Rensselaer Polytechnic Institute

NSF Awards · FY 2025 · 2025-01

Crude oil and petroleum spills contaminate soil, surface water, and groundwater. Crude oil is made up of many different molecules, and when oil is released into the environment, microbes in the soil and water degrade and change the structure of these molecules. These biologically transformed oil molecules are collectively called “oxygen-containing organic compounds” (OCOCs). Although OCOCs are not yet regulated in the USA, these compounds are of emerging concern because they are known to harm human and ecological health. The goal of this project is to determine how OCOCs form from crude oil spills in the environment and how this process affects their persistence in contaminated groundwater. To achieve this goal, microbial degradation experiments will be conducted to evaluate OCOC formation and breakdown with different crude oil types under various environmental conditions. Data gathered from experiments and from a historic oil spill site will be used to build a reactive transport model that predicts the environmental persistence and behavior of OCOCs in oil-contaminated groundwater plumes. This work benefits society by providing stakeholders with information needed to assess the human and ecological health risk of OCOCs at contaminated sites. Training of early career investigators and graduate and undergraduate students will increase scientific literacy and enhance workforce development. Petroleum-contaminated groundwater contains a mixture of natural dissolved organic matter (DOM), dissolved petroleum hydrocarbons, and OCOCs in the form of polar metabolites or hydrocarbon oxidation products. The OCOCs are contaminants of emerging concern because they harm aquatic flora and fauna but are not well characterized by traditional analytical methods. The overarching goal of this project is to link OCOCs to their petroleum source, and to assess intrinsic and extrinsic controls on their environmental persistence. This work combines experimental and field-collected data to constrain a reactive transport model for simulating the natural attenuation and distribution of OCOCs. Petroleum-contaminated groundwater will be collected along a plume transect from a historic oil spill to recover microbial communities for experiments and inform modeling efforts. Specific objectives include: i) determining the chemical variability of OCOCs among parent oil types and between anaerobic and aerobic biodegradation conditions, ii) evaluating whether biological “priming” of native DOM alters the OCOC molecular signatures, and iii) predicting the environmental persistence of OCOCs in an oil-contaminated aquifer. Microbial degradation of specific OCOC molecular signatures will be assessed using ultrahigh resolution mass spectrometry, Fourier-transform infrared spectroscopy, and optical proxies. Successful completion of this research will provide information necessary for stakeholders to address and mitigate the human and ecological health risks associated with OCOCs. 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

Abstract for NSF proposal #2430223, entitled “ENG-AI: EPCN: Small: Computationally Efficient Learning using Graph Neural Networks with Theoretical Guarantees,” PI: Wang, Meng: Associate Professor, Rensselaer Polytechnic Institute Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and processing graph-structured data. They have found applications in diverse fields such as robotics, power systems, recommendation engines, and social network analysis. Despite those success, their widespread application faces significant challenges, including high computational requirements and lack of interpretability and performance guarantees. This proposal aims to lay the groundwork for overcoming those challenges, establishing theoretical foundations and developing practical algorithms to enhance the efficiency and reliability of GNNs across various engineering applications. Key objectives include systematically analyzing how graph topology and network architecture influence performance by delving into the dynamics of learning and generalization in GNNs. Most of the existing theoretical works on GNNs focus on either analyzing the expressive power of GNNs or bounding the generalization gap between training and testing or characterizing the training convergence, disregarding the joint problem of learning dynamics and generalization. This study encompasses a range of GNN architectures, from established models like graph convolutional networks (GCNs) to emerging structures such as graph transformers (GTs) and graph mixture of experts (GMoEs). A crucial aspect of this proposal is the optimization of computational and memory resources in various aspects. Techniques such as graph data aggregation reduction, network pruning, attention sparsification, and dynamic joint sparsification methods will be explored to streamline GNN operations. These efforts are complemented by the introduction of novel GMoE architecture to further enhance efficiency. This proposal will advance the development of trustworthy AI systems applicable across societal infrastructures like social networks and power grids. Moreover, by focusing on computational efficiency, the proposal contributes to the advancement of green AI, aiming to reduce economic costs and environmental impact associated with large-scale AI models. Collaboration with IBM through the RPI-IBM AI Research Collaboration expands the project's reach and ensures real-world applicability. Additionally, an integral education and outreach plan is included, spanning from K-12 education to professional training in AI research and application. 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

Due to their exceptional strength-to-weight ratios, fiber-reinforced plastics (FRPs) are increasingly used in high-performance applications such as aerospace, automotive, wind energy, sports equipment, and medical devices. However, they face challenges due to damage from repeated fatigue loading and poor recyclability. This can result in deficiencies in terms of performance, cost, safety, and reliability of structural components. Existing approaches to improve fatigue life, such as adding nanomaterials or self-healing agents, provide limited benefits, as they cannot fully reverse accumulated damage. This research aims to overcome these challenges by developing a new class of FRPs based on vitrimers. Vitrimer polymers allow for the "reversal" of fatigue-induced damage when exposed to heat. By exploring the balance between material stiffness, healability, and glass transition temperatures, the project seeks to develop vitrimers that not only enhance mechanical properties but also maintain the ability to heal repeatedly, significantly improving the durability, safety, and lifecycle of FRPs in demanding applications. The project will also develop interactive learning modules and mobile apps to teach students about the design of sustainable polymers and their applications. Additionally, the project will involve outreach initiative for underrepresented groups, partnering with Montclair State University (a Hispanic Serving Institution), and engaging K-12 students through hands-on workshops and virtual demonstrations. Vitrimers are polymers with associative dynamic covalent adaptive networks; these polymers have gained traction in the last decade, yet their potential for fatigue damage reversal remains largely unexplored. The researchers hypothesize that fatigue damage in vitrimers can be reversed by periodic heating above the topology freezing transition temperature (Tv) – at which rearrangement reactions occur. Previous work on examining the mechanics of vitrimers has mainly focused on low glass transition temperature (Tg) variants; this compromises their creep resistance and limits their applicability in high-performance sectors. To address these challenges, this project will develop high Tg vitrimers using tri- and tetra-functional epoxides to enhance stiffness and creep resistance while maintaining reversible healing properties. This project will systematically explore the “tug-of-war” between healability and Tg as a function of various parameters such as, polymer chemistry, catalyst type, and concentration, etc., thereby generating new fundamental knowledge. By combining reactive molecular dynamics simulations with experimental techniques, we aim to address three core scientific challenges: (1) understanding the trade-off between increasing Tg and maintaining healability, (2) elucidating the processing-structure-property relationships in high Tg vitrimers, particularly the influence of cross-linking density and molecular architecture on thermo-mechanical properties and healing efficiency, and (3) examining the impact of fiber reinforcement on the healing behavior of fiber-reinforced vitrimer composites, focusing on how the polymer-fiber interface responds to cyclic loading and periodic healing. This research could transform the design of FRPs by enabling materials that not only resist but actively reverse fatigue damage, significantly enhancing durability, safety, and lifecycle performance across various industries while also promoting educational and diversity outreach. 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

