Worcester Polytechnic Institute

NSF Awards · FY 2024 · 2024-08

ExpAR is envisioned as CISE community research infrastructure that addresses significant challenges in augmented reality (AR) research by enabling more scalable, controllable AR experimentation under real-world conditions. Traditionally, experimentation is limited by the need for diverse technical skills, extensive human effort, and substantial financial investments in specialized hardware. ExpAR will introduce an innovative AR experimentation room designed to facilitate automatic data collection, configurable experimentation, and convenient participant observation via online surveys. Further, ExpAR will abstract AR systems in terms of a generalized pipeline of sensing, understanding, and rendering, allowing researchers to efficiently conduct both component-level and holistic evaluations. During the planning phase funded through this award, the PIs will focus on understanding the community needs and priorities via a range of engagement activities, including formative interviews, local site workshops, and proof-of-concept prototyping and data collection. ExpAR has the potential to advance AR applications by enabling researchers from various disciplines, including arts and visualization, to perform scalable and controllable experimentation without an extensive background in systems, networking, or hardware. This enhanced inclusivity will foster a more diverse research community and accelerate the development of innovative AR solutions. The managing team is committed to supporting the growth of this community by preparing prototype releases, creating tutorials, and providing open datasets for various AR experiments. This effort will enhance current AR applications' capabilities and ensure that future AR technologies are more robust, user-friendly, and accessible. 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 · 2024-07

ABSTRACT The decreasing quality of oocytes, associated with maternal age, is a major component of reduced fertility in older women. Many molecular markers are observed in deteriorating oocytes, including transcriptomic changes. Interestingly, in most animal models, the end of oogenesis and early embryogenesis proceed in the absence of transcription, relying on stored messenger RNAs (mRNAs). The developing oocyte carefully manages different populations of RNA during maturation, fertilization, and zygotic genome activation to successfully jumpstart embryogenesis. The accumulation of these maternal transcripts during oogenesis is therefore essential. Yet we know very little of how the transcriptome is sculpted after the transcriptional machinery is inactivated. What drives transcriptomic changes and how are they contributing to oocyte quality? RNA tailing, or the addition of untemplated nucleotides to the 3' end of RNA molecules, is a post- transcriptional process that has long been associated with the regulation of RNA stability and translation. Both nucleotide composition and length of tails can determine tail function. RNA tails are especially dynamic in the germline and the early embryo. The objective of this work is to identify the terminal nucleotidyl transferases and exonuclease that modulate RNA tails and to elucidate the mechanism that mediate the downstream effects on RNA stability and translation in the oocyte. First, I will examine the physiological function of TNTs and exonucleases in C. elegans fertility. Second, I will characterize changes in RNA tail length and composition during oogenesis and early embryogenesis to shed light on conserved pathways involved in generating viable and competent oocytes. Third, I will identify the co-factors that act upstream and downstream of TNTs and exonucleases, to coordinate their activity, and modulate tail-mediated regulation of RNA stability and translation. I am uniquely qualified to conduct this research, having studied different types of RNA and their biology throughout my graduate and postdoctoral training. In Katherine McJunkin’s laboratory, I have used the C. elegans model system and large-scale genomic screens to identify TNTs responsible for miRNA tailing and to assess its impact on microRNA turnover. In the proposed work, I will apply state-of-the-art techniques to dissect the machinery responsible for mRNA tailing in the context of reproduction and fertility. This work seeks to explore post-transcriptional mechanisms regulating gene expression during the oocyte-to-embryo transition and their contribution to oocyte quality.

