluate its performance. Objective 2: Evaluate the adhesive’s cytocombatibility and its ability to promote healing and prevent infection in culture. Objective 3: Verify candidate adhesives’ ability to promote healing in an infected wound model in diabetic mice. Future work will evaluate adhesive’s ability to promote healing in a larger animal model.