Grants

DEPT OF DEFENSE.DEFENSE LOGISTICS AGENCY.DCSO COLUMBUS DIVISION #3

DLA-SM Broad Agency Announcement

OD - NIH Office of the Director

PROJECT SUMMARY Down syndrome (DS), with triplication of chromosome 21, is recognized as one of the most important leukemia-predisposing syndromes. 1-2% of DS children develop myeloid leukemia (DS-ML) before age 5, which is preceded by a pre-leukemic phase termed transient abnormal myelopoiesis (TAM). 30% of TAM cases progressing to DS-ML. Aside from low dose chemotherapy, there are no treatment options for TAM and no preventative measures to stall DS-ML onset. Nearly 10-15% of children with DS-ML are either refractory to treatment or suffer early relapse. These children with refractory disease face a dismal outcome with 3-year event-free survival less than 21%. Moreover, treatment-related toxicity and morbidity is a major cause of death in DS-ML patients. Therefore, novel therapeutic options are needed for this rare disease. Both TAM and DS- ML are characterized by the pathognomonic mutation in the gene encoding essential hematopoietic transcription factor GATA1, resulting in N-terminally truncated mutant GATA1 protein (GATA1s). Trisomy 21 and GATA1s are sufficient to induce TAM, while additional co-operating mutations in genes such as STAG2 are required for DS-ML leukemogenesis. The individual and synergistic contribution of these genetic events towards DS-ML refractoriness and remodeling the bone marrow microenvironment remain poorly defined. Using CRISPR/Cas9 mediated gene targeting for stepwise introduction of GATA1 or GATA1 and STAG2 mutations in iPSCs with trisomy 21, we modelled TAM and DS-ML respectively. We generated patient-derived xenograft (PDX) models representing refractory DS-ML. Our transcriptome analysis to identify novel DS-ML specific cell surface proteins revealed Delta-like non-canonical Notch ligand 1 (DLK1) as one of the top targets. DLK1 transcript was undetectable in normal bone marrow specimens, while it was more abundant in DS-ML compared to TAM. Enhanced expression of DLK1 was also detected in our iPSC-generated DS-ML models, but not in TAM models. Our preliminary data shows that DLK1 knockout in DS-ML cell line suppressed proliferation and delayed engraftment. Furthermore, targeting DLK1 via an immuno-conjugate significantly reduced leukemic burden and prolonged survival in refractory DS-ML PDX lines. These results indicate that DLK1 may promote DS-ML leukemogenesis and can be used as a therapeutic target. Consistent with the effect of DLK1 on the inhibition of interferon signaling via suppression of NOTCH1 activity, we identified that downregulation of DLK1 resulted in increased interferon signaling. Interestingly, we demonstrated that GATA1s suppressed interferon signaling via downregulation of the RIG-I pathway in iPSC-derived TAM and DS-ML models. Exogenous treatment of DS-ML cells with interferon-a resurrected interferon signaling, confirming that the pathway is intact. Thus, suppression of interferon signaling via GATA1s and/or DLK1 is a DS-ML dependency that can be exploited therapeutically. Upon study completion, we will identify novel treatment options for TAM/DS-ML, and a newly characterized target that may also be exploited in non-DS leukemia.

NINDS - National Institute of Neurological Disorders and Stroke

PROJECT SUMMARY / ABSTRACT Neurofibromatosis type 1 (NF1) is one of the most common cancer predisposition syndromes, affecting approximately 1 in 2500 individuals worldwide. It is caused by pathogenic variants or deletions of the NF1 tumor suppressor gene resulting in Ras pathway hyperactivation. A hallmark feature of NF1 is the development of histologically benign peripheral nerve sheath tumors (PNST) called plexiform neurofibromas (PNF). While benign, the lifetime risk of these pre-existing neurofibroma undergoing malignant transformation to a highly aggressive sarcoma called a malignant peripheral nerve sheath tumor (MPNST) is 8-16%. The progression of PNF to MPNST typically occurs with little to no warning and clinical features concerning for malignancy often manifest after transformation has already occurred. The development of MPNST from pre-existing neurofibroma often proceeds through intermediate lesions known as atypical neurofibromatous neoplasms with unknown biological potential (ANNUBP). However, not all neurofibroma will progress to MPNST and there are currently no clinically validated biomarkers capable of accurately identifying lesions at a high risk of undergoing malignant transformation. Risk assessment and therapeutic stratification of NF1-PNST is associated with several challenges as these tumors can exhibit significant intratumoral heterogeneity and diverse trajectories. During biopsy, sampling bias and the location of tumors in anatomically complex regions can hinder capture of the full spectrum of histopathological features. Current imaging methodologies also have limitations. On both MRI and FDG-PET scan, it challenging to definitively distinguish between benign and malignant tumors, particularly in cases of intermediary lesions or those actively transforming. These challenges underscore the need for diagnostic tools. We have identified DLK1 as a biomarker that holds potential promise in the setting of NF1-PNST. In collaboration with the Indiana Biosciences Research Institute (IBRI) and the Indiana Institute for Biomedical Imaging Sciences (IIBIS), we propose to build upon this work by undertaking a series of experiments to comprehensively elucidate the role of DLK1 in MPNST tumorigenesis and therapeutic sensitivity. We will extend these studies by developing and validating a novel DLK1-targeted diagnostic tools comprised of tissue- and molecular imaging-based approaches. Our overarching goal for the studies proposed in this application is to increase mechanistic DLK1 facilitated tumor progression and the development of clinical-grade DLK1- targeted diagnostic tools that can proceed immediately to clinical trial for further evaluation.

NYC Department of Cultural Affairs

DMF Youth, Inc.