NSF Awards · FY 2024 · 2024-07

The broader impact of this Partnerships for Innovation – Research Partnership (PFI-RP) project is to develop innovative, cost-effective, and environmentally friendly soil improvement solution for coastal regions. This technology addresses a hidden but serious climate-induced hazard to coastal civil infrastructure due to seawater intrusion and concomitant soil salinization. Traditional calcium-based methods of stabilization are neither effective nor sustainable due to high energy consumption and large carbon emissions in their manufacturing process. This technology offers a greener alternative with a significant market potential and applications to civil infrastructure, water systems, and other ground improvement projects. The market size is expected to grow to $30.2 billion by 2032. This project seeks to create socio-economic benefits by enhancing the sustainability and climate resilience of coastal communities. Additionally, the project addresses global issues such as conserving energy and resource recovery by recycling waste materials. The use-inspired, translational research will replace the current, non-sustainable calcium-based stabilization practice, generating jobs and promoting the adoption of the new materials and technologies. An integrated entrepreneurial educational and leadership development plan will be used to train graduate students to become future entrepreneurship leaders in advanced, next-generation soil improvement technologies. The project presents an innovative, cost-effective, and practical solution to improve saline coastal soils when traditional Portland cement-based methods are ineffective or even problematic. The soil improvement technology addresses technological, industrial, and market needs by developing cost-effective and ‘greener’ soil stabilizer derived from abundant industrial waste (i.e., fly ash-based geopolymer). Such a soil stabilizer is technically sound for treating salt-bearing coastal soils. The research objectives of this project are to: (i) Optimize fly ash-based geopolymers suitable for improving coastal salt-bearing soils; (ii) Develop a soil improvement procedure that is ready for engineering implementation by holistically considering strength development, volume change, and durability through medium-scale laboratory experiments; and (iii) Demonstrate cost effectiveness, and environmental and resiliency benefits of the soil improvement technology by performing life cycle cost and environmental impact assessments, facilitating commercialization and adoption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

Primary cilia mediate transduction of all major signaling pathways, which in turn modulate cilia morphology and functional outputs. The research in my lab is focused on identifying new genes and mapping signaling networks that regulate cilia assembly during development and cilia remodeling in response to environmental stress at single-cell resolution in vivo. To pursue these research questions, we use sophisticated molecular genetics, proteomics, and live imaging approaches in C. elegans with complementary studies in mammalian cell culture models. In the next five years, we propose to address three research goals. A first major goal is to define molecular mechanisms that regulate heterotrimeric G protein signaling during cilia morphogenesis. Heterotrimeric G proteins and their canonical activators G protein coupled receptors (GPCRs) localize to cilia, modulate cilia morphology, and when disrupted cause genetic disorders in humans. Key outstanding questions that we propose to address are: What conserved and species-specific mechanisms regulate ciliary trafficking of heterotrimeric G proteins? How do non-canonical, GPCR-independent mechanisms modulate functions of ciliary G proteins? A second major goal is to determine in vivo mechanisms that regulate and mediate cilia remodeling in response to stress. After exposure to environmental stress, primary cilia undergo structural remodeling, which is associated with altered signaling output. We want to know how stress modulates signaling capacity of cilia in vivo. What are the stress- and cell-specific mechanisms of stress-induced cilia dynamics? A third major goal is to identify new conserved genes and pathways in cilia morphogenesis and function. Cell- and tissue-specific differences in the ciliogenic programs are not well understood, and the causative genes for many ciliopathy cases are unknown. For example, nearly 40 percent of individuals with neurodevelopmental ciliopathies are estimated to be without genetic diagnosis. We want to harness the power of bioinformatic approaches and C. elegans genetics to ask – what are the developmental mechanisms that operate in cell-specific contexts to assemble cilia? Collectively, these projects will generate fundamental insight into cell-specific signaling networks that regulate cilia assembly, remodeling, and ciliamediated cellular functions. This knowledge is an important steppingstone toward better understanding of the genetic basis of ciliopathies, and more broadly, of mechanisms that allow cells to communicate with their environment. This award would also allow us to train researchers, launch new collaborations, and follow new hypotheses that emerge in the course of this study

