Northeastern University

NSF Awards · FY 2024 · 2024-08

Reinforcement learning (RL), where machines learn to perform tasks by trial and error, has become an incredibly important paradigm in artificial intelligence. Nevertheless, much of the progress has focused on fully observable domains, where we have full knowledge of the world that these machines are in. Real-world domains are typically partially observable with limited information as to what is happening. For example, domains such as autonomous driving or robotics are partially observable due to limited ability to sense what is happening in the environment. In order for reinforcement learning to perform well in these partially observable applications, machines must be able to remember relevant information from the past. Current methods typically use relatively simple approaches based on neural networks to determine what information to remember and what to forget. In contrast, algorithms, like transformers, have shown a massive improvement over other models in language (e.g., ChatGPT) and vision (e.g., DALL-E) problems. This project will develop new methods for partially observable information, which should lead to significantly improved performance in partially observable reinforcement learning problems. This project will develop a number of novel methods for partial observable reinforcement learning (PORL). In particular, the project will develop attention-based model-free approaches (including the first attention-based actor-critic methods) that perform well, are computationally efficient, can reason over long horizons, incorporate structure such as permutation invariant histories, and can process high-dimensional vision input. The project will also develop the first attention-based model-based PORL methods. These approaches will improve sample efficiency and performance by incorporating supervised auxiliary tasks, dynamics models, or additional planning steps for improving exploration and model learning. Lastly, the project will formalize goal-conditioning in partially observable Markov decision processes (POMDPs), develop the first attention-based multi-task and goal-conditioned PORL methods, and develop pre-training approaches that can train on a wide range of domains and fine-tune to desired test domains. The resulting methods and analysis will allow the community to better understand the role of attention-based methods for PORL and to build on this work to develop approaches that can efficiently learn in realistic partially observable domains. 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

Many of the organs in our body are built of tubes. They include the digestive, reproductive, and cardiovascular systems. Critical components of these tubular systems are contractile valves and sphincters that regulate passage of solid or liquid contents between tissue compartments. Sphincters in large tissues are made of many muscle cells arranged in a circle. However, tiny valves composed of a just few cells can somehow also perform these functions. In the reproductive system of the nematode C. elegans there is a donut-shaped valve that opens and closes hundreds of times to allow eggs to pass from where they are fertilized to the uterus. The team will characterize the inner structure of the valve cell with light and electron microscopy and use genetic perturbations to discover the molecular mechanisms that regulate its formation. Then, they will investigate its function with live imaging and use biophysical modeling to understand how its structure facilitates its function. This work is a first step in understanding how molecular values might be made as part of bioinspired molecular machines with bioeconomy applications. The integrated science education and outreach goals are focused on increasing access to and representation in research, developing a new Bioinformatics Course-based Undergraduate Research Experience (CURE), and supporting international understanding and collaboration. Students will play an integral role in the project as they discover and characterize the genes that play a role in the development and function of the contractile valve. While multicellular, muscular valves are well studied, we know very little about the smallest contractile epithelial valves, which recapitulate the function of a large tissue using only a few cells. To determine how these structures form and function, the team is using a tiny, donut-shaped valve in the reproductive system of the nematode C. elegans as a model system. A contractile acto-myosin ring in this valve opens and closes hundreds of times to control the passage of eggs from the spermatheca to the uterus. The contractile apparatus is a complex bi-layered structure with longitudinal actin cables surrounded by circumferential actin rings, surrounded by a ring of tubulin, of as yet unknown function. The Cram, Zaidel-Bar, and Shemesh labs are investigating the assembly and regulation of this contractile apparatus by use of fluorescence confocal microscopy of endogenously tagged proteins, cell-specific RNAi, long-term imaging in a microfluidic device, laser ablations, and mathematical modeling. This research will reveal basic principles of how intracellular contractile rings form, function, and are robustly regulated within small valves. This apparatus has features in common with contractile rings in other animals, suggesting generalizable principles will be revealed. Features of the ring, such as a barrel arrangement of actin and the surrounding microtubule ring, imply that novel concepts and mechanisms of contractility remain to be discovered, including how contractile cytoskeletal structures are assembled and positioned, how this valve can expand and contract repeatedly and robustly, and the role of the microtubules that surround the ring. This collaborative US/Israel project is supported by the US National Science Foundation and the Israeli Binational Science Foundation. 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

Common cellular and porous materials deform and absorb energy mainly through bending, stretching, and compression of continuously connected phases. In contrast this new category of mechanical metamaterials, named frictional mechanical metamaterials (FMM), are composed of separated but interacting components that dissipate energy through internal friction between components as they engage with each other. The new concepts, methodology, and tool sets developed in this research will not only create new knowledge in fundamental mechanics, but also will bring broad technical revolutions to leverage the fast advancements of mechanical metamaterials, additive manufacturing, and material science. The potential applications for new FMMs include protective materials, energy materials, soft robotics, actuators, dampers, adhesives, seismic engineering, and biomedical engineering. Customized STEM education activities under the theme of ‘Magic Friction’ will be designed and launched with support from the Center of STEM Education at Northeastern University. The education and outreach activities will focus on attracting and retaining students from underrepresented groups by promoting new design concepts for achieving unusual material properties and behaviors based off traditional mechanical engineering topics. This project will make fundamental contributions to the mechanics of materials and structures in theoretical, experimental, and numerical aspects by bridging the frontiers of the fields of mechanics, materials, mechanical metamaterials, and advanced additive manufacturing at both macro- and micro/nano- scales. For conventional materials, resilience and hysteresis often conflict with each other. The FMMs provide a platform to achieve both properties simultaneously. To amplify energy dissipation efficiency, auxeticity is utilized to promote internal friction in multiple directions, and chirality is employed to generate coupled sliding and rotational friction. Under the general goal, three specific objectives are planned: Objective I: To advance fundamental mechanics of friction under complicated conditions; Objective II: To explore mechanical behaviors of various engaging key-channel pairs; Objective III: To generate internal friction in multiple directions via auxeticity. Prototypes will be fabricated via 3D printing in both macro and micro scales. Innovative designs will be generated. An integrated analytical, numerical, and experimental methodology will be used for systematic investigation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract: This research proposal responds to the pressing need for comprehensive understanding and targeted support in breastfeeding outcomes, an area significantly impacting child health and development. Despite the recognized importance of breastfeeding, existing clinical practices often overlook the intricate interplay of maternal lifestyle and behavior. Mothers face diverse challenges including return to work, inconsistent milk supply, lack of support, and postpartum mental stress, all of which profoundly influence breastfeeding success and subsequently, the well-being of the child. Although national recommendations advocate exclusive breastfeeding for the initial six months of a child's life, the actual compliance, as reported by the Center for Disease Control and Prevention, is less than 25%. This study takes a digital phenotyping approach by harnessing passive longitudinal monitoring, facilitated through smartphones, smartwatches, and ecological momentary assessments (EMAs). By examining physiological components such as sleep, exercise, and vitals through smartwatches, alongside behavioral patterns encompassing mobility, screen time, and social support via smartphones, we intend to identify lifestyle factors during the third trimester impacting breastfeeding outcomes. A pivotal aspect of this research lies in its ability to bridge the existing knowledge gap by delving into the multifaceted aspects of maternal well-being, mood, lifestyle, mental health, and social support during the critical period of the third trimester to postpartum. To our knowledge, this is the first study of its kind to focus on breastfeeding outcomes. We seek to modify and implement a culturally adapted digital phenotyping protocol tailored for first-time mothers. Through rigorous data collection, encompassing both the objective data from wearables and mobile devices and subjective self-reports, we aim to understand the nuanced dynamics affecting breastfeeding success. This research, anchored in innovative technology and supported by a team with extensive expertise in maternal health and behavioral science, is poised to transform breastfeeding support. By offering novel insights, our findings will inform future research endeavors and pave the way for personalized interventions, thereby enhancing breastfeeding success and ultimately contributing to the improved health and development of children.