NSF

With the support of the Division of Chemistry, the Office of Strategic Initiatives, and the Division of Materials Research, Prof. Ying Diao and collaborators at the University of Illinois at Urbana-Champaign and the University of Washington will develop novel approaches to imparting materials with the ability to control the electron spin. This project will focus on unveiling fundamental design rules for chiral emergence from achiral semiconducting polymers. By leveraging the ability of chiral structures to control the electronic spin, the research team aims to modulate chemical reaction pathways to increase the efficiency of electron transfer and energy transduction. The fundamental knowledge to be gained from this research will inform future design of electronics and redox active materials critical for advancing semiconductor technologies. The multidisciplinary research training of students and the educational activities aim to increase the STEM workforce. The educational activities through the high school summer camp and the Electrochemical Bootcamp will be on the topics of chirality, self-driven labs, directed assembly, polymer sciences and electronics, which are aligned with the research program. This research project seeks to revolutionize electronic and energy materials by introducing supramolecular chirality and thus imparting materials with the ability to control the electronic spin. This will be achieved through merging distinct areas of research: semiconducting polymers, chirality-induced spin selectivity, and electrocatalysis, which represent three separate topics with minimal overlaps to date. This project will pursue a two-pronged approach complementing hypothesis-driven discoveries (Aim 1) with data-driven autonomous experimentation (Aim 2) to discover chiral emergence from achiral high performance redox active conducting polymers. This approach will circumvent the complex synthetic challenge for inducing chirality while also meeting the demanding requirements on electronic and redox properties. This research will test the hypothesis that spin momentum control conferred by chiral structures can serve to precisely and dynamically modulate chemical reaction pathways. The research team will demonstrate this concept through an example of green hydrogen peroxide production through electrocatalysis (Aim 3). 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

Synthetic membranes facilitate the selective separation of specific molecules from mixtures. For example, membranes enable the production of clean water and safe pharmaceuticals. Unfortunately, the industrial manufacturing process used to produce membranes relies on the use of toxic solvents. In contrast, biological membranes leverage components that self-assemble to create multi-scale and hierarchical barriers that maintain unprecedented selectivity, controlling what and when molecules enter and exit cells. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to harness biology-like performance and the chemical versatility of synthetic polymers. This looks to be achieved by mixing lipids, which are the building blocks of biological membranes, with charged polymers that can be synthesized to exhibit specific chemistries or charge patterns. These mixtures will spontaneously form nanoscale, ordered structures, that can form the basis for high-precision membranes. A major challenge is to design both lipids and polymers, which can have countless variations of chemical and physical features, to yield membranes for a given application. This project aligns with DMREF and the Materials Genome Initiative by combining materials synthesis and characterization with multi-scale molecular simulation and machine learning as the experiments will inform new computational models, which will then be used to expedite materials discovery to design new membranes. This interdisciplinary effort will bring together academic researchers and scientists from the Air Force Research Laboratory (AFRL) who have combined expertise regarding the making and characterization of membranes, molecular simulation and machine learning, and the physics of lipid assembly. The effort looks to harness nanoscale structure to achieve separations capabilities seen in biology, which will benefit society and the US by establishing a versatile class of membranes with broad applications in biological, chemical, agricultural, and industrial separations. The research will also involve the interdisciplinary training of researchers with broad expertise spanning chemistry, engineering, and physics, via both student mentorship and educational outreach to K-12 students. This project seeks to establish rational design of co-assembling lipids and polyelectrolytes, using patterned polyelectrolytes and judicious choice of chemical features to target specific nanostructures that can be used in separation membranes. This effort looks to harness expertise in polymer and lipid characterization and synthesis, using sequence-defined polyelectrolytes to modulate nano-scale assembly that will be evaluated by scattering. This will be coupled with a multi-scale modeling effort that connects atomistic simulations with coarse-grained models and polymer field theory to yield predictions of charge-driven assembly. Physical insights from this combined experimental and modeling approach intend to inform machine learning tools to predict structures relevant for separation membranes, which will be tested experimentally. The overarching goal is to establish a versatile molecular design protocol capable of integrating bioinspired lipid-based assemblies with complex-forming polymers to rationally engineer permeable membranes with desired selectivity. 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

Synthetic membranes facilitate the selective separation of specific molecules from mixtures. For example, membranes enable the production of clean water and safe pharmaceuticals. Unfortunately, the industrial manufacturing process used to produce membranes relies on the use of toxic solvents. In contrast, biological membranes leverage components that self-assemble to create multi-scale and hierarchical barriers that maintain unprecedented selectivity, controlling what and when molecules enter and exit cells. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to harness biology-like performance and the chemical versatility of synthetic polymers. This looks to be achieved by mixing lipids, which are the building blocks of biological membranes, with charged polymers that can be synthesized to exhibit specific chemistries or charge patterns. These mixtures will spontaneously form nanoscale, ordered structures, that can form the basis for high-precision membranes. A major challenge is to design both lipids and polymers, which can have countless variations of chemical and physical features, to yield membranes for a given application. This project aligns with DMREF and the Materials Genome Initiative by combining materials synthesis and characterization with multi-scale molecular simulation and machine learning as the experiments will inform new computational models, which will then be used to expedite materials discovery to design new membranes. This interdisciplinary effort will bring together academic researchers and scientists from the Air Force Research Laboratory (AFRL) who have combined expertise regarding the making and characterization of membranes, molecular simulation and machine learning, and the physics of lipid assembly. The effort looks to harness nanoscale structure to achieve separations capabilities seen in biology, which will benefit society and the US by establishing a versatile class of membranes with broad applications in biological, chemical, agricultural, and industrial separations. The research will also involve the interdisciplinary training of researchers with broad expertise spanning chemistry, engineering, and physics, via both student mentorship and educational outreach to K-12 students. This project seeks to establish rational design of co-assembling lipids and polyelectrolytes, using patterned polyelectrolytes and judicious choice of chemical features to target specific nanostructures that can be used in separation membranes. This effort looks to harness expertise in polymer and lipid characterization and synthesis, using sequence-defined polyelectrolytes to modulate nano-scale assembly that will be evaluated by scattering. This will be coupled with a multi-scale modeling effort that connects atomistic simulations with coarse-grained models and polymer field theory to yield predictions of charge-driven assembly. Physical insights from this combined experimental and modeling approach intend to inform machine learning tools to predict structures relevant for separation membranes, which will be tested experimentally. The overarching goal is to establish a versatile molecular design protocol capable of integrating bioinspired lipid-based assemblies with complex-forming polymers to rationally engineer permeable membranes with desired selectivity. 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