NSF Awards · FY 2024 · 2024-06

The placenta is a crucial organ that supports in utero human development; yet, it is one of the least understood human organs. Within the placenta, trophoblasts are the main cellular component and play several biological roles, including invasion into the endometrium to anchor the placenta and to facilitate nutrient and waste transport. The biochemical signals that regulate trophoblast function are unclear. The goal of this proposal is to better understand how the tissue microenvironment promotes cell growth and secreted extracellular vesicles within that microenvironment influence trophoblast invasiveness. Besides potentially transforming our current knowledge of factors influencing trophoblast invasion, the anticipated results may inform molecular mechanisms that regulate cellular invasion in other healthy and disease tissues. In addition, the project will build research capacity by expanding experiential research experiences and fostering didactic training at the intersection of cell biology and biomanufacturing. In the placenta, there are two main types of trophoblast cells, villous and extravillous trophoblasts, that have important and distinct functions. Villous trophoblast cells are important for nutrient and waste transport, while extravillous trophoblasts facilitate host integration by invading decidualized endometrium. Both types of trophoblasts secrete extracellular vesicles called exosomes, which are thought to play an important role in cellular communication and regulating cell phenotype. The PI hypothesizes that the trophoblast environment influences invasiveness, and that this consequentially impacts exosome production and composition. To test this hypothesis, two objectives are proposed. The first objective will focus on characterizing how oxygen tension and biochemical factors influence trophoblast invasion of extracellular matrix. The second will focus on characterizing how oxygen tension and biochemical factors alter exosomal payload, which in turn alters trophoblast invasiveness. Trophoblast function within the placenta, including their invasive potential, make them an ideal candidate to study the relationship between invasion and exosome-mediated communication. While the anticipated findings will inform placental function, this research will also further knowledge on cell migration and invasion applicable to several cell types that experience similar biochemical cues. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Computer systems are embedded and integrated into everyday items such as cars, smart cards, and medical devices. The security properties of these systems are crucial to keep their users safe and secure, and thorough testing of those properties is needed before deployment. This project explores the creation of an open-source ecosystem (OSE) for the measurement and analysis of embedded systems security. The OSE is based on Chipwhisperer, an existing open-source project that has grown over the past decade into a collection of hardware and software to support every aspect of embedded security testing. The project identifies a path for ChipWhisperer to become a conduit between developers of testing hardware and software for embedded systems and their users and use cases. This project organizes a set of concrete scoping activities to identify the main roles in the ChipWhisperer OSE, including (a) the managing organization, (b) the users, and (c) the developers. To support governance, the project seeks to integrate the ChipWhisperer developers and with OSE experts to establish a technical charter and to identify techniques for continuous development and sustainability. With a series of workshops and tutorials, co-located with major conferences on embedded security, the project explores use cases of interest to the research community. The project engages open-source contributors to improve the embedded security testing process by contributing open interfaces for measurement hardware, standard data formats, analysis software, and embedded testing targets. Furthermore, the project develops and promotes use cases that benefit from the OSE, including academic research, artifact exchange, crypto competitions, standard testing techniques, and embedded security beyond cryptography. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This renewal Research Experiences for Teachers (RET) Site: Engineering for People and the Planet: Research Experiences for Teaching Integrated STEM is hosted by the Worcester Polytechnic Institute (WPI). The RET site focuses on the United Nations Sustainable Development Goals for an integrated approach of learning and doing science, math, and engineering through real-world problems that focus on people and the planet. A pre-service teacher and a local in-service teacher will conduct summer research for six weeks. Participants will work with a faculty mentor on topics such as healthy lives, quality education, clean energy, responsible consumption, and climate action. The teachers will participate in professional development workshops to translate their research experience into lesson plans and classroom activities, as well as present their work to broader audiences. This project will impact many high school science, tech/engineering, and math (STEM) teachers and high school students in underserved districts with populations that are racially and ethnically diverse. By building the capacity for strong STEM educators in our region, we can provide relevant STEM learning opportunities that engage students to develop real-world problem-solving skills and help prepare the next generation of scientists and engineers. This renewal Research Experiences for Teachers (RET) Site: Engineering for People and the Planet: Research Experiences for Teaching Integrated STEM is hosted by the Worcester Polytechnic Institute (WPI). The objectives are to: 1) Provide authentic research experiences in labs focusing on Engineering problem-solving and research that is centered on People and the Planet (U.N. Sustainable Develop Goals); 2) Deliver high-quality professional development for the teachers to create an “Integrated STEM” lesson plan based on their research experiences, implement their lessons with their students, and share the lesson plan to broader audiences; 3) Increase participants’ confidence and knowledge about how engineering benefits people; and 4) Foster a community of educators for mentoring and support among the pre-service teachers, area in-service teachers, WPI faculty and graduate students, industry partners, and the STEM Education Center. Four pre-service teachers and four local in-service high school teachers from high needs schools will be paired with WPI faculty for six weeks. Research topics may include: 1) genetically engineering a plasmid to study antibiotic resistance, 2) brain sensing for personalized learning environments, 3) photocatalysts for treatment of contaminated water and production of clean energy, 4) water-based technology for plastic recycling, and 5) continuous manufacturing for cleaner pharmaceutical engineering. Participants will have weekly workshops to guide their research projects as they learn research techniques and culminate in a public RET Poster Symposium. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-06