NIH Research Projects · FY 2026 · 2024-08

ABSTRACT Tobacco addiction remains a leading cause of disease and death worldwide, and an increasing number of never- smokers are being exposed to nicotine via e-cigarettes. However, the role of specific nicotinic acetylcholine receptors (nAChR) in nicotine’s behavioral effects remains poorly understood because of the availability of very few subtype-selective probes, which limit drug development potential. Thus, this proposal aims to develop novel probes to better understand the function of the different α4β2 subtypes, which have been proposed to underlie behaviors characteristic of nicotine dependence. The α4β2 nAChR subtypes exhibit two distinct isoforms: α42β23, which has a high affinity for acetylcholine and nicotine, and α43β22 nAChR, which has a lower affinity for acetylcholine and nicotine. Our recent findings have suggested that probes selectively targeting the high sensitivity α42β23 nAChRs may provide a novel, previously undefined understanding of nicotine’s behavioral and pharmacological actions. Thus, these studies will develop and optimize novel positive allosteric modulators (PAMs) of the high sensitivity α42β23 nAChRs. Specific Aim 1 will begin by focusing on the synthesis of novel analogs of desformylflustrabromine (dFBr) and GAT2802 to derive an improved pharmacological profile. Specific Aim 2 will involve in vitro characterization of novel probes from both structural classes as α42β3 nAChR-selective PAMs. Thereafter, Specific Aim 3 studies will evaluate ADME/PK profiling of key α42β23 PAMs to eliminate potential liabilities and identify probes with suitable drug-like properties. Finally, in Specific Aim 4, the most promising probes will be tested for their efficacy in inducing behaviors characteristic of nicotine’s 42 nAChR actions in adult male and female mice. Taken together, these studies will develop and validate novel HS-selective α42β23 PAM probes to better understand the pharmacological effects of α4β2 nAChRs in behaviors characteristic of nicotine’s actions, which may thereby lead to future therapeutic implications.

NIH Research Projects · FY 2025 · 2024-08

Using Mobile Technology and Real-World Vocalization Samples to Generate Quantitative Metrics of Vocal Communication for Minimally-Speaking Individuals Over one million individuals remain minimally-speaking past the age of 5, often communicating through vocalizations that contain little to no speech. Yet, little is known about the vocal communication of profoundly affected minimally-speaking individuals. Current speech and communication metrics are often normalized based on typically-developing individuals and fail to adequately capture the skills of minimally-speaking individuals. Moreover, measures that are designed for this population are generally qualitative, relying on caregiver reports or examiner observations from a short clinical visit. The lack of quantitative communication measures that map how vocalizations from minimally-speaking individuals develop over time has profound consequences for researchers, clinicians, families, and the individuals themselves. The lack of quantitative communication measures designed for this population has profound consequences for researchers, clinicians, families, and the individuals: Clinical trials have failed to advance because the outcome measures did not capture the subtle changes in participants’ development, and families of minimally-speaking individuals consistently list communication as one of their top research priorities. This proposal aims to generate a sensitive, quantitative metric of vocal communication for minimally-speaking individuals by tracking their phonological vocalization complexity over time. To achieve this aim, I will enroll n=90 individuals (ages 6-35 years, enrolled equally over the age range) with fewer than 50 spoken words using a smartphone app I previously developed and deployed. This app allows caregivers to record a vocalization and label its meaning with the tap of a button in their homes and natural environments. Caregivers will be prompted to elicit and record as many vocalizations as possible during daily ten-minute intervals for one week every six months for two years. Using this unique database of real-world vocalizations, I will characterize the complexity of vocal communication across and within individuals over time (Aims 1 and 2) and will examine how this metric relates to established assessments (Aim 3). I will also explore acoustic-based metrics that capture vocalization complexity in an objective and scalable manner. The results of this work will allow us to determine sensitive vocal communication milestones for profoundly affected minimally-speaking individuals and appropriate outcome measures for clinical trials and interventions.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT The tumor microenvironment (TME) plays a critical role in hematologic malignancies, especially in multiple myeloma (MM). Each cellular and non-cellular component of the TME exerts a different effect on MM cell survival, proliferation, immune evasion, and resistance to treatment. Several studies have shown that CAR- T therapy produces an overall response rate of 78% in the relapsed/refractory MM setting; however, many patients still relapse, and the median time to disease progression is about 1 year. Therefore, there is an urgent need to define mechanisms of disease progression and resistance to CAR-T and bispecific antibodies using an in vitro cell culture system that mimics the complex TME. Continuous progress in tissue engineering, including the development of various 3D scaffolds and microfluidic systems, has improved the diversity, fidelity, and capacity of culture models that can be used in cancer and other disease research. Most 3D in vitro culture systems lack the integral TME and dynamic perfusion and/or influence immune-tumor crosstalk or/and prolonged culture capabilities. In addition, most models cannot mimic the hypoxic gradients observed in tumors, which dramatically reduces the efficacy of molecular and cellular therapeutics. The hypoxic TME likely protects tumors against immunotherapies by altering cellular metabolism and inducing immune suppression. Therefore, an ideal experimental in vitro cell culture system should mimic the heterogeneous nature of the hypoxic TME to allow a more complete understanding of cancer cell and immune cell biology, immunotherapy validation, and development of efficacious treatment strategies for clinical application. To address the aforementioned limitations, in this proposal, we will focus on developing a novel microfluidic droplet-based platform (MDP) technology to generate and analyze a 3D biomimetic multicellular immunogenic tumor model and test its capabilities to (1) establish multiple levels of hypoxia within the same tumor-chip for parallel processing; (2) investigate spatiotemporal interaction between TME cancer- immune cells during therapy; (3) quantify the impact of state-of-the-art targeted immunotherapy efficacy and define multiparametric (dynamic, secretomic, and transcriptomic) responses for a comprehensive analysis of cell fate. We will incorporate patient tumor cells and their microenvironment in MDP to predict the status of the patient as a potential responder or non-responder. Potential responders to immunologic therapy such as CAR-T will benefit from not having to wait for several months in clinic to determine whether the therapy has achieved response or not, while potential non-responders will be spared the side-effects of non-effective treatment and help clinicians choose other forms of therapy. Our approach will therefore result in the development of a versatile and multifunctional system that can serve as a new and innovative technology for deep analysis of cell-cell interactions and predicting the optimal therapy for individual patients and significantly advance the goal of personalized medicine.