Synthetic membranes facilitate the selective separation of specific molecules from mixtures. For example, membranes enable the production of clean water and safe pharmaceuticals. Unfortunately, the industrial manufacturing process used to produce membranes relies on the use of toxic solvents. In contrast, biological membranes leverage components that self-assemble to create multi-scale and hierarchical barriers that maintain unprecedented selectivity, controlling what and when molecules enter and exit cells. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to harness biology-like performance and the chemical versatility of synthetic polymers. This looks to be achieved by mixing lipids, which are the building blocks of biological membranes, with charged polymers that can be synthesized to exhibit specific chemistries or charge patterns. These mixtures will spontaneously form nanoscale, ordered structures, that can form the basis for high-precision membranes. A major challenge is to design both lipids and polymers, which can have countless variations of chemical and physical features, to yield membranes for a given application. This project aligns with DMREF and the Materials Genome Initiative by combining materials synthesis and characterization with multi-scale molecular simulation and machine learning as the experiments will inform new computational models, which will then be used to expedite materials discovery to design new membranes. This interdisciplinary effort will bring together academic researchers and scientists from the Air Force Research Laboratory (AFRL) who have combined expertise regarding the making and characterization of membranes, molecular simulation and machine learning, and the physics of lipid assembly. The effort looks to harness nanoscale structure to achieve separations capabilities seen in biology, which will benefit society and the US by establishing a versatile class of membranes with broad applications in biological, chemical, agricultural, and industrial separations. The research will also involve the interdisciplinary training of researchers with broad expertise spanning chemistry, engineering, and physics, via both student mentorship and educational outreach to K-12 students. This project seeks to establish rational design of co-assembling lipids and polyelectrolytes, using patterned polyelectrolytes and judicious choice of chemical features to target specific nanostructures that can be used in separation membranes. This effort looks to harness expertise in polymer and lipid characterization and synthesis, using sequence-defined polyelectrolytes to modulate nano-scale assembly that will be evaluated by scattering. This will be coupled with a multi-scale modeling effort that connects atomistic simulations with coarse-grained models and polymer field theory to yield predictions of charge-driven assembly. Physical insights from this combined experimental and modeling approach intend to inform machine learning tools to predict structures relevant for separation membranes, which will be tested experimentally. The overarching goal is to establish a versatile molecular design protocol capable of integrating bioinspired lipid-based assemblies with complex-forming polymers to rationally engineer permeable membranes with desired selectivity. 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

Non-technical Description: The development of transformative, next-generation quantum devices will require breakthrough innovation in molecular materials. Nature has evolved the sophisticated ability to organize chromophores and pigments to harness quantum mechanical properties for powerful capabilities, including solar energy harvesting, nanoscale energy transport, and energy conversion. Replicating these quantum capabilities of nature using synthetic materials remains a significant goal of rational materials design and fabrication. To overcome the bottlenecks that are intrinsic to modern materials design efforts, including costly and time-consuming synthesis and experimental characterization, the project will develop a machine learning-guided, high-throughput in silico screening platform to accelerate the discovery of functional chromophore- and qubit-DNA assemblies. The research team seeks to achieve predictive control over electronic state evolution in molecular systems organized using designer DNA assemblies. Realizing this control will enable the design of revolutionary materials and devices for various applications in quantum science and technology, including next-generation photovoltaic coatings, quantum sensors, quantum simulators, and biological imaging agents. Technical Description: DNA origami has the ability to precisely position small molecule geometries and controllably modify their local molecular environments. This ability can be leveraged to endow materials with tailored electronic properties. The project aims at designing nanomaterials that (1) extend the lifetime of optical excitations for application to solar energy conversion and storage and (2) protect and maintain coherence in molecular spin-center arrays for application to quantum sensing and computing. The originality in the research approach lies in the fact that this leverage will be used to explicitly and independently target and manipulate the properties of the molecular system’s environment (i.e., bath and system-bath interactions) to protect and enhance quantum properties. The project will develop an open-source platform for computational high-throughput screening of DNA-small molecular hybrid nanostructures that can be used to accelerate the development of materials with novel and controllable electronic properties. The project will provide cross-disciplinary training for graduate and undergraduate students to learn innovative methods that span theory, computation, DNA synthesis, and spectroscopic characterization. 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

Non-technical abstract: This project will accelerate the discovery of new superconducting materials through a transformative approach that combines artificial intelligence (AI), quantum theory, and experimental synthesis. Superconductors are essential for technologies ranging from MRI systems and high-field magnets to quantum computing and sustainable energy. Yet analysis of known compounds suggests that only a small fraction of potential superconductors may have been discovered. This project aims to significantly expand the number of known superconductors and identify materials optimized for practical applications—specifically those with high critical temperatures and magnetic fields, ductility for wire fabrication, and three-dimensional electronic structures for enhanced performance. A core educational mission will train a group of students in AI-driven materials research and develop hands-on experiment kits for K–12 classrooms to promote STEM engagement. Partnerships with national laboratories, industry, and international collaborators will ensure timely and impactful transition of discoveries to real-world applications. Technical abstract: The research integrates two complementary AI methods to accelerate superconductor discovery. Property prediction models based on graph neural networks will estimate superconducting characteristics—such as electron-phonon spectral functions, critical temperatures, and critical fields—directly from crystal structures. In parallel, generative AI models using stochastic flow matching will design novel, synthesizable materials with targeted superconducting and mechanical properties. Predictions from both models will be evaluated using density functional theory (DFT) to assess thermodynamic stability, electronic structure, and superconducting potential. Selected candidates will undergo targeted synthesis and experimental characterization using high-throughput techniques. Both AI models will be iteratively refined using experimental feedback, forming a closed discovery loop that integrates theory, simulation, and validation. This approach will yield a comprehensive, open-access dataset of successful and unsuccessful candidates, providing a foundation for future AI-guided materials discovery. 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