When solutions or solutions that contain particles are frozen on Earth, particle settling and liquid motion due to gravity affect the structure of the resulting solid. By freezing these solutions in microgravity condition, the subtle forces involved in growing the solvent crystals and forming particle-assemblies between those crystals will be revealed. The experimental setup on board the ISS will provide an ideal environment for imaging crystal growth and particle motion under strictly controlled conditions. Comparing the structural features obtained on the ISS with those on the Earth will reveal the mechanisms of structure formation that are obscured by gravity. Combined with computational models, the proposed research will help designing new materials as well as improving both the structure and properties of existing ones for applications in biomedicine, catalysis, water purification, and energy generation and storage. This award is ideally suited for the integration of research and teaching in STEM educational programs that incorporate space themes to increase interest and diversity, and to improve skills in K-12 STEM education. The fundamental knowledge gained by performing structure formation studies on the ISS and Earth will be of interest to both the freeze casting and the directional solidification communities. To date, only few short duration (25 seconds on a parabolic flight) microgravity freeze casting experiments have been performed. This study is the first to analyze and quantify complex dynamics and interactions of directional crystal growth and particle self-assembly, in the presence and the absence of gravitational forces, and with an externally applied magnetic field. The complementary set of experimental and simulation results will enable a more systematic exploration of currently unpopulated spaces in material structure and property. Two complementary approaches will be pursued: i) ex situ and in situ observations and quantification of the freeze casting process, and analysis of the morphology of ice-templated materials manufactured in microgravity and in terrestrial under well-defined and controlled conditions; and ii) the development of simulation techniques using the experimentally determined input data to enhance the predictive capability of freeze-casting models for fabrication of critical materials in Space and on Earth. The new experimentally validated, model-based tools will enable the science-based design and manufacture of new materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary The proposed Rensselaer Alzheimer’s Fellows to Accelerate Entrepreneurship in Life Sciences (RAFAELs) program will augment existing research and entrepreneurial activities at Rensselaer Polytechnic Institute (RPI) to train graduate students in commercialization skills in ADRD field. RAFAELs program is composed of three parts: aim 1, Launch annual summer RAFAELs courses focused on ADRD translation and commercialization, enriched by AI and data science; aim 2, Launch a RAFAELs course to nurture and evolve startups toward financial sustainability; aim 3, Establish a mandatory industrial internship for each RAFAELs fellow. Upon achieving these Aims, this Program will bridge the gap between foundational entrepreneurial knowledge and tangible commercial prospects. It will empower researchers to seamlessly navigate the ADRD translational journey from ideation to market realization, paving the way for flourishing careers beyond academia.

NSF Awards · FY 2024 · 2024-09

This project proposes to trace the history and reasonings behind the decision-making process for the lifecycle of the Arecibo Observatory (i.e., its funding, building, maintenance, and decommissioning), one of the most historically significant astronomy observatories in the world. In doing so, this study will better understand how decisions to fund scientific research facilities are made and how those decisions impact local communities. In better understanding the history behind these decisions, the project will result in important takeaways for how scientific institutions, like the National Science Foundation, might make more impactful use of funding for future large scientific projects like new telescopes. The project will train a graduate student in qualitative research methods and analysis. It will also translate to policy briefings to be distributed to science funding agencies as well as public-facing publications such as the Conversation. The project’s scope is therefore to better trace the social and political history of the Arecibo Observatory. It focuses on the question: What are the histories of decision-making behind the creation, funding, siting, building, maintenance, and eventual gradual decommission of the Arecibo Observatory? It will answer this question through archival work as well as interviews with people who were involved in the processes of decision-making, such as former Observatory directors, policy-makers, among others. Critically, this research is novel in that it focuses on more than just the moment where we decide to fund a new project. Instead, it proposes to trace the entire lifecycle of a large research facility, including decisions around defunding. 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

NON-TECHNICAL SUMMARY: This project will advance our understanding of polymer gels, an economically important class of materials that consist mostly of liquid but are held together by a sparse mesh of long polymer molecules. Gels are important to health applications, where they are used in medical treatments such as wound dressings and drug delivery systems that gradually release medicines. They are also used in a variety of industries, ranging from food and personal care commodities, to defense and infrastructure, where they find use as sealants, adhesives and flame retardants. This project will advance our understanding of polymer gels by measuring how variations at the molecular level determine their mechanical properties, e.g. how resistant these materials are to being squeezed and stretched, properties that are central to their many uses. The accompanying educational and outreach efforts seek to broaden the pipeline of STEM scientists and engineers by (i) introducing young learners at the K-8 level to molecules via playful learning environments on mobile phones, (ii) increasing awareness of materials engineering as a discipline amongst pre-college students through hands-on learning workshops, and (iii) supporting undergraduate research projects. TECHNICAL SUMMARY: This research will contribute knowledge to the ongoing, now multidecadal, endeavor of establishing a complete mechanistic understanding of the mechanical behavior of polymer gels. The objective is to establish the correlation between spatiotemporal fluctuations of individual monomers and network junction points with the morphology and macroscopic elastic properties of the parent gel networks, which are invariably heterogeneous. The timeliness of the research arises from recent advances in super-resolution optical microscopy that allow for the tracking of single molecules with unprecedented spatiotemporal resolutions and the imaging of gel morphology on the nano and sub-micron scale. Gels which exhibit inhomogeneous distributions of monomer density, commonly known as spatial heterogeneities, will be studied by (i) quantifying spatiotemporal fluctuations with single monomer fidelity in gels that are prepared across a variety of thermodynamic conditions, and (ii) correlating variations in spatiotemporal fluctuations of monomers across nano and sub-micron scale morphological features with the macroscopic bulk modulus of the respective gels. 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