PROJECT SUMMARY Sex is an important risk factor to consider in concussion but has been historically under-studied. Computational models of the human head transform external head impacts into regional brain strains that are well believed to be the primary cause for injury. They are thought to offer more effective biomechanical mechanism underpinning concussion. However, current brain injury models do not consider sex differences in morphology, either at the organ level or at the microscale, axon level. Therefore, current brain injury models are not feasible to study the biomechanical basis of sex differences in concussion. This proposal has two specific aims. First, we will develop subject-specific brain injury models of the whole brain and axons. Next, we will use subject-specific head impacts from male and female ice-hockey players to characterize sex differences in brain strain and extent of axonal damage, and then correlate them with a range of biomarkers. Sex differences in both concussion and subconsussion will be studied. The proposed research will provide an initial understanding of the sex-related differences in strains sustained in male and female brains and the extent of axonal structural damages. Ultimately, these efforts will contribute to refined health policymaking and sex-specific concussion mitigation strategies to reduce the growing health-care burden.

NSF Awards · FY 2024 · 2024-06

The field of software development needs developers to write secure code, as well as to continuously respond to evolving threats and adapt system designs to meet new security needs. This requires developers to gain a deep understanding of foundational concepts in secure programming, and continuously learn and practice defensive, secure, and robust coding. Given the current lack of consistent and comprehensive secure programming training in most computing programs, and the need for any training to evolve to meet new requirements, it is essential to have mechanisms that make secure programming training adaptive and intelligent. The goal for this project is to develop one such mechanism, named SecTutor, which is a dual-purpose adaptive testing and intelligent tutoring system. Using SecTutor, learners will be able to identify their current missing knowledge and areas of misunderstanding in secure programming, and access content to improve learning at their own pace. SecTutor will provide immediate feedback and learning analytics to motivate and guide learners. The project will take an assessment-driven approach for personalized, self-directed learning: a rigorous assessment tests the learner's level of knowledge and skill so that the intelligent tutoring system can calibrate the instruction directly to the learner. Specifically, the first step of the project will be to construct an adaptive test to diagnose learners' current level of foundational understanding in secure programming. This adaptive test will be based on a rigorously constructed secure programming concept inventory. This test will also diagnose what topics the learner is finding difficult or is fundamentally not understanding. The next step of the project will be to build an intelligent tutorial system that will provide both content and guidance for the learner to master secure programming concepts and skills. The third step will be to incorporate learning analytics into the system that will not only provide feedback to individual learners, but also provide mechanisms for instructors to gather information about their learners, compare them to other demographics, analyze secure programming questions, and adapt their curriculum to address specific challenges or customized requirements. SecTutor will eventually be integrated into other existing secure programming resources and will be adopted broadly for secure programming training. 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 · 2023-09

This proposal entitled “Development of EEG/MEG Source Reconstruction with Fast Multipole Method” will address the currently unmet need for significantly more accurate, near real-time EEG/MEG source localization tools. The result will be the construction of an entirely new, high-speed, high-resolution EEG/MEG source localization pipeline that includes: (i) field solution using the charge-based Boundary Element Method (BEM) with Fast Multipole acceleration; (ii) inverse problem solution algorithm utilizing the circuital reciprocity theorem and global cortical basis functions with an unlimited number of cortical sources; (iii) precisely segmented head models and; (iv) precise MEG compatible EEG hardware, verification and validation experiments and their documentation. If successful, the developed technology will be adaptable to any branch of cognitive neuroscience diagnosis (EEG and MEG), providing enhanced understanding of a patient’s response during antidepressant treatments and greater insight into overall mental well-being. Aim 1 Algorithm for EEG/MEG source reconstruction with fast multipole method Aim 1A Algorithm construction and development (WPI Worcester and MPI Leipzig) Aim 1B Preliminary results (WPI Worcester and MGH) Aim 2 Integration with high density dry-electrode EEG system. EEG/MEG Measurements Aim 2A Design a 256 channel MEG-compatible dry-electrode system (TU Ilmenau) Aim 2B Perform MRI/EEG/MEG verification measurements (MPI Leipzig & TU Ilmenau) Aim 2C Verify, modify and improve BEM-FMM based source reconstruction software (MPI Leipzig, WPI Worcester, TU Ilmenau) Coupling high-resolution algorithms with the finest yet robust and easy-to-use EEG hardware is one major proposal goal. Such coupling could make an advanced high-density dry-electrode EEG very accurate.