Non-technical description: The traditional approach to new materials discovery and optimization emphasizes perfect crystallinity, minimization of disorder, and the avoidance of complex symmetries, structures, and chemical compositions. High entropy materials, however, embrace a random multi-element solid solution distribution with large configurational entropy to dominate the enthalpy of formation, leading to optimized materials properties. This project aims to develop a new subfield, High Entropy Quantum Materials (HEQMs), to elucidate the potential of configurational entropy for expanding the landscape of quantum phenomena. Utilizing the power of configurational disorder in stabilizing exotic phases of matter, this research will explore several benchmark platforms where configurational entropy is utilized to tune magnetic, topological and correlated electron phases to establish new entropy-function relationships hitherto unrealized in a vast landscape of materials systems. In parallel, this project will have a major impact on the training of undergraduates, graduate students, and post-doctoral associates in synthesis and characterization of materials at the cutting edge of science and technology, and foster interaction of researchers from several different science and engineering disciplines at our universities and national laboratory institutions, and collaborative partnerships around the world. Bringing in both high-throughput synthesis and characterization, solid state chemistry and the computational framework of the AFLOW repository, the activities of this project will integrate experiments, computation and data-driven methods with interdisciplinary collaboration to establish the HEQM landscape as a potential new frontier for the Materials Genome Initiative. Technical description: This research addresses key questions in the new field of HEQMs, establishing a framework for a) exploring how entropy stabilizes new phases, b) identifying theoretical and experimental benchmarks, c) distinguishing high-entropy from non-high-entropy variants, and d) elucidating the role of entropy in emergent low-temperature properties. Key activities include discovery and exploration of new HEQMs using computational prediction and assisted learning techniques, and applying configurational entropy to known quantum materials systems. The focus on four example materials families amenable to high entropy configurations – spinel oxides, transition metal dichalcogenides, half-Heuslers and ThCr2Si2-type systems – will provide a template for benchmarking future directions of this approach. In parallel, establishing a dedicated repository for HEQM data will ensure that knowledge relating to synthesis activities is readily curated, stored and made accessible for analysis and mining, and generated materials data is prepared for public accessibility and application. Together, these activities will accelerate the development of HEQMs as a new avenue for discovery and optimization of quantum phenomena and applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Cement is the backbone of modern infrastructure, used in everything from buildings and roads to bridges and energy systems. Yet, its production is among the most energy-intensive industrial processes, accounting for 2-3 percent of global energy use and approximately 9 percent of human-made CO2 emissions. Traditional cement manufacturing relies on fossil-fuel-based kilns that heat materials to extreme temperatures for hours, making the process inefficient, costly and difficult to electrify. As global demand rises due to population growth and aging infrastructure, there is an urgent need for new production methods that improve energy efficiency, reduce cost and environmental impact, and maintain high performance. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project introduces flash Joule heating (FJH), an electrified process that rapidly heats raw materials to extreme temperatures in seconds—enabling fast, energy-efficient synthesis of cement clinker. Its compact, modular nature supports decentralized production, reducing transportation-related costs and emissions while enabling local use of raw materials and industrial wastes. By integrating advanced synthesis, modeling, experiments, and AI-guided optimization, this project seeks to revolutionize cement production while training the next generation of engineers and scientists in materials science, civil engineering, and artificial intelligence. This project will establish flash Joule heating as a scientifically grounded, electrified method for synthesizing cement clinker and minerals with ultrahigh energy efficiency and phase selectivity. Unlike conventional kilns, FJH applies short, high-power electrical pulses to heat raw materials above 3000 K in seconds, enabling the rapid formation of reactive clinker phases present in conventional cement clinker, such as tricalcium silicate, dicalcium silicate, and tricalcium aluminate, but at significantly lower energy cost. The research integrates thermodynamic modeling, atomistic simulations, and advanced characterization to uncover high-temperature reaction mechanisms and guide FJH process optimization. The resulting FJH clinker will be evaluated in terms of mineralogy, atomic structure, reaction kinetics and mechanisms, pore structure and engineering performance (e.g., workability, strength development, durability). These insights will inform the design of blended cements incorporating FJH clinker and supplementary cementitious materials to deliver high performance at low cost. The work will be supported by life cycle and techno-economic analyses, along with AI-driven modeling to accelerate synthesis optimization and formulation discovery. This closed-loop framework supports electrified, decentralized cement production with unprecedented energy efficiency. By tightly integrating synthesis, multiscale modeling, experimental validation, and AI-guided design, the project directly advances the goals of DMREF and the Materials Genome Initiative—accelerating materials discovery and deployment in a critical industrial sector. 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