NSF Awards · FY 2024 · 2024-08

Four million tons of end-of-life tires (ELTs) are generated annually in the USA. Significant amounts of waste tire rubber are repurposed as ground rubber, used in pavement, or sent to landfills. Currently in the USA there are more than 13,000 artificial turf fields filled with crumb rubber and 50 million unmanaged scrap tires exposed to sunlight and other weathering phenomena. This causes the breakdown of ELTs resulting in emissions of chemicals, some of which have ecological toxicity in soil and water environments. There are considerable gaps in our knowledge of the occurrence, transformation, and release of toxic chemicals from ELTs. This study will help address these gaps by identifying chemicals released from ELTs and assessing their environmental behavior. This will be achieved using state-of-the-art chemical analysis techniques in controlled laboratory and field studies. The results will allow us to assess the release and transformation of chemicals from ELTs and characterize their occurrence in the environment. Results will be used to benefit society by informing effective waste tire management practices and help develop environmentally friendly products. Further benefits to society will result from collaboration and training of citizen scientists to enhance scientific literacy and empower citizen scientists with knowledge and skills in environmental chemistry. Recent studies have demonstrated that tire rubber-derived chemicals such as 6PPD-quinone are both ecotoxic and prevalent in the environment. This project is built on the hypothesis that yet to be identified rubber-derived chemicals are released during the ELT aging process, and these chemicals contribute to the environmental mobility, bioaccessibility, and ecotoxicity of ELT materials. Research will focus on identifying the transformation products of ELT aging in controlled laboratory conditions and in the field. High-resolution mass spectrometric analysis of environmental samples will be compared to reference chemicals and existing databases to identify and quantify both known and unknown reaction products at artificial turf fields, tire disposal piles, and underwater reefs. Simulated aging studies will utilize similar analytical methods for three types of waste rubber samples - cryo-milled new tire rubber, mixtures of new/used tire rubber, and new crumb rubber from artificial turf. Aging of samples under simulated solar radiation will be used to investigate the effect of light on product generation at different stages of sample aging. Simulated oxidation/ozonation experiments will assess the reaction kinetics of product formation. Rooftop aging experiments will simulate natural aging under real conditions via integration of the combined effects of sunlight, precipitation, temperature, and atmospheric oxidation. Results will be used to understand i) water leaching potential, ii) ELT product bioaccessibility; iii) in vitro toxicity, and iv) effect-directed analysis of sample extracts exhibiting high ecotoxicity to identify causal chemicals. 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

With the support of the Chemical Synthesis Program (SYN) in the Division of Chemistry (CHE), Professor George O’Doherty of Northeastern University in Boston will study a novel carbohydrate linking reaction and its application for the synthesis of oligosaccharides. Oligosaccharides are comprised of several monosaccharide, or simple sugar, building blocks and possess structural complexities unique from the other biological polymers/oligomers (e.g., protein, DNA, RNA). In particular, the selective construction and linkage of these monosaccharide units remains a significant challenge, creating the need for lengthy synthetic sequences and often limiting access to potentially valuable unnatural derivatives. These synthetic challenges have made it more difficult to study the role of oligosaccharides in biology and medicine. The O’Doherty group will explore novel synthetic methods designed to mimic the reactivity of natural enzymatic systems to prepare both the individual sugar building blocks as well as selectively assemble them into biologically relevant oligomers. The students who will be conducting this research are diverse in terms of training (undergraduate, graduate, and postdoctoral) and cultural backgrounds with significant participation of women and under-represented students from the US and around the world. A major focus of the student’s research efforts is on overall synthetic efficiency, which both benefits the environment (green chemistry) as well as better enables future biological and medicinal chemistry studies. This project aims to develop new synthetic methods for the efficient asymmetric syntheses of oligosaccharide motifs with novel regio-chemical and stereo-chemical connectivity. The oligosaccharide structures that are targeted are based upon naturally occurring motifs, however, the approach will enable access to unnatural oligosaccharide variants with novel stereochemistry and substitution. Access to these unnatural motifs will enable future biological and medicinal chemistry studies that are currently viewed as impractical. Central to this effort will be the study of a recently discovered bimetallic B/Pd-catalyzed glycosylation reaction. This B/Pd-catalyzed glycosylation can be viewed as an abiotic mimic of glycosyltransferases with the boron portion being the site of nucleophilic catalysis and the Pd-site being the site of electrophilic catalysis, with an ionic bond replacing the peptide backbone that holds the two sites together. The B/Pd-glycosylation is the key coupling reaction that enables the synthetically efficient de novo asymmetric synthesis of oligosaccharides, where the efficiency can be seen in the low number of synthetic transformations and protecting groups used. In the end, this synthetic chemistry project aims to fundamentally change the way oligosaccharides are synthesized which will have the downstream effect of enabling the application of carbohydrate chemistry to real-world problems in science and medicine. 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