NIH Research Projects · FY 2025 · 2023-09

IMPACT Abstract Chronic pain impacts 50 million U.S. adults, severely interferes with the work and life of over 25 million, and costs $635 billion annually for medical treatment and resultant loss of productivity. While some non- pharmacological complementary pain management methods, such as Mindfulness-Based Stress Reduction (MBSR), are effective at reducing the pain of some patients, others do not respond. Clinicians lack the tools to accurately and reliably predict which patients will respond to complementary treatments. In response to RFA-NS-22-050 (UG3/UH3), IMPACT – Integrative Mindfulness-Based Predictive Approach for Chronic low back pain Treatment proposes using machine learning methods (a subfield of AI) to identify biopsychosocial predictive and monitoring markers of the response to MBSR for chronic low back pain (cLBP). This research will include a population (total n=350) suffering from cLBP. Comprehensive biopsychosocial data (locomotor activity, sleep, circadian rhythms, heart rate variability, depression, anxiety, pain outcomes, and social support) will be collected from patients treated with MBSR for cLBP. Aim 1 (UG3) will involve the initiation of a clinical trial of MBSR for cLBP (n=50) and ML modeling with longitudinal biopsychosocial data and related clinical trial datasets to identify candidate predictive and monitoring markers of the response to MBSR for cLBP prior to expanding the trial in the UH3 phase. Milestones for transition from the UG3 phase (Aim 1) to the pending clinical trial of the UH3 phase (Aims 2+3) will include: (1) finalized data collection and primary analysis protocols for the clinical trial of MBSR for cLBP, (2) success with passive data collection procedures and experimentation with ML model training and testing for the identification of predictive and monitoring biopsychosocial markers of the response to MBSR for cLBP, and (3) preliminary validation of candidate ML-based biopsychosocial predictive and monitoring markers of the response to MBSR for cLBP using statistical and cross-validation methods. Aim 2 (UH3) will expand the clinical trial initiated in Aim 1 to collect biopsychosocial data from a sample of 300 individuals. Aim 3 (UH3) will involve ML modeling with data collected in Aim 2 to identify and validate accurate biopsychosocial predictive and monitoring markers of the response to MBSR for cLBP. To complete our aims, clinician scientists from Boston University, University of Massachusetts Chan Medical School, and Cambridge Health Alliance with extensive expertise in successfully recruiting and engaging populations in clinical trials of mindfulness interventions for pain will collaborate with biomedical, data scientists and machine learning researchers from Worcester Polytechnic Institute. This proposed project will ultimately enhance clinical decision- making and targeted treatment of cLBP.

NIH Research Projects · FY 2025 · 2023-09

Annually, ∼300,000 neonates are admitted to the neonatal intensive care unit (NICU) in the U.S., mostly due to respiratory distress. After a successful medical treatment in the NICU, earlier discharge to a stable home life could improve the chance of survival for neonates. However, the outpatient management of babies with fragile respiratory status is challenging in the absence of reliable remote monitoring devices suitable for use at home. Due to the small form factor and ease of use, contemporary home monitoring devices deploy sensors measuring SpO2, the saturation of hemoglobin bound to oxygen. Nevertheless, accurate assessment of oxygenation is complicated and not possible with only one parameter. Therefore, in clinical practice, invasive blood gas measurements - including partial pressure of oxygen (PaO2), which is the free dissolved oxygen (not bound to hemoglobin) - are not entirely replaced with SpO2. PaO2 is an essential cardio-respiratory measurement and the direct indication of lung effectiveness. A direct noninvasive method of assessing PaO2 is PtcO2, oxygen gas that diffuses through the skin. Creating an ability to sense two respiratory parameters (PtcO2 and SpO2) with one wearable is unique and superior to the current practice of measuring only SpO2, filling an important gap in miniaturization of the transcutaneous blood gas monitors.