The ability to reliably order our groceries or takeout, enjoy rapid package delivery, check the weather forecast, and navigate with GPS is all a part of the United States’ ever-growing space economy. One promising breakthrough in propulsion technology that could enable more affordable and efficient access to space is the Rotating Detonation Engine (RDE), a revolutionary engine concept now under active development. An RDE generates power through sustaining a circulating detonation wave in an annular chamber at thousands of meters per second, achieving power levels orders of magnitude higher than conventional engines, while providing higher efficiencies, more compact designs, and higher thrust-to-weight ratios. While the RDE technology is advancing rapidly, the lack of established material solutions to contend with the extreme thermomechanical loadings associated with detonation remains a critical barrier to deployment. Current testing has reported material failures after only a few engine cycles, with no clear consensus on ideal materials. Metals, composites, and ceramics are all being explored. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to tackle this materials barrier by establishing a synergistic platform informed by the Materials Genome Initiative. It will integrate industry collaboration and a partnership with the Air Force Research Laboratory to accelerate the design of high-performance copper alloys and testing protocols for the kinds of extreme dynamic conditions found in RDEs. Through a strategic combination of (i) multidisciplinary research encompassing materials science, materials informatics, mechanics, aerodynamics and combustion, (ii) organization of workshops and conferences with academic, government and industrial participation, and (iii) educational and outreach initiatives to undergraduate students and the K-12 community, this project will establish a new hub of materials discovery and design for extreme aerospace environments and help train the next-generation workforce. This DMREF project will leverage a generative AI multi-agent framework to enable closed-loop design of copper-based alloys for extreme dynamic environments, using both cold spray and directed energy deposition additive manufacturing. This framework will coordinate experimental and simulation activities through an uncertainty-responsive model, guiding material design through iterative learning. A unique aspect of the project is the development of a first-of-its-kind miniaturized RDE material testing platform, specifically designed and instrumented to rapidly screen candidate materials under realistic RDE conditions. In addition, this project will integrate reduced-order models for cyclic and high-strain-rate material performance and generate damage and failure mechanism regime maps, providing fundamental insights into how composition, microstructure, and gradation may mitigate high-frequency, high-amplitude thermomechanical loads. The resulting knowledge will not only advance copper-based alloys but also provide transferable principles for designing broader classes of structural alloys via additive manufacturing and coated materials systems for propulsion and power generation. This project is supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG), and the Division of Materials Research (DMR) of the Directorate for Mathematical and Physical Sciences (MPS). 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

A material's force-displacement response, modal response, and wave transmission and absorption response to dynamic loadings, all can be construed as its characteristic fingerprints. The behaviors of materials under dynamic loads that are applied within a fraction of a second remain poorly understood due to the complex, nonlinear interplay between material microstructure, geometry, and applied load. The complexity increases manifold for architected materials, in which topological considerations are paramount to achieve specific responses or functions. Consequently, methodical design of architected materials with optimal dynamic fingerprints is a challenge that has not been adequately addressed. By seamlessly integrating advances in graph network theory, machine learning, numerical simulations, and high-speed additive manufacturing approaches, this Designing Materials to Revolutionize and Engineer our Future (DMREF) award will accelerate the understanding, inverse design, and fabrication of architected materials with tailorable dynamic fingerprints. The outcome will be materials with inversely designed three-dimensional micro-architectures fabricated via desktop additive manufacturing with prescribed behaviors, such as impact shielding and wave transmission. Applications include energy and shock absorption, acoustic wave filtering, stretchable electronics, and other multifunctional material systems. The project will also train graduate and undergraduate students in the new paradigm of autonomous inverse design and additive manufacturing based on desired behaviors. Moreover, demonstration modules, design games, and additive printing activities will be used for outreach to K-12 students. This project will extend graph-based generative machine learning modeling techniques to identify the underlying motifs within architected materials to understand their dynamic behaviors as well as provide an inverse design framework for optimized functional responses. The first step is to develop a graph space model to represent an arbitrary architected material composed of an arbitrarily complex 3D micro-architecture, by size, scale, hierarchy, lattice topology, and material attributes. The next step involves obtaining high-fidelity experimental data and higher-order simulation data with large amounts of lower-order experimental data to accelerate the training and discovery process. A forward graph-based machine learning model will be trained on the combined data for functional response prediction. Lastly, the graph neural network with reinforcement learning will be used to generate graphs with the desired properties based on the forward predictive model. This extensive and experimentally validated framework will be used to discover fundamental knowledge pertaining to structural and dynamic characteristics, which will then be leveraged to inversely design materials with prescribed dynamic fingerprint. This project is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) in the Directorate for Engineering (ENG) and the Division of Information and Intelligent Systems (IIS) in the Directorate for Computer and Information Science and Engineering (CISE). 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

Modern civil engineering structures are designed using industry-standard packages, backed by stringent design codes. Yet, there remain frequent, costly failures caused by the unexplained dynamic interaction of crowds, notably in the lateral instability of pedestrian bridges. This project aims to provide bridge engineers with practical tools for better predicting the nonlinear effects associated with human interactions and oscillating mechanical structures. It will develop new mathematical paradigms of model reduction and data-driven dynamical systems and translate these insights into practical, quantitative design principles to avoid harmful oscillations or catastrophic bridge failures. The project will also train students in cutting-edge mathematical modeling and promote synergistic interactions with international groups of applied mathematicians and engineers in the United States and the United Kingdom. This project will contribute to the development of a new mathematical theory in a largely unexplored area of collective dynamics involving highly nonlinear agents whose individual motions can be non-smooth, with hybrid discrete-time and continuous-time elements. The project is comprised of three main objectives: (i) to understand how heterogeneity and agent-to-agent interactions influence instability thresholds in multi-agent systems coupled to a global mode; (ii) to develop and implement data-driven reduced-order modeling techniques for dynamic processes using invariance principles, such as invariant foliations and invariant manifolds; and (iii) to demonstrate the efficacy of a broader mathematical theory of emergent instability in agent-based systems due to coupling to a global environmental variable, through the development of enhanced guidelines for safe behavior in pedestrian bridges and related structures. This award by the Applied Mathematics program in the Division of Mathematical Sciences is jointly supported by the Operations Engineering as well as the Engineering Design and Systems Engineering program in the Division of Civil, Mechanical and Manufacturing Innovation, as well as the Office of International Science and Engineering. 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.