Graph-structured data appear in many applications such as social networks, functional brain networks, and protein-protein interaction networks. Graph convolutional networks have demonstrated significant performance improvements over traditional methods for performing large scale graph tasks due to their learnable parameters that can capture more and task-adaptive information. Despite the success of graph convolutional networks, accurate and efficient algorithm development is still in its early stages. This proposal focuses on addressing the challenges for handling large-scale graph tasks using graph convolutional networks. New models will be built to produce task-desired solutions and to exploit feature information in challenging large-scale graph tasks. Novel numerical approaches will be designed to solve existing and new-built models in an efficient and reliable way. This project aims at achieving good practical performance on real graph tasks, provably fast convergence for the designed algorithms, and low overall complexity in computing numerical solutions. The project will involve graduate and undergraduate students, in particular underrepresented students in STEM, by involving them in research activities. The research findings will be integrated into curricula, thus impacting both undergraduate and graduate education. Novel mathematical models and algorithms for deep graph learning will be designed and analyzed. First, variance-reduced neighbor sampling approaches and a new constrained optimization model aimed at enabling more efficient algorithms will be designed for deep graph representation learning. Asynchronous parallel versions of these new methods will also be developed to increase efficiency. Second, new deep graph representation learning -assisted models will be built for graph matching, by using sparsity-promoting regularizers or penalty terms that can lead to task-desired solutions. On solving the new models, accelerated low-order methods will be designed by using the proposed variance-reduced neighbor sampling and momentum acceleration techniques, under the framework of the augmented Lagrangian method or the alternating minimization. Third, new models with finite-sum structured nonconvex constraints will be built for graph clustering by using deep graph representation learning to exploit feature information. Variance-reduced stochastic methods will be designed to solve the models by exploiting the finite-sum structure. These investigations are expected to lead to novel models and efficient algorithms for large-scale graph tasks that currently cannot be completed in an accurate and/or efficient way. 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

Graphs, representing complex sensing and other societal systems like disease networks, social networks, and communication networks, are essential in understanding interactions within these systems. By accurately modeling relationships and structures within data via graphs, today machine learning over graphs (LoGs) plays a vital role in various applications. However, LoG introduces additional hyperparameters such as graph topologies and nodal embeddings into the already complicated neural network training processes. Traditionally, LoG approaches relied on user-defined heuristics to extract features encoding structural information about a graph. However, this process becomes prohibitively expensive in large models and high-dimensional data regimes, and the performance of LoGs highly depends on the choice of these hyperparameters. To address these challenges, the project puts forth a unified bi-level optimization-based training framework for LoGs with automatic selection of hyperparameters. The project also supports the education and diversity goals of the NSF by integrating LoGs research advances into machine learning courses taught in University of California at Irvine and Rensselaer Polytechnic Institute, making cutting-edge LoGs techniques more accessible to a wider range of researchers and students, fostering innovation and inclusivity in the scientific community. Towards this goal, this project aims to develop a bi-level optimization (BLO) framework for trustworthy and efficient LoG, called BLoG. In addition to the basic algorithm and optimization theory development for BLoG, the project will build a tri-level BLoG problem for robust and adversarial graph neural network training tasks, tailoring gradient-based BLO algorithms to these problems. The project will also develop a BLoG framework with multiple lower-level problems for multiple LoG tasks, named Fast-BLoG. Fast-BLoG will tackle fast and efficient semi-supervised graph neural network training. The project will highlight the advantages and new technical challenges of using the BLoG framework for handling machine learning tasks over graphs. 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

About 85% of the mass in the Universe is thought to be dark matter. Dark matter is theoretically made up of particles that have mass, but can pass right through ordinary matter, like the Earth. These particles are thought to exist because they are needed to explain the motions of stars and galaxies. These visible objects are moving as if being tugged by gravitational forces of some unseen masses. However, dark matter has never been directly detected. The investigator will observe small galaxies that are close to our own Milky Way galaxy to learn how accurately the dark matter in these small galaxies can be measured. This project supports a graduate student who will lead activities for the Flying Cloud Institute for Young Women in Science (YWIS). YWIS offers after-school science club laboratories and a summer camp for middle school girls who are interested in science. The investigator leads the MilkyWay@home project. In this project thousands of people volunteer their computers to help MilkyWay@home do research, and these citizens also follow the research developments. In addition, this project funds summer research experiences for three undergraduate students. This project aims to quantify the systematic errors in measurements of the dark matter mass of dwarf galaxy satellites of the Milky Way from observations of tidal streams. When a smaller galaxy falls into the Milky Way, our galaxy’s gravitational tidal forces pull it apart and its stars are spread along its orbit into a long “stream” of stars. The key goal is to understand the amount of dark matter in the Large Magellanic Cloud (LMC), the largest of the small galaxies that orbit around the Milky Way galaxy. Another goal is to figure out how well we can determine the amount of dark matter in a small galaxy that used to orbit around the Milky Way, but now has been ripped apart by our Galaxy’s gravity. Now, the stars from this small galaxy are spread out across the sky. While both of these measurements of dark matter mass are surprising, the systematic errors in these measurements are not well known. By studying the relationship between the assumptions made and the measurements obtained, the investigator will determine the range of dark matter masses of these Milky Way satellites that is consistent with currently available data. 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