Artificial neural networks in machine learning have impacted many fields of science and engineering. Despite tremendous progress and achievements, implementing artificial neural networks on conventional computers is becoming increasingly challenging due to power and speed constraints. Optical neural networks (ONNs), which offer potential advantages in energy efficiency, speed, parallelism, bandwidth, and scalability, stand out as a highly promising solution to this challenge. The goal of this project is to design and implement a novel class of metasurface-based ONNs, termed meta-ONNs, which can operate at optical frequencies and realize diverse functions, including all-optical image recognition and pattern generation. Metasurfaces are composed of artificially engineered structures much smaller than the wavelength of light. They can manipulate light characteristics such as the amplitude, polarization state, and phase in a prescribed manner. By leveraging the unique properties of metasurfaces, this project will demonstrate innovative meta-ONNs capable of encoding multiple functional channels within a single system and achieving functions beyond conventional classification, significantly expanding the capabilities of ONNs by transforming computing, communications, and information processing technologies, thus benefiting the public and the nation. Integrated with the research, the education effort of the project will enhance outreach activities and educate students across different levels. Students will actively participate in the project, gaining frontier knowledge in multiple fields and eventually becoming leaders in the next-generation workforce. The project aims to unlock the potential of meta-ONNs as a new platform for multifunctional optical computing, complex information processing, and innovative image generation through a software-hardware co-design approach that seamlessly integrates photonics, neural network models, advanced manufacturing, and systems engineering. The project consists of three research thrusts: (1) Design multiplexed meta-ONNs based on artificial intelligence (AI) and optimization techniques to seamlessly integrate multiple wavelength and polarization channels within a single system, greatly enhancing the capacity and versatility of ONNs; (2) Demonstrate generative meta-ONNs that can create distinct images after light propagates through the meta-ONNs, enabling novel optical encryption schemes and serving as pivotal tools for AI-assisted photonic design; (3) Fabricate low-loss, multilayered metasurfaces to implement the designed meta-ONNs, and experimentally characterize the key performance metrics including accuracy, efficiency, and robustness. The precise control of light at the subwavelength meta-neuron level is expected to significantly boost the capacity of ONNs. New manufacturing methodologies and techniques will be developed to realize high-efficiency meta-ONNs. The potential applications of the meta-ONNs include image generation for virtual reality and entertainment, medical imaging and diagnostics, security and surveillance, autonomous driving, and advanced photonic circuits for quantum computing. The research findings will accelerate the interplay between AI and photonics, forming the virtuous AI-photonics-AI circle. The design principles could also serve as inspiration for other physical neural networks and intelligent devices based on mechanical, electrical, and acoustic systems. 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 · 2024-07

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry Professors Reinhard and Dennis from Boston University and Northeastern University will investigate charge and energy transfer between metal nanostructures and semiconductor nanocrystals through single particle spectroscopy. The chosen metal (gold and silver) and semiconductor (chalcopyrite, CuFeS2) nanomaterials both support collective charge oscillations that provide opportunities for very efficient coupling between them under resonant conditions. The lineshape of the scattering spectra of individual hybrid structures containing both metal nanoparticles and semiconductor nanocrystals will be analyzed to characterize direct charge and energy transfer between the building blocks. Optimization of these transfer processes has the potential to result in enhanced photocatalytic activity for the hybrid nanomaterials, which will be tested experimentally. Improved photocatalytic materials have important societal relevance, for instance in solar energy conversion and waste water remediation. The research of this project will be enriched by educational and outreach components. For instance, a Nano Workshop (Boston University) and a Quantum Dot Bootcamp (Northeastern University) will be developed to introduce interested high school teachers and inner-city high school students to the concepts and science underlying this research project. Plasmon dephasing in noble metal nanostructures generates hot charge carriers that are of interest in a wide range of applications, including photoconversion and photocatalysis. Unfortunately, hot electrons and holes recombine rapidly in noble metal nanostructures, severely limiting their potential for applications. Hybrid structures comprising noble metal nanoparticles and semiconductor nanocrystals may increase the lifetime of the reactive charge carriers by charge separation, but extraction of the hot charge carriers competes with their rapid thermalization, limiting the efficiency of the process. Hybrid nanostructures that produce excited charge centers in the semiconductor through direct energy and/or charge transfer without a priori generation of hot charge carriers in the metal hold great potential to increase the generation of long-lived reactive species. Chalcopyrite nanocrystals sustain quasi-static resonances in the visible, which provides unique opportunities for enhancing direct charge and energy transfer in hybrid structures in which noble metal and chalcopyrite building blocks are resonantly coupled. This project will use single particle spectroscopy to quantify interfacial plasmon dephasing as a measure of direct excitation transfer in metal/chalcopyrite hybrid systems with correlated electron microscopy to elucidate the composite structure/function relationship on a single-particle scale. Hybrid systems containing building blocks whose collective resonances show different degrees of energetic overlap will be used to test the hypothesis that resonant coupling between the building blocks enhances direct excitation transfer. 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 · 2024-07

Is it possible to download a video from a streaming service without revealing to the service which video is being downloaded? Is it possible to search the Internet without revealing the search terms to the search provider? Two advanced cryptographic tools called Private Information Retrieval and Fully Homomorphic Encryption enable secure computations over encrypted data, raising the potential of private services such as the ones above. However, these tools place a heavy computational burden on the server. For example, they would require the search engine to read the entire internet to answer each private search query. The PI’s recent work shows how to remove the above inefficiencies by constructing schemes, where the data is preprocessed once upfront, but afterwards each execution is efficient and avoids reading the entire data. This proposed research builds on the recent progress and addresses many remaining open problems. In addition, the project features graduate student mentoring, curricular development, and undergraduate inclusion in research. The aim of the project is to study Doubly Efficient Private Information Retrieval (DEPIR) and Fully Homomorphic Encryption for Random-Access Machines. The main goals fall into four categories: (1) Practical Efficiency: The existing results show theoretical feasibility, but are highly impractical. Can one get practically efficient constructions? Luckily, there is much potential for improvement/optimization. (2) Assumptions. The existing results are based on the Ring learning-with-errors assumption. Can one also get similar results from standard learning-with-errors, or other assumptions? (3) Beyond Fully Homomorphic Encryption: Can we have other “doubly efficient” cryptographic primitives that operate over huge data in the random-access machine model? Candidates include attribute-based/functional encryption, laconic function evaluation, commitments, succinct arguments and multi-party computation with active security. (4) Client-Specific Preprocessing: We also consider a variant of DEPIR with client-specific preprocessing, which is not known to imply standard private information retrieval or even one-way functions. We explore the possibility of vastly more efficient constructions and potentially even information-theoretic security. 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 · 2024-07