NIH Research Projects · FY 2025 · 2023-07

ABSTRACT All major open-source brain modeling packages currently available (e.g., SimNIBS, DUNEuro, SciRun, ROAST) as well as their commercial counterparts (e.g., Sim4Life, Ansys Maxwell, COMSOL) use the electric potential- based Finite Element Method (FEM) for electromagnetic modeling. FEM has been continuously improved over the past 60 years, is simple to implement and can model averaged tissue anisotropy. At the same time, FEM may have some intrinsic weaknesses specifically affecting high-definition brain modeling. The present proposal aims to develop and disseminate a novel alternative brain modeling engine. In contrast to FEM which uses the electric potential, it operates with the primary (bio)physical quantity – surface (and volumetric) induced electric charge density. To model charge interactions, it naturally employs the modern Fast Multipole Method (FMM) instead of FEM. For piecewise homogeneous biological media of any complexity, only surface charges at bound- aries are present. Their interactions are most accurately described by the boundary element method (BEM). This combination of BEM and FMM is the new proposed BEM-FMM charge engine. The principal advantage of BEM- FMM is its numerically unconstrained spatial field resolution. AIM 1. Improve and complete the BEM-FMM modeling engine. Sub-aims: (i) major speed up of the BEM-FMM engine; (ii) new adaptive mesh refinement algorithm; (iii) new volumetric anisotropic co-solver, (iv) computing activating function with unconstrained numer- ical resolution and; (v) full-scale numerical verification against established FEM solvers SimNIBS and DUNEuro at meso (submillimeter) scale. AIM 2. BEM-FMM testbed for non-invasive recordings and stimulation. 2A. Develop BEM-FMM source localization stream for EEG/MEG recordings. We will construct and validate an improvement over currently existing BEM EEG/MEG source localization software suites using BEM-FMM. We will deliver a ready-to-use testbed with twenty head models and EEG/MEG experimental data. 2B. Develop a BEM-FMM modeling stream with extracerebral compartments for noninvasive stimulation. For enhanced resolution, we will automatically add fine-resolution major extracerebral compartments into existing segmenta- tions pipelines based on anatomical rules. We will then deliver the ready-to-use BEM-FMM testbed targeting TES and ECT (electroconvulsive therapy) where their effect might be critical for the correct dosage prediction and correct targeting. AIM 3. BEM-FMM testbed for invasive electrical stimulation. 3A. Validate BEM-FMM testbed for modeling activating function in animal axons. Verification for a giant inter-neuronal axon of cray- fish Procambarus clarkia via electrical/magnetic stimulation and compound action potential generation for paral- lel fibers in turtle Pseudemys Scripta Elegans cerebellum will be done. 3B. Verify BEM-FMM testbed for mod- eling DBS responses. Using retrospective clinical data, we will develop a BEM-FMM algorithm for patient- specific multipolar DBS and evaluate whether the model predictions align with clinical observations.

NIH Research Projects · FY 2024 · 2023-03

Project Summary/Abstract We propose to determine how the dynamic mechanical environment of the valve regulates the attachment, invasion, and differentiation of host cells into “off-the-shelf” decellularized tissue engineered heart valves (TEHVs). We hypothesize that dynamic mechanical stretch and fluid shear stress regulate repopulation of the TEHV matrix by enhancing and aligning 3D matrix adhesions and activating latent TGF-beta from the matrix. To test our hypothesis, biopolymer scaffolds seeded with fibroblasts will be cast in stretchable wells and microfluidic chambers until remodeled into isotropic or aligned neo-tissues and then decellularized in situ. We will then quantify the extent to which vascular and circulating cells adhere to and invade the matrix under cyclic stretch or dynamic flow conditions relevant to in vivo implantation. Cell attachment, infiltration, proliferation, apoptosis, phenotype, and endothelial-to-mesenchymal transition markers will be quantitatively monitored over time. TGF-beta activation and 3D matrix adhesion protein content and alignment will be examined, and associated signal transduction pathways will be interrogated to determine the mechanisms governing the cell responses. The results from this systematic study will have a direct impact on TEHV development by determining the signals that aid (or hinder) host cell repopulation of the valve matrix with the goal of optimizing valve design for adaptive remodeling under complex in vivo conditions. The administrative supplement will enable additional studies within the scope of the parent grant in addition to mentoring activities to support students from marginalized communities.