Non-Technical Summary: Solidifying batteries with inorganic ceramic electrolytes has been considered a promising approach to improve the safety and energy density of today’s lithium-ion batteries. However, full scale commercialization of solid-state batteries is still challenging due to the difficulties in scalable synthesis and processing of solid electrolytes (SEs). Solution-based synthesis of SEs is believed to be a promising approach for large-scale synthesis of SEs but the material synthesized from this approach exhibits much lower performance compared with that synthesized by the conventional solid-state approach. This project, supported by the Ceramics Program in the Division of Materials Research, aims to combine expertise from both university and industry researchers to understand the underlying reasons for the decreased performance of solution synthesized SEs. Through the GOALI partner (Saft America), the work will contribute directly to industrial development and manufacturing of next-generation batteries for commercial and defense applications. The project will leverage the unique industrial expertise of Saft America, through technical collaboration and student internships, to further validate the research outcome and promote the development of practical processes for large-scale manufacturing of SEs. The multi-disciplinary research, involving chemistry, materials science, and computer simulation and modeling, will provide multiple opportunities for training graduate and undergraduate researchers. The project will also contribute broadly to society through outreach activities to K-12 students. Technical Summary: Supported by the CERAMICS program at the National Science Foundation, this project aims to understand: (i) the reaction pathway for solution-based synthesis of lithium thiophosphate glass-ceramic SEs, (ii) the crystallization dynamics of lithium thiophosphate glass-ceramic SEs during synthetic heat treatments, and (iii) the synthesis/processing-structure-property of solution-synthesized lithium thiophosphate SEs. Correlating the structural information with the ionic conductivity of SEs not only helps understand the effect of residual solvent, crystallinity, and disorder on the resulting ionic conductivity to identify the dominant reason for the decreased ionic conductivity, but also provides direct fundamental insights to precisely control the synthesis and processing of superior SEs at a large scale. This project also leverages the unique expertise of the GOALI partner in industrial cell manufacturing and electrochemical measurements at extreme conditions. The proposed research can help address sustainable chemistry challenges in (i) designing and developing innovative experimental and computational techniques to understand chemical reactions in solutions at an atomic scale, (ii) developing a database for the pair distribution functions of liquid-phase Li2S-P2S5 binary in various solutions, and (iii) developing precise and industrial relevant procedures for energy- and resource-efficient manufacturing of critical SE materials for clean and safe energy storage. The partnership with Saft America will provide students with internship and collaboration opportunities with scientists in a leading battery company and promote the scale-up and practical application of the solution-based synthesis and processing of SEs. 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 This R03 application aims to create innovative magnetically responsive drug delivery electrospun fibers to improve regeneration after spinal cord injury (SCI). Traumatic spinal cord injury (SCI) is a devastating condition currently affecting approximately 296,000 US citizens, with around 18,000 new cases each year. SCI occurs when a severe physical force causes compression of the spinal cord, killing neurons and glia at the injury site. Multiple secondary injury cascades are initiated immediately after the initial insult and lead to additional neuronal loss. The degree of inflammation that occurs after SCI has been shown to relate to the magnitude and duration of secondary injury. Depending on the severity of the SCI and the demographics of the patient, the inflammatory response varies after injury. Biomaterials, such as electrospun fibers, can provide local release of therapeutics to limit adverse off-target effects; however, drug-releasing biomaterials do not address the variability of patient inflammation. To provide a means of tailoring the therapeutic delivery to a patient, we propose to fabricate magnetic, growth factor-loaded coaxial electrospun fibers that can be stimulated non-invasively with a magnetic field to increase the rate of growth factor release from the fibers. Over the past several years, the Gilbert laboratory in collaboration with several other laboratories have applied superparamagnetic iron oxide nanoparticles (SPIONs) to astrocyte and neuronal cultures. More recently, our group has applied SPIONs to polymer systems, creating unique composites where magnetic fields can move aligned polymer fibers to more successfully direct extending neurites in culture. In collaboration with the Samuel laboratory, we were able to apply magnetic field stimulation to neurons cultured on scaffolds where SPIONs were tethered to fibrous scaffolds to stimulate neurite outgrowth. In this proposal, we hypothesize that the combination of SPIONs with electrospun fibers can create unique drug delivering scaffolds capable of releasing drugs at precise dosages that are tailored to a patient’s inflammatory response. Our group recently showed that the anti-inflammatory cytokine transforming growth factor beta 3 (TGFβ3) mitigates astrocyte reactivity in culture. Loading TGFβ3 into magnetic polymer fibers may allow for on-demand delivery of precise TGFβ3 dosages to mitigate astrocyte reactivity and more effectively treat an individual’s unique SCI. This project is likely to make significant contributions by developing new biomaterials capable of releasing large therapeutic molecules at precise dosages using an external magnetic field. Furthermore, approaches that focus on astrocyte phenotype may yield new areas of research where the astrocytes, not extending axons, are the focus of future treatments for SCI.