As the ultimate arbiter of crucial legal disputes, the U.S. Supreme Court occupies a pivotal position in American democracy. However, relatively few cases make it to the Court. In any given year, the Court receives thousands of petitions asking it to review lower court decisions, of which less than one hundred are granted review, or writ of certiorari. The discretionary authority to choose its cases endows the Court with substantial influence in shaping national public policy. Given the statistical rarity of a case receiving a formal decision by the U.S. Supreme Court, and the unrepresentative nature of the petitions that justices choose among, it is critical to understand not just the decisions the Court makes on the merit, but also the density and diversity of issues and interests competing for the Court’s consideration. How does the supply of certiorari petitions shape the justices’ behaviors? How do external actors, such as interest groups and the media, shape the Court’s docket? How does the Court’s selection of petitions granted review represent the plurality of public interests? To answer these and related questions, the research project endeavors to collect, categorize and analyze a host of important features in the writ of certiorari process. Recognizing the seminal importance of the agenda-setting stage, the research intends to provide a better understanding of judicial decision-making and judicial processes as well as the essential role of courts in American democracy. The research enhances existing theories of judicial behavior, with a specific focus on the early stage of decision-making in the United States Supreme Court: the decision to grant review, or writ of certiorari. The research collects and analyzes case features from all writ of certiorari petitions— that is, all lower court cases where the losing party, dissatisfied with the outcome, appeals to the US Supreme Court. The study catalogs—for the first time—the geographic origin, temporal distribution, and issue areas of cases the Court is asked to review. It also examines the role played by external actors in shaping the Court’s agenda, including the media and interest groups, as well as the extent to which the Court’s final selection of cases is consistent with the expectations of the plurality of interests in society. This multifaceted investigation inquires into how these factors collectively impact the decision-making process of the justices, leading to both empirical and theoretical contributions in the study of judicial decision-making. First, it provides new data on writs of certiorari petitions that are comprehensive over time and space, and across issues. Second, the project presents a unique perspective on the courts more generally by contributing to theories that are built around informational cues, and by using computational social science methods to test how the selection of cases impacts judicial outcomes. Third, it seeks to refine and extend machine learning algorithms for legal text analysis of cert petitions and external actors, potentially paving the way and setting new standards for data-driven research in judicial politics. Finally, it supplements the quantitative analyses with interviews of former Supreme Court clerks and amicus-filing entities to provide a richer perspective on how the justices sort through the large number of petitions received every year, and how the Court selects the final set of cases to review. Through the research, and, importantly, the creation of a dataset that categorizes a host of features of all cert petitions to the Court, this project will provide judicial, interest group, media scholars and the public with new insights on the workings of the judicial system. 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 · 2024-07

Despite the recognized need for a strong cybersecurity workforce to complement our nation’s technical strength, the U.S. suffers from a substantial cybersecurity workforce gap. Nowhere is this gap more keenly felt than in the public sector, where agencies at all levels struggle to acquire and develop strong cybersecurity talent. This project is a continuation of Northeastern University’s long-standing participation in the Scholarship for Service (SFS) program since 2009. Northeastern is designated as a National Center of Academic Excellence in Cyber Research (CAE-R) by the National Centers of Academic Excellence in Cybersecurity (NCAE-C) and has a long track record of success in placing skilled cybersecurity workers across government agencies and Federally Funded Research and Development Centers (FFRDCs). In this project, Northeastern will build upon its successful history of training cybersecurity professionals with a three-fold approach: (i) broaden recruitment efforts, (ii) deepen exposure to the cybersecurity community and current needs, and (iii) strengthen support for placing students at federal executive agencies. With this approach, the team broadly seeks to scale up its SFS programmatic efforts across the workforce recruitment and development pipeline. The team will raise awareness of the mutual benefits of the SFS program to a wider population of candidates. Recognizing the significant benefit of use-inspired research and education, the project will deepen the exposure of SFS scholars to contemporary issues and open needs in the security community. Finally, the project will strengthen already-established mentorship and advising frameworks, building additional recruitment channels to participating agencies. The ultimate objective of this effort is for all SFS students at Northeastern to efficiently fill open positions at agencies across the US government and other hiring partners. This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal Government organization for the same duration as their scholarship support.  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 · 2024-07

Microbial consortia play crucial roles in human health, food crop performance, and other industrial and biotechnological applications. Synthetic ecology is a bottoms-up approach in which defined microbial communities are assembled and studied in controlled laboratory conditions. This type of study provides a powerful paradigm for elucidating the factors underlying community dynamics, stability, and outputs. However, the potential of this approach is constrained by a key technological barrier: the inability to conduct experiments under physiologically relevant conditions (continuous culture) at scale (multiple experimental conditions in parallel), and with control of the atmospheric gas composition. This project addresses such constraints by developing a low-cost bioreactor system for precise generation and simultaneously delivery of headspace gas mixtures across individual culture vessels in various cultures. This system is used to investigate a model synthetic community, comprised of aerobic and anaerobic bacteria, to understand how variations in oxygen drive community microbial composition in environments like the Human gut microbiome. Elements of this research are incorporated into educational activities for undergraduate and high school students. This research also supports the establishment of an iGEM team at Northeastern University, a new K-12 curriculum, and workshops designed to enhance interactions between synthetic biologists and audiences in the arts and humanities. Low-cost technology that provides precise, temporal control of the gas composition across individual cultures would be groundbreaking for studying microbial consortia. In particular, those that comprise both aerobes and anaerobes, or those that use gaseous substrates like CO2 and H2 for growth would significantly benefit from such technology. This project develops a system for precise generation and delivery of headspace gas mixtures (atmostat), coupling it with the eVOLVER which is a parallel mini-bioreactor platform, to generate a first-in-class benchtop bioreactor technology. The system is capable of scalable, automated exploration of microbial consortia in continuous culture with control of atmospheric gas composition. The eVOLVER-atmostat system is used to study a novel synthetic community consisting of the aerobic heterotroph E. Coli and the anaerobic model acetogen Clostridium ljungdahlii. The first study examines the impact of O2 and H2 levels on community dynamics and global transcription and investigates how community dynamics are affected by heterologous expression of O2 detoxification mechanisms. The second study focuses on elucidating and engineering novel syntrophic metabolic cross-feeding interactions that can occur exclusively in a community combining aerobic and anaerobic metabolism. 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 · 2024-07