NIGMS - National Institute of General Medical Sciences

The dispersal of bacteria across a substrate underlies their ability to invade and carry harmful molecules to new environments. This depends critically on physical and chemical conditions of that environment, greatly modifying the bacteria’s behavior and impact to that substrate. In environments where fungi and bacteria live together, including the built environment and in/on our own bodies, fungal hyphae can facilitate bacterial dispersal and external transport of molecules. The long-term objectives of this research are to understand the mechanisms of bacterial dispersal and predict the transport of molecules along fungal hyphae through model building and refinement. This project will build initial models of bacterial (Lysinibacillus and Klebsiella) dispersal on fungal hyphae (Fusarium) through three specific aims: Aim 1 will quantitatively characterize the movement of bacteria over a short time scale in laboratory microcosms. The results will provide first-principle understanding of bacterial dispersal on fungal hyphae, laying the groundwork for more complex models. Aim 2 will predict how bacteria migrate and disperse along the hyphae over an expanded time scale across nutrient gradients and soil microcosms. The predictive models built from this aim is expected to better reflect bacterial migration in more complex, real-world environments. Aim 3 will test the mechanisms of bacterial movement on molecule (e.g. nutrients, harmful compounds) co-transport, using dyes and quantum dot particles as tracking sources. The results can help bring new concepts and implications of molecule co-transport through bacterial movement on fungal hyphae. This project has the potential to advance knowledge of bacterial dispersal using fungal hyphae across substrates and the functional outcomes of such dispersal. A mechanistic understanding of the properties that influence bacterial dispersal on hyphae can potentially uncover pathways of bacterial dispersal across different substrates including human skin, mucosal membranes, and across the surfaces of our ubiquitous built environment. RELEVANCE (See instructions): Bacteria and fungi co-exist in many environments in and around us. Potentially harmful bacteria can use fungi to move across our living spaces or across our own tissues and disperse harmful molecules. The findings from this project can help strategize ways to potentially reduce the movement of pathogens and toxic molecules of relevance to public health.

NSF

Computer-aided drug design (CADD), including structure-based virtual screening of a large number of available compounds (ligands) for a given drug target, has become an essential component of modern drug discovery. The actual value of the virtual screening relies on the accuracy of the target-ligand binding affinity prediction. It is recognized as a grand challenge for the virtual screening to accurately predict the target-ligand binding structures (molecular geometries) and binding affinities associated with diverse and massive datasets. This project aims to address the grand challenge in development of machine-learning (ML)-CADD models by introducing new, more effective mathematical representations of molecular geometries with the ability to track molecular geometry changes via Ricci curvatures and their associated spectral information. The outcomes of this project will furnish novel, more reliable computational approaches in essential areas of computational drug design, biomolecular modeling, data analysis, dimensionality reduction, and mathematical biology. Moreover, this project will provide graduate and undergraduate students with training in data analysis, biological modeling, algorithm development, and computational drug design. The enhancement of curricula from this project is planned as a continuation of the investigators' teaching-research practice. The new mathematical framework and deep learning architectures are directly integrated into computer software packages to ensure extensive usage by the community of researchers in drug design, biology, computer science, and mathematics. Additionally, the project will help train the next generation of researchers in advanced mathematics, data science, and molecular biology. This project will develop novel low-dimensional representations for biomolecular data analysis from mathematics-based approaches and robustness training data to revolutionize the current practice in structure-based virtual screening. The main objectives are: 1) to introduce molecular shape guided persistent Ricci curvature and, at the same time, to provide local geometry and spectral information to reduce the structural complexity while still maintaining an adequate description of biomolecular interactions; 2) to develop a target-ligand adaptive deep learning protocol for post-docking pose selection, binding affinity prediction, ranking, and estimation of other molecular properties; 3) to extensively validate the proposed methods on a variety of datasets to optimize the mathematical representations and learning networks. Specifically, this project will focus on the development of the proposed models for the virtual screening of phosphodiesterase-2 (PDE2) inhibitors, providing valuable hits of a promising therapeutic strategy for the treatment of various human diseases. A close loop integrating computational-experimental models will further strengthen the robustness and accuracy of the proposed models.; 4) to develop user-friendly software packages and web servers using parallel and GPU architectures for researchers who are not formally trained in advanced mathematics or sophisticated machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project investigates splicing, a fundamental cellular process where RNA molecules from the same gene are processed to produce multiple distinct proteins. This flexibility is essential for healthy development, and splicing errors are linked to numerous human diseases, including cancer and neurodegenerative disorders. A major obstacle in understanding splicing control is that traditional live-cell imaging interferes with the very dynamics under observation. This research overcomes this limitation through a tightly integrated experimental-theoretical approach that infers molecular dynamics from spatial patterns in static, high-resolution cellular images. By combining advanced microscopy with predictive mathematical modeling, the investigators will uncover the rules governing RNA splicing. This work will advance biological understanding and national health by providing deeper, quantitative insights into gene regulation to inform future therapies. The project also trains interdisciplinary scientists and produces open-source software for the research community. This research quantifies the kinetic rates governing RNA fate, including co-transcriptional splicing, post-transcriptional splicing, and degradation. The project's central strategy is the close, iterative integration of experimental imaging and mechanistic modeling to infer these dynamics from static, single-molecule images. Experimentally, fluorescence in-situ hybridization (FISH) generates high-resolution spatial maps of RNA isoforms that directly inform and validate computational models. Computationally, this project develops novel spatial stochastic models based on reaction-diffusion processes to describe RNA dynamics within spatially heterogeneous cellular environments. Bespoke inference pipelines will connect these models to imaging data for robust parameter estimation and hypothesis testing between competing splicing mechanisms. This synergistic approach will yield dual impacts for biology and mathematics, providing biological resolution to RNA splicing dynamics while establishing new mathematical theory and a powerful paradigm for inferring dynamics from static 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