NIH Research Projects · FY 2026 · 2024-08

Research in the Zha Lab at Rensselaer Polytechnic Institute focuses on creating biomimetic materials and processes that aid in studying, diagnosing, preventing, and treating disease. By leveraging the self-assembly capabilities of rationally designed biomacromolecules, this research aims to create hierarchically structured systems that are multifunctional. This MIRA R35 for Early Stage Investigators project will support the exploration and development of new approaches for enhancing the function of biomedical and biological interfaces. The approaches are based on an interfacial phenomenon recently reported by the PI, whereby robust nanothin coatings are generated non- covalently on surfaces by controlling the self-assembly of structural proteins such as silk fibroin. These coatings can transform the physicochemical properties of a wide range of substrates under biocompatible conditions, regardless of surface chemistry or topography and without specialized equipment. The research program will explore coatings that: i) exhibit dynamic behavior mimicking the temporal complexity of cell signaling in biological processes, ii) shield surfaces against unwanted macromolecular and cellular interactions, iii) improve biocompatibility and bioactivity of cell-material interfaces, and iv) present or release bioactive payloads in a sustained, controlled manner. While the innovations developed by the research program are disease-agnostic and will have the potential to address a wide range of biomedical challenges, three proof-of-concept topics using model systems will be examined in this project. 1) Coatings with temporally orchestrated release of multiple neurotrophic factors will be developed to control Schwann cell phenotype for nerve tissue regeneration. 2) Antifouling peptide motifs will be discovered by combinatorial synthesis and machine learning to generate coatings that improve the long-term performance of implanted biosensors. 3) Surfaces of living cells will be engineered by interfacial protein self- assembly to enhance their viability and bioactivity in therapeutic applications. The outcomes of this research program are expected to broadly yield versatile, modular tools for biomaterials development.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY/ABSTRACT Bacteroidales is the most abundant order of bacteria in the human gut, yet we still know little about energy generating processes of these bacteria and how these systems support their fitness in the gut and affect host processes. We have demonstrated that Bacteroides have a complex respiratory chain that provides substantial energy during both anaerobic and nanaerobic growth. Our preliminary results reveal new and unexpected complexities of the respiratory pathway, new terminal electron acceptors that we predict contribute to bacterial and host fitness, and differences between Bacteroides species that likely impact microbiota composition. These important features and complexities of respiration in the gut Bacteroides will be addressed in three specific aims. In Aim 1, we will study the NUO complex, likely the most important generator of the proton gradient, which provides the energy for transport functions including TonB- dependent import processes such as acquisition of dietary polysaccharides. Unlike most NUO enzymes, Bacteroides has the NUO-11 variant that does not accept electrons from NADH. In this aim, we will use genetics, biochemistry, and mouse models to conclusively identify the electron donor to NUO-11, determine the importance of NUO and Na+/H+ antiporters in maintaining the essential proton gradient, and determine their contributions to bacterial fitness. In Aim 2, we will study the acquisition and remodeling of the essential respiratory component menaquinone (MK). Most Bacteroides have all the genes necessary for the de novo synthesis of MK; however, certain Bacteroides species lack the primary men genes and must obtain and remodel MK from dietary or microbial sources. This remodeling requires cleavage of the hydrophobic side chain by an unknown enzyme that also is likely necessary for the synthesis of MK-4 (Vitamin K2) by humans. We will explore unknown features of MK synthesis including identification and characterization of the enzyme that cleaves the isoprenoid chain in the remodeling process, how species without the men operon obtain MK precursors, and the dietary and/or microbiota sources of these precursors. In Aim 3, we will study the NrfHA complex, expression of which is among the most upregulated during nanaerobic growth. We predict NrfHA is an additional terminal carrier that donates electrons to both nitrite and nitric oxide (NO). We predict NrfHA protects Bacteroides against NO produced in the normal and inflamed gut. We will study the regulation of the nrfHA operon during nanaerobic growth, study the ability of NrfHA to donate electrons to both nitrite and NO, and determine if this complex allows Bacteroides to better survive in the inflamed gut with concomitant protective effects for the host. The data obtained from the experiments of this proposal will reveal several new aspects of the physiology of the Bacteroides that can be translated for human health benefits.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Nearly every aspect of cellular function—from development to metabolism and signaling—requires that membrane proteins be targeted to the correct location in the cell. This process requires high specificity as different cargoes are destined for different destinations. Although the basic steps of this process are known (i.e., cargoes are sorted into vesicles, the proper kinesins are recruited, and kinesins then transport vesicles to the proper destination), our current understanding of the mechanisms by which kinesins are recruited to vesicles and how their action is regulated on vesicles in live cells is extremely limited. Previously, the field lacked many of the tools needed to better characterize and understand these mechanisms, but our group has developed several cutting-edge tools that allow us for the first time to answer several fundamentally important questions. In this project, we will apply our experience with these tools in combination with primary hippocampal neuron culture and live cell imaging to achieve four key goals: 1) determine the mechanisms of on-vesicle regulation for kinesin- mediated transport, using Kinesin-3 family member KIF13A as an initial model, 2) identify molecular links between cargo sorting and kinesin recruitment, 3) determine the mechanisms that underlie the sorting of polarized cargoes at the trans-Golgi network, and 4) develop additional molecular tools in pursuit of these goals that also have applicability to the broader trafficking community.

NSF Awards · FY 2024 · 2024-06

Modern cyber-physical systems (CPS) are increasingly neuro-symbolic. A typical CPS control pipeline consists of 1) neural networks (NNs), used to process raw high-dimensional data, such as camera images, and 2) downstream symbolic components, such as state estimation and control, that take the NNs' output in order to close the loop. However, there is a fundamental mismatch between the uncertainty on the NN outputs and the assumptions of the downstream components. NNs are known to be vulnerable to even minor input perturbations and distribution shifts that make it hard to characterize the properties of NN outputs. In turn, such robustness issues violate the symbolic tasks' assumptions and guarantees, thus compromising the overall system safety and predictability. The project’s novelties are neuro-symbolic calibration and training methods that aim to repair the fundamental neuro-symbolic mismatch. The project's impacts are safer and more predictable CPS with NN perception across a variety of CPS domains, including transportation, agriculture, and medicine. The research would enable the application of powerful symbolic tasks (e.g., resilient state estimation and robust control) to modern perception-based CPS where the presence of NNs might otherwise violate the symbolic tasks' assumptions. On the educational front, the investigators will co-develop a graduate course on NN calibration that will expose students to the adverse effects of miscalibration in modern CPS and ways to combat it. The main innovation of this project is the formalizing of the connection between calibration, training and neuro-symbolic methods, especially in the CPS domain. There is a need for a CPS calibration framework that: 1) is robust to data artifacts such as temporary sensor faults; 2) provides calibrated outputs that are consistent with system dynamics over time; and 3) considers the assumptions of the downstream symbolic task. We provide a general framework for combining standard (neural) calibration losses with symbolic losses that aims to align the NN outputs with the assumptions of the downstream symbolic tasks. The research agenda consists of two directions: (i) extrinsic neuro-symbolic calibration to align the uncertainty in NNs with the subsequent symbolic tasks without retraining the NNs, and (ii) intrinsic neuro-symbolic training and calibration to simultaneously train and calibrate NNs for the subsequent symbolic tasks. Both directions are being applied to two broad classes of symbolic tasks, namely state estimation and control, for two general types of symbolic assumptions, i.e., probabilistic and bounded inputs. The benefits of neuro-symbolic calibration and training are being demonstrated on a 1/10-scale autonomous racing platform - the F1/10 car. In addition, the PIs will conduct outreach activities within Rensselaer Center for Open Source and Course-Based Undergraduate Research Experience (CURE) with the Center for Undergraduate Research at the University of Florida. 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 · 2024-05

Project Summary Polymorphism in the APOE gene is the leading risk modulator for late onset Alzheimer’s Disease (LOAD). Despite extensive studies, the molecular mechanisms by which Apolipoprotein E (ApoE) influences AD risk remain poorly understood. A growing body of research is pointing towards a significant role for ApoE/heparan sulfate interactions in AD pathogenesis. Heparan sulfate (HS) is ubiquitously present on neuronal and glial surface and facilitates ApoE cell surface binding and cellular uptake. In our recent studies, we have shown that ApoE/HS binding affinity correlates with AD risk and that ApoE recognizes a rare HS modification, 3-O-sulfation. In addition, we demonstrate an increase in the 3-O-sulfated HS in AD brains. In preliminary data, we show that ApoE4 allele further increases the level of 3-O-sulfated HS, leading to differential interaction between ApoE isoforms and HS in the brain. We hypothesize that the differential binding modes between ApoE isoforms and HS contribute to AD risk. This hypothesis will be tested on multiple levels using an interdisciplinary approach in three aims. Aim 1, Delineate the glycan determinants of ApoE-HS interaction in AD. Aim 2, Define isoform specific ApoE-HS interaction at atomic resolution. Aim 3, Study isoform specific ApoE-HS interaction in cellular and animal models. Successful completion of this project will provide novel insights into how ApoE isoforms modulate AD risk through their distinct interactions with HS, and will identify new drug targets for AD.