NIH Research Projects · FY 2025 · 2022-09

I. PROJECT SUMMARY: Smartphone-based wound infection risk screener and care recommender by combining thermal images and photographs using deep learning methods Chronic wounds, which affect 6.5 million patients in the US12 severely affect their quality of life, can take up to a year to heal and re-occur in 60-70% of patients. Wounds often get infected (bacteria in wound), resulting in limb amputations if not treated properly and on time1. In current practice, at the Point of Care (POC) (e.g., nurses visiting patients’ homes and trauma sites), caregivers who are not wound experts have no way to diagnose infections. Thus, they cautiously refer wounds suspected to be infected to clinics for debridement of dead tissues, blood tests and infection diagnoses by experts57-60. However, referrals increase time before infected wounds are treated, and the chances of limb amputation. Moreover, some referred wounds end up not being infected, wasting patient and expert time and expenses (e.g., transportation)15-16. What is needed is a digital health solution that enables non-expert wound caregivers to accurately detect infected wounds at the POC even without debridement and provide standardized recommendations on evidence-based care and when to refer. Smartphones equipped with high resolution cameras and the processing power to run machine/deep learning methods are owned by most wound caregivers in the US56. Prior work by Goyal et al1 reported preliminary results that show that infection can be detected from visual attributes such as increased redness in/around the wound in a photograph using deep learning (accuracy 0.727± 0.025, sensitivity 0.709 ± 0.044, specificity 0.744 ± 0.05). While promising, their results need to be improved and validated before clinical applications. Moreover, their dataset included already debrided wounds with easily discernable infection cases, and they did not recommend evidence based best care and decide when referrals to wound clinics were the best course of action. Certain thermal image patterns are reliable indicators of wound infection20, and some models of smartphones are now equipped with thermal cameras55. Our hypotheses are that 1) the accuracy of smartphone wound infection detection can be improved by combining thermal images with photographs jointly analyzed using a deep learning method 2) recommendations for actionable, evidence-based wound care and when to refer can be generated using machine learning to standardize care provided by non-experts. In response to NOT-EB-19-018, we propose research to investigate the capability and accuracy of detecting infected wounds before debridement using deep learning methods applied to combinations of wound photographs and thermal images and generating care and referral recommendations. We also propose integration of the smartphone-based infection screener into our group’s existing wound assessment system7-9, 21-29 and validating it on new patients (N=100). Success on our proposed aims will increase the number and objectivity of wound infections detected outside the wound clinic and fast-tracked to the clinic for treatment, reducing the number of patients who require amputations. Our findings will apply to diverse wound types including diabetic, pressure, arterial, venous, surgical61 and trauma wounds62, which all get infected.

NIH Research Projects · FY 2025 · 2022-08

Project Summary The regeneration of damaged or diseased tissues that serve biomechanical functions, such as musculoskeletal tissues, has been a long-standing challenge in clinical practice and research. Regenerative engineering offers a promising alternative to auto- or allografts for tissue regeneration by combining biomaterial scaffolds, viable cells, and bioactive factors. Engineering scaffolds that provide both mechanical support and biological activities is critical for regenerating such tissues with biomechanical functions. However, currently existing scaffolds, which include either tough polymers with limited bioactivities or soft hydrogels with poor mechanical properties, fall short of meeting both mechanical and biological needs. To address this issue, we propose the development of a novel family of emulsion bioinks to enable the 3D bioprinting of strong living scaffolds with built-in mechanical robustness and desirable biological functions for tissue regeneration. The encapsulation of biologics (cells and bioactive factors) within scaffolds presents an attractive strategy to equip the scaffolds with desired biological functions. The major roadblocks to encapsulate biologics within tough polymers include their lack of bioactivity and the frequent usage of harmful chemicals, such as organic solvents and/or toxic reactants. In this study, a water-in-oil emulsion bioink is designed by dispersing an aqueous internal phase of hydrogel droplets (microgels) with encapsulated biologics in an external phase of tough polymer solution. It is hypothesized that microgels will protect the functions of encapsulated biologics from harmful chemicals by limiting their diffusion from the external to internal phases. The solidification of tough polymer around each dispersed microgel during 3D-bioprinting will mainly contribute to mechanical robustness of the final scaffold. The preliminary data demonstrates that: 1) >95% viability of fibroblast cells is achieved in an emulsion bioink; and 2) the resulting emulsion scaffolds afford both the mechanical robustness (elastic moduli 5-40 MPa) and >90% cell viability. This project will initiate with the development of cytocompatible and bioprintable cell- laden emulsion bioinks, followed by characterization of 3D-bioprinted emulsion scaffolds, and conclude with validating the functions of encapsulated bioactive factors and cells within scaffolds for meniscus regeneration as a test model. This model will include assessments of proliferation, fibrochondrogenic differentiation in vitro, and neo-menisci formation in vivo. Overall, our approach presents a new method to produce mechanically strong and biologically functional living scaffolds by integrating emulsion chemistry and 3D bioprinting technology. We anticipate that this work will have a broad and significant impact on regenerative engineering by benefiting repair or regeneration of broad-spectrum tissues with biomechanical functions.