Genetically engineered cell therapies are transforming how doctors treat challenging diseases like cancer, sickle cell disease, HIV, and autoimmune conditions. These treatments work by giving a patient’s own cells a new ability to find and fight disease more effectively. A crucial step in making these cell therapies accessible is by swiftly delivering new genetic material into cells using viral vectors in a liquid medium. Porous materials that guide the flow of cells and viruses dramatically improve delivery, but we do not yet fully understand how they work. Understanding how porous materials enhance cell therapy production will help make cell therapies more reliable and affordable for patients in dire need of treatment. This project will use artificial intelligence, computer modeling and experiments to determine how flow inside porous materials efficiently produces genetically modified cells. In answering this important scientific question, this project will engage students from the high school to graduate levels and help train future scientists and engineers. By integrating research outcomes in classrooms and through partnerships with industries and foundations, this work will also help spread awareness of advanced engineered cell therapies and how they can improve health outcomes. This research will develop a mechanistic understanding of how liquid flow in microfluidic devices and porous scaffolds enhances the efficiency of viral transduction in engineered cell therapies. The project will integrate experimental studies with detailed mathematical modeling to quantify cell-virus collision frequencies and the biological pathways that lead to successful gene delivery. A combination of computational fluid dynamics, discrete particle simulations, and electrostatic interaction models will be used to capture the complex flow physics at the microscale level. Novel neural network approaches, including generative adversarial models and neural net optimization algorithms, will design porous biomaterials and predict the interactions involved in viral binding, internalization, and gene integration in cells. By rigorously varying flow conditions, the research will uncover how convective and diffusive transport mechanisms impact cell-virus encounters and transduction success rates. The resulting models will provide design principles for improving gene delivery platforms and could also shed light on related biological processes, such as immune cell infiltration in tumors. The project outcomes will advance both the theory and application of cellular transduction for next-generation gene and cell therapies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIGMS - National Institute of General Medical Sciences

Infectious disease medicine is poised at the cusp of a revolutionary change in diagnostics, from simple binary diagnostics (presence/absence) directed at individual pathogens to universal tests for all pathogens along with instantaneous genomic characterization. Not only will there be benefits to sick individuals, but the entire population will benefit from better disease monitoring and knowledge of circulating strains. Nevertheless, there is still much work required to achieve genome-level diagnostic information. To achieve high sensitivity at low cost, we assume amplification of input RNA/DNA is necessary. In this context, we are faced with serious computational challenges to design sequence specific oligonucleotides, statistical challenges to account for the biases of sampling when perfect oligonucleotides are inevitably elusive, and diagnostic failures as tests lose sensitivity in the face of evolving pathogens. Recent protocol advances help to separate the confounded biases in traditional sequencing based assays, providing an opportunity for mathematical models to aid in solving the remaining challenges. We propose a novel branching process model exquisitely matched to the emerging sequencing protocols, along with novel statistical estimation methods to link the model to data (Aim 1). Aim 2 proposes to use this system to iteratively optimize a protocol for RNA virus diagnostics. Once initialized, the protocol is expected to be able to solve the oligonucleotide design problem in a data-driven manner. We also propose statistical methods for viral characterization (genotyping) in the face of inevitable biased sampling due to amplification; current methods fail to account for this bias. Aim 3 tackles longer term goals include (1) exploring the generalizability of our findings to related, but distinct protocols, (2) developing methods to monitor and update an active diagnostic protocol in the face of pathogen evolution, and (3) scaling our methods to large diagnostic datasets, such as those produced in diagnostic laboratories, by considering flexible neural networks to model the relationship between sequence and diagnostic detection. RELEVANCE (See instructions): Achieving the goals of this proposal will facilitate the development of many kinds of modern diagnostics for human and animal health. The proposed methodology can assist diagnostic test development, improvement, and maintenance in the face of evolving pathogen threats. Modern diagnostics that monitor infections and the genomic sequence of the infecting agent will facilitate pathogen-personalized treatments and better intervention strategies to protect a vulnerable population.

NIGMS - National Institute of General Medical Sciences

Physiological processes such as the sleep-wake cycle, metabolism, hormone secretion,neuro transmitter release, sensory capabilities, and a variety of behaviors including sleep, aggression, and mating are controlled by a circadian rhythm adapted to 24h day-night periodicity. The suprachiasmatic nucleus (SCN), which is in the anterior hypothalamus, controls the physiological responses in vertebrates. The peripheral tissues such as liver, heart, kidney, and mammary glands contain functional endogenous glands. SCN influences both the peripheral clock (which regulates numerous physiological processes including proliferation and apoptosis) as well as the central clock via a combination of neural and hormonal signals. Several epidemiological studies revealed that circadian rhythm disruption (CRD) impacts human health and increases the risk of developing metabolic disorders, cardiovascular diseases, and mood disorders. As the exact molecular mechanisms by which CRD alters mammary microenvironment are not known, therefore, in this study we propose to leverage the strength of next-generation sequencing and statistical bioinformatics approaches by performing single–cell spatial proteomics of the studied samples. We hypothesize using this high throughput technique will not only give an unbiased global and complete view of the cellular activities but will also provide pivotal insights about the affected cellular pathways. Using spatial transcriptomics, we will measure gene expressions at the single cell level along with the information of spatial locations of these cells in the tissue. In spatial transcriptomics, the gene expression data, along with spatial co- ordinates of the single cells, provides information on both gene expression significance and cell spatial dependencies. We propose flexible Bayesian approaches to investigate how CRD affects cell composition of a tissue by using spatial clustering algorithms to the spatial transcriptomic data. Next, after obtaining the cell types we will identify the cell-type-specific spatially varying genes. These will be utilized to see the effect of CRD disorder in gene and cell levels. The proposed research is targeted to single cell spatial transcriptomics; however, the derived methods and the results will have deep impact on the research fields of Bioinformatics and data science. Finally, computationally efficient, and tractable software (R/Python) packages will be developed, will be delivered and will be regularly updated to maximize impact across both statistics and medicine. The proposed research has immense transformative potential in the areas of basic cell and development biology, and single cell bioinformatics. It will bring together researchers from multiple disciplinary areas to conduct research on these fundamental themes. RELEVANCE (See instructions): The proposed research will provide critical answers as to whether circadian disruption via shift-work or traveling across the zones can impact mammary gland development and set the stage for further investigation of the underlying mechanisms. This study will lead to a better understanding of how circadian rhythm disruption (CRD) interrupts cell- cell interaction during different developmental stages using the data from single cell transcriptomic technique and will identify the key genes.