NIH Research Projects · FY 2025 · 2023-09

Project Summary/Abstract The overarching goal of this Training Program for predoctoral students based at the at the Rensselaer Polytechnic Institute (RPI) is to provide cross-disciplinary training for future innovative leaders in translational ADRD research. Our training will emphasize the application of data science and engineering principles to ADRD biology and translational research through interdisciplinary research projects and coursework. The proposed Training Program will provide trainees with a keen understanding of the interdisciplinary and translational nature of ADRD research, which is firmly based on fundamental research in biological sciences, data science and engineering, leading to innovative new scientific disciplines and technologies, novel AD drug development, and translation of discoveries to the clinic. Training faculty are actively involved in research programs spanning: 1) data science as applied to ADRD, including medical imaging; 2) pharmacologic interventions for ADRD; and 3) structural biology of protein folding/tau. The Alzheimer’s Disease Research Center (ADRC) at Icahn School of Medicine at Mount Sinai (ISMMS) will be our clinical training partner through a subcontract and will provide ample access to educational and research opportunities to our trainees. Key aspects of the Training Program include: [1] a set of five courses to maximize didactic training among the key disciplines, including a new course entitled “Data Science and ADRD” and summer bootcamp “Data Science and ADRD: Hands-on Training and Research Challenge”. Development and launch of the new data science course and summer bootcamp have been funded by a supplement to the current NIA T32, with their inaugural runs planned for 2022; [2] a core course entitled “Perspectives in Alzheimer’s Disease Research” that is taken by all trainees and open to other RPI students; [3] a seminar series (Frontiers in Alzheimer’s Research) and related research symposia that builds on campus-wide programs at RPI and clinical and industry partners; [4] a student-run seminar program, in which trainees present their research to their peers and training faculty; [5] training in responsible conduct of research; [6] Co-advising and multidisciplinary PhD thesis committee with ADRD expertise; [7] translational and clinical internship at Icahn School of Medicine at Mount Sinai (ISMMS) and industrial partners; [8] poster presentation and participation at annual meetings of the Rensselaer Center for Biotechnology and Interdisciplinary Studies (CBIS) and Institute for Data Exploration and Applications (IDEA); [9] entrepreneurship and commercial translation training; and [10] an annual retreat in contemporary research in AD and related dementias at which trainees and members of the larger campus community interact with prominent researchers in the field.

NIH Research Projects · FY 2025 · 2023-08

The regulation of cell-cell junctions is essential for the biological functions of various tissues such as epithelium and endothelium. Recent evidence in embryonic development and vascular physiology suggests that cell-cell junctions are regulated by cell chirality, a universal but fundamental property of the cell. We pioneer in research in cell chirality using engineered in vitro platforms. Here, using these platforms, we are to investigate the biophysical mechanism associated with chirality at cell-cell junctions. While the principle may be shared among many cell types, this study will focus on endothelial cells and the regulation of vascular permeability. The endothelial cell layer is a semi-permeable barrier that tightly controls the passage of proteins and cells in the bloodstream into the interstitial space and regulates the local environment of biological tissues in living organisms. Cells achieve this vital function primarily through mediating paracellular transport by controlling the opening and closure of cell-cell junctions. Protein Kinase C (PKC) activation has been associated with endothelial dysfunction in chronic conditions such as diabetes and long-term smoking as well as acute diseases such as sepsis, acute lung injury, and viral infection. Restoring and maintaining vascular integrity is critical for body function and patient survival, especially for acute diseases. Recently, we have demonstrated that PKC can reverse cell chirality, which mediates endothelial permeability. However, little is known about the molecular mechanism of how PKC activation reverses endothelial chirality or that of how cell chirality alters endothelial permeability. In this proposal, we hypothesize that PKC reverses cell chirality by reducing the level of actin crosslinking and that cell chirality regulates cell-cell junctions (and therefore endothelial permeability) biomechanically through actin tilting and VE-cadherin localization. We will pursue the following three aims: Aim 1. Identify the timing and location of biomechanical asymmetry responsible for multicellular chiral morphogenesis using traction force microscopy (TFM). Combing 2D micropatterning for cell chirality and TFM for cellular forces, we are to study in great detail of 2D collective symmetry breaking and to interrogate underlying cellular biomechanical mechanisms. Aim 2. Determine cytoskeletal mechanisms underlying PKC induced reversal of endothelial cell chirality. We will identify formin isoforms and actin crosslinkers involved in this process, and their regulation by PKC signaling. Aim 3. Investigate the role of chirality mismatch in the intercellular gap formation and endothelial permeability. We will quantify actin structure and dynamics during the intercellular junctions and examine how the mismatch of cell chirality can lead to actin remodeling and induce intercellular gap formation. If successful, we will be able to identify the biophysical mechanisms, allowing for the potential development of novel, specific therapies based on cell chirality for endothelial dysfunction. With data obtained from this proposal, we will seek further support and examine our findings with animal models.

NIH Research Projects · FY 2026 · 2023-06

Abstract Quantification of drug-target engagement is recognized as the most crucial parameter in the drug development pipeline as it is central to therapeutic action. Though, such parameter can only be assessed via invasive biochemical and immunohistochemical (IHC) approaches in ex vivo tissues. Herein, we propose to integrate and optimize a multimodal optical imaging platform that can provide direct longitudinal (multiple time points) measurements of the drug-target engagement distribution across the same tissue volume in correlation with drug delivery efficacy parameters, including, tumor vasculature, and indicators of drug response, such as metabolism. The imaging platform will be validated in human breast tumor and patient derived xenografts in live animals subjected to HER2-trastuzumab therapy. Additionally, as MFMT is an indirect image formation technique relying on complex computational tasks, we will further pioneer the use of Deep Learning methodologies for fast, accurate, parameter-free and user friendly 2D and 3D MFMT image formation.