Non-terrestrial networks (NTNs) can provide ubiquitous coverage and resilient connectivity, but currently only with achievable data rates far from the expectations of 5G networks and 6G forecasts. Terahertz (THz) band technology (0.1-10THz) has recently been envisioned as a potential enabler of high-rate space-based NTNs. This project is aimed at exploring the development of the world’s first community research platform for sub-terahertz satellite communication networks. The platform plans to have two twin small satellites with a software-defined sub-terahertz radio platform that will enable numerous inter-satellite communication experiments, as well as a ground station to investigate the feasibility of ground-to-satellite access links. This collaborative project brings together interdisciplinary investigators from Northeastern University (NU) and Morehead State University (MSU), including accomplished experts in THz communications, wireless networking, mechanical and aerospace engineering, systems engineering, and satellite development. This two-year effort will focus on developing and testing space-qualified sub-terahertz radios, which will integrate world-record transmit power front-ends at 225 GHz with an ultrabroadband programmable digital signal processing engine. In addition, the satellite bus requirements, including the dimensions and weight of the small satellites, the electrical power system capacity, mechanical interfaces, and deployment procedures, will be analyzed. Moreover, a digital twin simulator will be designed to accurately replicate the entire infrastructure operation and provide extensive inputs to the mission planners. This project's developmental work could lead to better global connectivity. High-throughput sub-THz NTNs will bridge the Digital Divide by providing reliable high-speed internet to remote and underserved communities. Additionally, the inherent resilience of satellite-based systems offers a reliable communication backbone for critical operations during both natural and human-driven instabilities and catastrophes. Beyond societal impact, the project will contribute to international NTN standardization efforts and spectrum policy development. Furthermore, project findings will be integrated into interdisciplinary courses at both universities, fostering future generations of researchers. The project website, http://www.thz-sat.com, will be a one-stop shop for everyone interested in broadband non-terrestrial networks at terahertz frequencies. The website will provide an overview of the field, the latest publications by the team, and the up-to-date status of the infrastructure development, including all the major milestones towards the eventual launch and operation of this unique infrastructure. 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 · 2024-07

A number of platforms match workers with employers offering short tasks or jobs that require some knowledge, such as writing short articles, editing videos, or labeling data for artificial intelligence (AI) algorithms. This "gig knowledge work" provides workers useful flexibility, but also a number of challenges, such as limited opportunities for mentoring and apprenticeship, lower wages than they might make elsewhere, and/or potential wage theft by employers who reject valid work. This project's goal is to design new tools to help gig knowledge workers identify shared challenges and collaborate to address them. These tools will allow workers to collect and make sense of job, wage, and employer data as a group in order to identify challenges they face together, and to develop and implement plans for taking action around those challenges. Through this research, the project team will advance understanding on both the challenges gig knowledge workers face and ways to address them, as well as around developing tools to support collective action more generally. The findings and tools will be disseminated widely through computer science courses for high school and college students that encourage socially responsible and nuanced design, as well as through workshops aimed at gig knowledge workers themselves. The research is organized around human-centered engineering approaches that address three main challenges: collecting data on gig labor, interpreting this data, and enabling collective action based on the insights gained. For data collection, the research team will work closely with gig knowledge workers to design monitoring tools that enable the aggregation of job-related data without adding to their workload or risking violations of platform terms of service. For understanding, the team will create and evaluate tools that analyze the collected data, grouping workers based on similar work activities and challenges. These tools will also incorporate suggestions from large language models to aid in brainstorming solutions for the identified challenges. For collective action, the team will create systems that support workers in implementing the plans devised during the brainstorming sessions, ensuring the plans are carried out in a socially cohesive and accountable manner. In all three sub-projects, the team will adopt a participatory design approach, guided by social science theories of collective action and social identity and leveraging capabilities of generative AI technologies to create user-friendly, intelligent interfaces that are specifically tailored for the needs of gig knowledge workers. To study and evaluate these technologies, the research team will conduct field experiments and longitudinal deployments in the real world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The design and synthesis of new materials and processes for growth of crystals is important for technological advances. The biennial American Association for Crystal Growth Conference (West) brings together researchers in diverse areas spanning natural sciences and engineering to present their latest findings. The secluded setting of the conference offers a unique platform for exchange of ideas in fundamental and applied areas related to crystal growth. Recent trends in the crystal growth community have revealed challenges in translating crystal growth technologies to industrial manufacturing processes, and in training a diverse workforce for sustaining the innovation economy. To this end, the organizing committee for the 28th version of the conference has included an advanced manufacturing component within the conference program. The award will support the attendance of early-stage researchers and students at the conference (graduate, undergraduate and high school) with emphasis on students from underrepresented communities from universities and high schools all over the country. The award will also support the organization of a panel discussion on student career planning and guidance. The initiatives in this and future versions of the conference to help several manufacturing technologies through development of interdisciplinary projects and collaborations, and for the education and training of a new generation of diverse workforce within the crystal growth community. The organizing committee for the conference has selected topics in areas of biomimetics, biocrystallization, energy materials, environmental systems, functional materials, and fundamental aspects of crystallization. With participation from industry, academia, and national laboratories, the conference allows a unique set of collaborations that underscore the issues and solutions in fundamental and application-focused aspects of crystal growth. Within each topic, there is an emphasis on addressing challenges in scaling up the synthesis techniques to industry-scale manufacturing platforms with data-enabled AI and machine-learning based techniques. These interdisciplinary bridges are needed to realize the transformative potential for several crystal growth principles and technologies in understanding, engineering, and manufacturing of crystals. The manufacturing focus in this meeting should serve as a template for future meetings to strengthen the nexus between manufacturing and crystal growth. Recruitment efforts are aimed at building a conference program that encouraged participation of several speakers new to the crystal growth community and to the AACG society, including early-stage researchers and students from underrepresented communities. The conference program and abstracts will be disseminated through the AACG website and through AACG newsletter. 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 · 2024-05