NIH Research Projects · FY 2025 · 2022-05

Alternative splicing (AS) of precursor mRNA provides an important means of genetic control and is a crucial step in the expression of most genes. AS gives rise to multiple isoforms that can exhibit differential stabilities, molecular binding capabilities, and phenotypic effects, thereby greatly expanding the functional capacity of genes. These functions are frequently deregulated in human disease, leading to aberrantly expressed isoforms that can act as drivers and “rewirers” of cellular pathways. Given the intrinsic role of AS in nearly every aspect of biology, tools and technologies for the functional understanding of isoforms are desperately needed. Unfortunately, prediction of isoform functions are notoriously difficult, due to our only crude understanding of the molecular determinants of isoform activities as well as a paucity of experimental datasets annotated at isoform resolution. The experiments, in turn, are challenging in their own right, due to a lack of robust methods for the detection, mapping, and phenotyping at isoform-resolution in vivo. As a consequence, one of the biggest gaps in the genomics and proteomics fields is an understanding the functional and evolutionary implications of the astonishing complexity of the human proteome. To make progress towards this gap, we must reformulate existing approaches. Here, we propose to build computational and experimental methodologies that are intertwined across the entire development and evaluation lifecycle. Computational approaches, capitalizing on ever-improving machine learning algorithms, can predict the effect of AS at high coverage. Experimental validation, on the other hand, is critical to benchmark the predictions as well as shed light on heretofore uncharacterized features of isoform functionality. The goals of this project are to develop (i) a predictor of protein isoform stability, a “first line of evidence” and prerequisite of isoform functionality, (ii) a novel bioinformatics approach to study the “rewiring” effects of AS isoforms on protein interactions, and (iii) a novel measure, the alternative splicing impact factor, that predicts the functional role of an isoform based on metrics such as the loss of interaction and expression patterns, and apply this concept to determine AS-induced phenotypes in-silico and in vitro/vivo (cell-based assay). Each computational stage will be closely complemented with a highly customized and novel experimental approach– large-scale isoform proteogenomics, interactomics, and functional assays experiments–to validate and benchmark the predictors, as well as feed into an iterative computation-experimental “virtuous cycle”.

NIH Research Projects · FY 2024 · 2021-08

Project Summary The goal of this proposal is to define the mechanisms of Cu homeostasis in the cell envelope of the pathogen Salmonella enterica. This organism is an important and frequent cause of gastroenteritis, as well as, systemic infections. Cu is required as a redox co-factor in the catalytic centers of enzymes. However, free Cu is highly reactive and deleterious to cells. Cu, along with the oxidative burst, is central in host-pathogen interactions as part of the innate immune response. As such, redox/Cu homeostasis is essential for bacterial virulence. While there has been significant progress in identifying cytoplasmic Cu homeostatic mechanisms, there is a lack of understanding of how the cell envelope handles and distributes Cu, whilst maintaining the associated redox balance. Our goal is to define and model the Cu distribution in the Salmonella cell envelope and identify its molecular links with the redox stress response. The aims of this proposal are: 1) Quantify Cu fluxes and equilibria among periplasmic, cytoplasmic and external compartments while defining the size and identity of the periplasmic Cu sink pool. 2) Define the role of CueP as the periplasmic Cu chaperone exchanging the metal with various targets. We will monitor CueP in vivo abundance, as well as its apo/holo equilibria, in response to changes in periplasmic Cu levels. We will determine how CueP obtains Cu from membrane transporters in the inner and outer membranes and delivers it to alternative carriers to achieve steady state levels of periplasmic Cu. CueP participation in the metallation of several periplasmic cuproenzymes will be assessed. 3) Determine the role of the ScsABCD system at the interface of Cu- and redox-homeostasis. The redox activity of these enzymes will be determined and in vivo substrates identified. The relation between ScsABCD activity and Cu binding to substrates or among ScsABCD enzymes will be established. To achieve these aims, the joint efforts of two laboratories with complementing expertise will use a combination of approaches (modeling of metal fluxes, proteomics, metallomics, in vitro host/pathogen interaction). Our approach to systematically elucidate the mechanisms of Cu/redox homeostasis in the envelope of an important human pathogen is novel, timely and innovative.