NIH Research Projects · FY 2026 · 2023-05

Project Summary Cells perform mechanical tasks across a wide range of processes including segregating chromosomes during cell division. These tasks are accomplished by the organization of force-generating cytoskeletal networks. Micron-scale microtubule networks need both motor and non-motor proteins to move and organize filaments into proper functional mechanical units. Our long-term goal is to decipher the mechanical code that underlies the assembly and function of these networks, using mitosis as a model biological process. To achieve this goal, we will employ biochemical reconstitution, biophysical methods, single-molecule fluorescence microscopy, and live- cell imaging. We will build on our recent publications and unpublished preliminary data to focus on microtubule network mechanics in mitosis in the following three Aims: (1) Determine the mechanical and functional differences between bridging fibers in metaphase and the central spindle microtubule network in anaphase. Specifically, we will dissect the molecular mechanisms of an essential crosslinking non-motor MAP, PRC1, that builds distinct motifs within the mitotic spindle. These features include bridging fibers that connect sister kinetochore fibers in metaphase and the central spindle midzone array in anaphase. PRC1 is cell cycle regulated by CDK/cyclin B, and therefore is a biochemically distinct molecule in metaphase and anaphase. We will assemble and mechanically probe filament networks to understand how the spindle is able to differentially generate forces and remodel itself while moving chromosomes in metaphase and anaphase. Imaging live cells during mitosis that express mutant PRC1 constructs will validate our in vitro findings. (2) Determine the molecular mechanisms for MAP clustering and the functional role of MAP clusters in regulating microtubule organization. Specially, we will examine how intrinsically disordered subdomains within PRC1 contribute to MAP clustering. Our published and preliminary data suggests that PRC1 clusters significantly impede filament sliding, and that the C-terminal unstructured domain mediates this effect. We will employ our biophysical and cell biological tools to determine the effect that reducing clustering has on microtubule organization. (3) Determine how complexes of motor and non-motor MAPs collectively regulate microtubule organization. We will examine how the Kif4A/PRC1 complex generates forces during microtubule sliding, and how a steady-state overlap arrangement produces resistive forces that maintain spindle midzone integrity. Together, our findings should advance our understanding of how micron-scale microtubule networks regulate chromosome motions in mitosis. We aim to elucidate a ‘code’ that defines how the structure and biochemistry of different MAPs gives rise to cellular machinery that can perform mechanical work. Errors in microtubule network assembly due to copy number variations or mutations in essential MAPs are linked to disease in humans. Our research will shed light on the biophysical properties that link network failure to disease states and may lead to therapies that target these proteins or provide insights into diagnostic tools for assessing disease progression.

NIH Research Projects · FY 2024 · 2023-05

The human body appears left-right (LR) symmetric, but the shape and positioning of internal organs are distinct at two sides. Defects in laterality such as isomerism (loss of asymmetry), and heterotaxia (a loss of concordance among the individual organs) are observed in more than 1 in 8000 live births, contribute to pre-term births and miscarriage, and have significant clinical implications. Our lab has pioneered in the research of the cellular LR asymmetry using novel in vitro microscale devices and has extensive experience in modeling organ asymmetries such as cardiac c-looping. We would like to extend our research into studying the overall LR asymmetric body plan, which is determined by a select group of cells in embryonic development, first identified in Xenopus as the Spemann-Mangold organizing center. Since then, many studies have explored the functionality of a left-right organizer (LRO) in various vertebrates, in particular, chick, fish, and mouse. Due to ethical concerns and the 14- day restriction of culturing human embryos in vitro, the ability of researchers to study the formation of a human organizer is very limited. Therefore, finding a biomimetic surrogate of the human organizer will be of great interest to basic science and health care. Recent rapid scientific advances in basic stem cell biology and organoid engineering have made it possible to engineer a human organizer for studying LR symmetry breaking. Scientists have demonstrated that human embryonic stem cells (hESCs) can be induced to express known organizer markers, including the Goosecoid (GSC), with either the culture of embryoid bodies or the patterning of hESCs on 2D micropatterned circles. GSC is a key organizer marker of LRO known to be conserved across several vertebrate species. The major challenge now is how we can engineer the cells into highly organized and naturally curved cell sheets with planar polarization and even with specific localization, structure, and motion of cilia so that the organizer can fulfill its critical function in symmetry breaking. As a team of well-trained bioengineers and development biologists with experience and expertise in stem cell biology and LR asymmetry, we are well-equipped to address this problem. We propose to develop a novel in vitro human organizer model that will utilize organizer differentiation protocols, a geometrically-control 3D hydrogel culture system, and a stable gradient generator for developmental morphogens to facilitate the differentiation and structural formation of a human organizer. We will further study the role of the cellular intrinsic bias, termed cell chirality, in planar cellular polarity (PCP) signaling and its regulation of the human LRO morphogenesis. Overall, the proposed study is timely in addressing a very fundamental yet fascinating question regarding the developmental LR asymmetry. We will not only establish an in vitro 3D platform for studying the human LRO, but also reveal biophysical mechanisms of PCP and chirality in realizing the critical function of LRO. It will pave the way towards the further development of screening platforms for teratogens and prenatal drugs.

NIH Research Projects · FY 2026 · 2022-08

Abstract Bone fractures contribute significantly to healthcare cost affecting the quality of life. Clinically, fracture risk can be predicted by dual x-ray absorptiometry (DXA) or the fracture risk assessment (FRAX) tool. Because type 2 diabetes (T2D) patients exhibit high bone mineral density (BMD), both tools fail to correctly predict fracture risk, leading to a significant increase of fragility fractures in diabetic subjects. Therefore, there is a need to investigate how modifications in collagen and other organic components in bone can predict diabetic fractures. Pentosidine (PEN), a fluorescent Advanced Glycation Endproduct (AGE) that forms in bone by reaction between sugars and proteins, is the only established marker of bone fragility. However, it does not consistently predict T2D and fragility fractures. Here, for the first time in bone, we demonstrate the presence of carboxymethyl-lysine (CML), a non-fluorescent glycoxidative AGE, and present a technique to measure it. We show that it forms in abundance in bone and is highly correlated to loss of bone toughness. We demonstrate that, in contrast to other AGEs, CML is upregulated >60% in T2D human bone compared to their age-matched controls. We then provide evidence that CML promotes formation and growth of additional hydroxyapatite (HA) crystals, similar to human T2D condition, and forms a ‘molecular link’ between the organic and inorganic components of bone (collagen-HA interface) impairing bone quality. Our overall hypothesis is that CML is a ‘new and relevant’ biomarker of T2D fracture that captures the effects of both hyperglycemia and oxidative stress in bone and explains the susceptibility of bone to fracture in T2D. Using an obese and a non-obese mouse model of T2D, that mimic both causality and impact of human T2D on bone, we provide evidence that T2D increases AGEs, with CML explaining bone fragility. Similarly, using data from the Health, Aging and Body Composition (ABC) study we show that higher serum CML levels are associated with increased risk of incident clinical fractures in T2D, independent of BMD. Thus, our overall goal is to establish CML as a new and relevant biomarker of bone fragility and determine how it contributes to bone fragility in T2D. Using in vitro models, ex vivo human cadaveric tissue, in vivo mouse models of obese and non-obese T2D, and existing data from the Health ABC study we will pursue there aims: Aim 1: Establish NEG conditions for enhanced formation of CML over other AGEs and determine the mechanism(s) by which it reduces energy dissipation in bone; Aim 2: Determine the contribution of CML and other AGEs to alterations in bone matrix and energy dissipation in human T2D vertebrae and cortical and cancellous bone from hip fracture patients; and Aim 3: Validate CML as a biomarker of T2D bone fragility and establish its association with hyperglycemia and oxidative stress. Our findings will provide a new understanding of the mechanism and the effects of CML and other AGEs on bone fractures leading to new strategies to predict, manage and mitigate T2D and fragility fractures.