The instrumental role of systematic gene knockdown studies, using RNA interference (RNAi), in identifying lifespan-extending gene alterations in model organisms like C. elegans is well established. In contrast, systematic gene overexpression remains mostly unexplored, representing a major knowledge gap in our understanding of the genetic basis of longevity and health. This gap exists primarily due to previous technological limitations which restricted studies to a limited selection of candidate genes. The recent advancements in CRISPR technology for C. elegans overcome those hurdles. These innovations allow rapid, conditional overexpression of specific genes simply by expressing dead Cas9 (dCas9) fused to a transcriptional activation domain in chosen tissues and feeding the organism bacteria carrying a particular guide RNA at the desired time (feeding CRISPRa). This technological leap allows for a systematic identification of genes that modulate longevity and health. This novel approach could identify a new class of genes pivotal for longevity and health, transforming our understanding of aging and disease. Our objective in this proposal is to identify new determinants of lifespan and health by extending these gene-specific overexpression tools to a genome scale. Results enabled by our labs’ collaborations and joint expertise make us uniquely well prepared to undertake the proposed research. Specifically, (i) the Apfeld lab pioneered genetic approaches to aging in C. elegans, (ii) the Levine lab pioneered systems biology approaches in C. elegans, (iii) both labs developed innovative, high- throughput approaches to study aging and resilience, including the automated Lifespan Machine scanner cluster, (iv) both labs have robust molecular biology expertise, and (v) we confirmed that feeding CRISPRa works in our hands for several genes. The rationale of the proposed research is twofold: identifying genes that prolong lifespan can pave the way for the development of new therapies to promote healthy aging and treat age-related diseases, while understanding genes that shorten lifespan can provide insights into disorders of accelerated aging and guide strategies to manage or mitigate their impact. We will accomplish these goals by pursuing two specific aims: in Aim 1 we will build a well-characterized gene-activation toolkit that enables time-dependent and tissue-specific systematic gene activation in C. elegans; in Aim 2 we will use this toolkit to identify the set of genes that modulate C. elegans lifespan and health when activated. The approach is innovative, in our opinion, because it pioneers the use of a rapid and simple CRISPR-activation technology to systematically identify new determinants of lifespan and health. This contribution will be significant because it is anticipated to provide a foundation for understanding how deliberate genetic activation can influence longevity and health, with potential applications to aging-related diseases and general health maintenance. Moreover, by equipping the C. elegans research community with a novel genome-scale toolkit for precise gene overexpression control, we are removing a major obstacle hindering the study of gene functions associated with aging.

NIH Research Projects · FY 2026 · 2024-05

PROJECT SUMMARY Emotional disorders are among the primary causes of disability and impairment worldwide. Predicting their future course can be used to improve disease management and to deliver more timely and individually tailored treatment. However, accurate forecasting is notoriously difficult. A major challenge is that the static features traditionally used for prediction (i.e., cross-sectional symptom data) are not theoretically optimal for forecasting disease progression. Overwhelming evidence demonstrates that emotional disorders are characterized by behaviors that unfold dynamically over time. A more principled disorder conceptualization is needed that emphasizes dynamic time-series features of emotional psychopathology. To address this gap, dynamical systems and complex network theory have been proposed as theoretical models that can explain the dynamic behavior of emotional disorders. In the current proposal, we will create a precision-medicine framework for predicting emotional disorder symptoms that leverages theoretically relevant time-series measures using modern “forecasting” machine learning methods. Patients with emotional disorders will be recruited to undergo a 4-week ecological momentary assessment period during which symptom and affect data will be collected 5 times per day and passive sensor data will be continuously monitored. Aim 1 will evaluate the predictive utility of “forecasting” machine learning using EMA-based dynamical systems and network features in predicting future transdiagnostic symptom domains. Aim 2 will evaluate the added predictive utility of incorporating dynamic time- series features from passive sensor data alongside EMA data in predicting future transdiagnostic symptom domains. In Aim 3, we will develop dynamic time-series phenotypes of emotional disorders using both self- reported EMA and sensor data. The current proposal will result in the first forecasting machine learning framework designed to leverage temporally dynamic features for predicting the future course of symptoms, thereby better capturing the dynamic nature of emotional psychopathology. This is significant as it facilitates more accurate monitoring and just-in-time interventions for emotional disorders, alleviating one of the major causes of disease and burden worldwide. Furthermore, a tailored training plan was created to cultivate competencies in advanced time-series computational techniques and career development, including expertise in (1) early warning signals in dynamical systems and network theory, (2) time-series machine learning (forecasting), (3) unsupervised machine learning, (4) passive sensor data, and (5) grantsmanship and transition to academic independence, with guidance from Dr. Paola Pedrelli (primary mentor), Dr. Richard McNally (co- mentor), Dr. Jordan Smoller (co-mentor), and Drs. Rosalind Picard and Amanda Baker (consultants). Research and training activities will harness the exceptional resources at Massachusetts General Hospital/Harvard Medical School. This training plan will facilitate a future research program leveraging computational modeling to advance precision medicine and personalized care for emotional disorders.