NIGMS - National Institute of General Medical Sciences

Biological networks are powerful resources for the discovery of genes and genetic modules. For the classical physical protein-protein interaction network, as well as for other types of pairwise association data between genes or proteins that is organized according to homophilic principles, diffusion-based low dimensional network embedding methods have proved quite powerful. While at a coarse scale, genetic interaction networks, built from high-throughput epistasis experiments, also display some homophily in their organization, at a fine scale, they display very different graph-theoretic structure that can be leveraged to find mechanisms of redundancy and fault tolerance among biological pathways active in the cell. However, the graph theoretic toolbox to analyze this fine-grained structure in genetic interaction networks is still underdeveloped, compared with algorithms developed for analysis of the purely homophilic biological networks. Inspired by some of the tools and techniques used in graph theory to study vertex cuts in networks, we propose to develop a more formal framework for categorizing the patterns of resilience and redundant pathway mechanisms in genetic interaction networks, and new algorithms for discovering genes involved in compensatory pathways. We will use our mathematical framework to computationally make phenotype predictions for the results of gene knockout experiments with either unseen gene combinations or in unseen environmental conditions. We will validate some of our predictions focusing on alternative pathways for DNA replication and repair in Saccharomyces cerevisiae (baker's yeast). Since DNA replication and repair are crucial pathways for cancer cell survival, identification of genes in those subnetworks could reveal new cancer therapy targets. Discovery of new genes is important for a basic understanding of cell functioning and to elucidate the cause of inherited genetic disease. RELEVANCE (See instructions): Saccharomyces cerevisiae is a foundational model system for studying mechanisms of resilience and fault tolerance in the Eukaryotic cell, and the pathways for DNA replication and repair are sufficiently evolutionarily conserved to make insights in these model systems relevant for understanding human diseases that exhibit genome instability. Methods to target compensatory pathways are also an active area of drug design, including in the search for personalized therapies customized by tumor in cancer.

NSF

This project develops combinatorial mathematics for new biological concepts that have emerged from genomic studies of phylogenetic trees and networks. Phylogenetics—the interpretation and construction of trees and networks that describe relationships of shared descent from common ancestors—is central throughout the entire field of biology, contributing to biological subfields in areas as varied as developmental biology, ecology, evolution, genetics, microbiology, and the biology of cancer. The research advances the fundamental understanding of mathematical structures that underlie descent relationships of cells, species, and strains for multifarious biological applications. The project develops the mathematical area of phylogenetic enumerative combinatorics at the intersection of mathematics and biology, toward performing classifications of new discrete combinatorial structures that relate to phylogenetic trees and networks. The project performs unified enumerative studies of several objects, advancing an approach for mathematical analysis of novel phylogenetic combinatorial structures. The structures that will be investigated include: the “perfect phylogenies” that appear in contexts involving genetic recombination and algorithmic improvements in phylogenetic computation; the “galled trees” and “rankable tree-child networks” that assist in extending trees to include biological processes of hybridization and horizontal gene transfer; the “ancestral configurations” that arise from consideration of genetic lineages descending through speciation events; and encodings of trees for phylogenetic analysis of pathogen strains. Its combinatorial analysis proceeds by a shared multidisciplinary framework linking biological structures with methods from the fields of enumerative combinatorics, analysis-of-algorithms, and analytic combinatorics, employing lattice theory, recurrences, generating functions, asymptotic growth analysis, and correspondences with existing combinatorial structures. The project strengthens linkages between mathematics and biology, advancing the mathematics that undergirds the field of phylogenetics, an area that itself serves as a central unifying topic throughout biology. Further, it promotes interdisciplinary mathematical and biological training at the postdoctoral and PhD levels and advances undergraduate research at the interface of biology and mathematics. 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.

NIGMS - National Institute of General Medical Sciences

Abstract: The objective of this project is to develop a novel modeling and simulation platform that addresses the crucial need for a quantitative understanding of micellar behavior as drug delivery systems. To achieve this, we will combine theoretical, computational, and experimental methods to create a new two-species constitutive model. This model will be capable of capturing the complex interplay between micellar structure and rheological behavior under various physiological conditions, including changes in pH and flow fields. The platform will leverage advanced numerical algorithms to simulate the dynamic evolution of micellar structures and their impact on macroscopic properties. By doing so, it will enable accurate predictions of drug release kinetics and other critical phenomena. To ensure the reliability of our platform, we will rigorously validate its results with experimental data. Ultimately, this platform will serve as a crucial asset for the development and refinement of micellar-based drug delivery systems. By providing accurate predictions and insights, it will expedite the creation of more effective and targeted therapies. This research project aims to address a significant challenge in the field of drug delivery by developing a novel modeling and simulation platform capable of accurately predicting the behavior of micellar systems under physiological conditions. Micelles, as versatile nanocarriers, offer immense potential for revolutionizing drug delivery by overcoming limitations associated with traditional therapies. However, our limited understanding of their complex interactions with external stimuli and their rheological properties hinders the development of effective micellar-based drug delivery systems. The proposed platform will provide a robust framework for simulating micellar behavior under diverse physiological conditions, such as pH and flow fields. By developing a new constitutive model that accounts for these factors, this research will contribute to a more comprehensive understanding of micellar behavior in complex environments. The platform will also enable simulations that mimic the drug delivery process, providing valuable insights into the viability of micelles for cancer treatment and other applications.