NIH Research Projects · FY 2025 · 2022-04

Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging ABSTRACT Over the past several years, artificial intelligence (AI) and machine learning (ML), especially deep learning (DL), has been the most prominent direction of tomographic research, commercial development, clinical translation, and FDA evaluation. Recently, it has become widely recognized that deep neural networks often have generalizability issues and are vulnerable to adversarial attacks, deliberate or unintentional. This critical challenge must be addressed to optimize the performance of deep neural networks in medical applications. In January this year, FDA published an action plan for furthering the oversight for AI/DL-based software as medical devices (SaMDs). One major action underlined in the plan is “regulatory science methods related to algorithm bias and robustness”. The significance of ensuring the safety and effectiveness of AI/DL-based SaMDs cannot be overestimated since AI is expected to play a critical role in the future of medicine. In this context, the overall goal of this academic-FDA partnership R01 project is to generate diverse training and challenging testing datasets of low-dose CT (LDCT) scans, prototype a virtual CT workflow, and establish an evaluation methodology for AI-based imaging products to support FDA marketing authorization. The technical innovation lies in cutting-edge DL methods empowered by (a) adversarial learning to generate anatomically and pathologically representative features in the human chest; (b) adversarial attacking to probe the virtual CT workflow in individual steps and its entirety; and (c) systematic evaluation methods to better characterize and predict the clinical performance of AI-based imaging products. In contrast to other CT simulation pipelines, our Adversarially Based CT (ABC) platform relies on adversarial learning to ensure diversity and realism of the simulated data and images and improve the generalizability of deep networks, and utilizes adversarial samples to probe the ABC workflow to address the robustness of deep networks. The overarching hypothesis is that adversarial learning and attacking methods are powerful to deliver high- quality datasets for AI-based imaging research and performance evaluation. The specific aims are: (1) diverse patient modeling (SBU), (2) virtual CT scanning (UTSW), (3) deep CT imaging (RPI), (4) virtual workflow validation (FDA), and (5) ABC system dissemination (RPI-SBU-UTSW-FDA). In this project, generative adversarial learning will play an instrumental role in generating features of clinical semantics. Also, adversarial samples will be produced in both sinogram and image domains. In these complementary ways, AI-based imaging products can be efficiently evaluated for not only accuracy but also generalizability and robustness. Upon completion, our ABC workflow/platform will be made publicly available and readily extendable to other imaging modalities and other diseases. This ABC system will be shared through the FDA’s Catalog of Regulatory Science Tools, and uniquely well positioned to greatly facilitate the development, assessment and translation of emerging AI-based imaging products.

NIH Research Projects · FY 2024 · 2021-07

Defects in laterality are observed in more than 1 in 8000 live births and have significant clinical implications. The embryonic heart starts as a straight cardiac tube along the midline of the embryo, which is subsequently transformed into a c-shaped heart loop reliably toward the right side of the body. This cardiac c-looping is considered as the earliest evident event of left-right (LR) asymmetry breaking (also called chirality or handedness) of a human organ. The inversed lateralization of cardiac looping often leads to severe clinical outcomes, including dextrocardia, septum defects, double outlet right ventricle, and even death of fetuses and infants. Accumulating evidence suggests that asymmetric cardiac looping derives from an unknown tissue- intrinsic mechanism. Recently, we have recapitulated chiral morphogenesis on micropatterned surfaces and in 3D hydrogels and demonstrated that cardiac cells have a definite chirality before asymmetric looping. Protein kinase C (PKC) activators can reverse both cell chirality and cardiac c looping. Our rationale is that novel cell chirality based high-throughput platforms, together with a better understanding of molecular mechanisms of cell chirality, can facilitate the LR symmetry research. We propose to use a combination of micro-fabrication, hydrogel technology, live-cell imaging, molecular assays, traction force microscopy, high-throughput screening, ex vivo culture, and genetic mouse models as tools to elucidate the biophysical and biochemical mechanisms. Our objectives are to determine biomolecular and biomechanical mechanisms of PKC regulated cell chirality and asymmetric looping and to identify cytoskeletal mechanisms of cell chirality during cardiac c-looping. SPECIFIC AIM 1: Identify components and signaling pathways that regulate cardiac chirality with high- throughput screening and validate with ex ovo embryo culture. We will screen inhibitors/activators of PKC isoforms, their downstream effectors, possible substrates, and a small-molecule kinase library, determine mechanisms of action, and validate the findings with the whole-embryo ex ovo culture. SPECIFIC AIM 2: Determine the biomechanical role of cell chirality in multicellular morphogenesis. We will examine whether chiral mechanical forces are sufficient to induce cardiac c-looping using traction force microscopy and whether the cells on ventral myocardium exhibit intrinsic chiral biases. SPECIFIC AIM 3: Determine cytoskeletal mechanisms in cardiac cell chirality during c-looping. We will analyze the chirality of actin dynamics of cardiac cells, observe its change under drugs of interest, and confirm the findings with ex ovo whole-embryo culture and genetic mouse models. If the project is successful, we will be able to establish a set of novel high-throughput platforms for studying the biophysics of asymmetric cardiac looping by measuring cell chirality, and further our understanding of congenital heart disease. Also, this proposed research is transformative, and potentially open a new field of research: cell chirality, a fundamental cellular property defining symmetry breaking in tissue development.