NIH Research Projects · FY 2026 · 2024-05

Project Summary As the sole producer of proteins in the cell, the ribosome is central to all cellular life. With this critical role, ribosome dysfunction can give rise to a host of human disease, which makes it a target for therapeutics. In addition, since ribosomes are organism-specific, several classes of antibiotics function by halting protein synthesis in bacteria, without interfering with the dynamics of human ribosomes. However, efforts to combat bacterial infections face multiple challenges, including antibiotic resistance and side effects. Accordingly, a thorough understanding of bacterial and eukaryotic ribosome dynamics can be used to design more effective antibiotics, as well as provide insights into the molecular origins of various diseases. In the current project, we will apply state-of-the art molecular simulation methods to study how ribosomes are able to accurately and efficiently produce proteins in the cell (i.e., translation). We will apply multiple types of simulation techniques to obtain atomistic insights into the dynamics of the conformational rearrangements that enable translation. These calculations will elucidate the precise interactions that control ribosome kinetics in bacterial and human ribosomes (cytosolic and mitochondrial). These studies will advance our understanding of ribosome dynamics, which will allow for more effective therapeutic strategies to be developed, in order to combat bacterial infections and disease. We will work towards this objective by pursuing multiple avenues of investigation. In collaboration with experimental groups, we will design and execute simulations that will specifically guide the development of next-generation single-molecule methods. Our models will also be used to understand the precise relationship between small-scale conformational motions (identified by nuclear magnetic resonance experiments) and large- scale/long-time dynamics in the ribosome. We will also use computational strategies to identify candidate small molecules that may serve as novel broad-spectrum antibiotics. Promising antibiotic candidates will then be characterized through biochemical, single-molecule and structural biology (cryoelectron microscopy) analysis, performed in well-established experimental laboratories. Finally, our collaborations with structural biology laboratories will allow us to provide the first insights into the dynamics of newly-identified functional states. In addition to immediate therapeutic applications, this broad range of efforts will help establish a comprehensive understanding of ribosome dynamics that connects experimental observables and theoretical principles, such that guiding principles may be identified.

NIH Research Projects · FY 2026 · 2024-04

Abstract The selection of a prosthetic foot that best suits an individual's unique gait patterns and mobility needs is currently an overwhelmingly challenging process. There are over 100 prosthetic feet available on the market, each with a different combination of mechanical behaviors, such as forefoot stiffness, heel stiffness, and energy return. Adding to the difficulty, the current prescription process in the US creates major barriers for patients to "pilot" multiple prosthetic feet, which often results in most patients being fitted with a sub-optimal prosthesis given their gait biomechanics and ambulation goals. Unfortunately, sub-optimal prosthesis fit can result in long-term complications such as low back pain, knee osteoarthritis, discomfort, reduced community ambulation, and decreased quality of life. Prosthetists, the clinical experts that select and align prosthetic feet to the socket, use informal systems based on both patient feedback and visual assessment of gait quality to improve prosthesis alignment—but tools and techniques for quickly finding the patient-optimal prosthesis model are currently not available. To address this issue, this project aims to develop the Ankle-Foot Optimization Tool (A-FOOT), a robotic device that can accurately and quickly replicate the behavior of any commercially available ankle-foot prosthesis. This new technology will consist of a wearable mechatronic platform that will allow the emulated prosthesis’s mechanical properties to be adjusted by prosthetists using a simple tablet-based app. Specifically, the app will enable prosthetists to make adjustments to the emulator's heel stiffness, forefoot stiffness, series damping, and parallel damping, providing them with the ability to customize the emulator's behavior to the specific needs of each patient. The device performance will be validated through a series of benchtop tests, and by comparing the just-noticeable difference, or limit of patient perception, for plantar/dorsiflexion alignment, forefoot stiffness, and hindfoot stiffness to values found with quasi-passive systems. Additionally, a pilot n=3 study will be conducted, in which three prosthetist-patient pairs will work together to select their preferred setting, and repeated testing (with random re-seeding of the start point) will provide a metric of prescription consistency. The long-term vision of this project is to revolutionize the prosthesis prescription process by enabling prosthetists to rapidly find the optimal foot model and alignment using this robotic emulator, improving the quality of life for individuals who have lost their leg, and reducing risk of long-term secondary complications. This proposal takes a major step towards realizing this future by creating and validating the mechatronic system upon which this new future will be built.

NIH Research Projects · FY 2025 · 2024-03

PROJECT SUMMARY Stimulant use disorders (e.g., cocaine, methamphetamine) are a major public health concern. Despite a heritability of ~40-50%, genome-wide association studies (GWAS) have identified very few loci, including one hit for cocaine (COC) dependence that maps to FAM53B, a gene also identified via expression quantitative trait locus (QTL) analysis to be associated with COC self-administration in mice. The primary objective is to rapidly identify novel genetic factors in rats that contribute to premorbid risk (compulsivity, impulsivity) and cocaine use traits in a spontaneously hypertensive rat (SHR) reduced complexity cross (RCC). A rodent systems genetics approach triangulates on discovery-based genetic and multi-level functional genomic analysis and can provide a more rapid genetic and neurobiological insight into drug action and neuroplasticity underlying addiction. For several years, the contact PI has been employing mouse reduced complexity crosses (RCCs) between near-isogenic inbred substrains to facilitate gene mapping, validation, and mechanisms. Because rodent substrains are > 99% genetically identical and contain several orders of magnitude fewer variants compared to classical inbred strains, mapping quantitative trait loci (QTLs) in RCCs yields orders of magnitude fewer causal candidate genes to consider. When combined with functional genomics, RCCs can rapidly lead to causal gene and variant identification. Our preliminary studies establish robust, heritable differences in premorbid impulsivity and compulsivity, sucrose reward sensitivity, and multiple COC use traits between SHR/NCrl and SHR/NHsd substrains, including COC-induced locomotor activity, COC IVSA taking, seeking, and intake cycles, demonstrating feasibility for gene mapping in an RCC. In Aims 1 and 2, we will pioneer the use of a rat RCC where we will conduct whole genome sequencing (WGS) and map behavioral QTLs and expression QTLs (eQTLs) from nucleus accumbens (NAC) and prefrontal cortex (PFC) at the whole transcript and exon levels in an F2 cross comprising COC-trained versus yoked saline (SAL)-trained rats. In Aim 3, we will conduct proteomic analysis of PFC and NAC from COC vs. yoked SAL-trained rats to triangulate on high confidence candidate quantitative trait genes (QTGs) and variants (QTVs) as we build functional connections between DNA variants, transcriptional regulation, protein translation, and cell signaling adaptations underlying premorbid and cocaine use traits. These studies pioneer the use of a rat RCC combined with deep behavioral phenotyping to rapidly identify high-confidence candidate novel genetic factors and molecular mechanisms influencing premorbid risk factors and cocaine use traits. Future gene editing of candidate causal gene variants will be modeled on the two near-isogenic SHR backgrounds to demonstrate necessity (mutation correction; “rescue”) and sufficiency (mutation induction). Deliverables include WGS’s of SHR substrains for future RCCs for complex trait analysis as well as adaptive rat transcriptomic and proteomic datasets in key brain regions of the mesocorticolimbic circuitry that can be further mined by investigators and hopefully inform therapeutics.

NIH Research Projects · FY 2026 · 2024-02

Advances in genome technologies ushered in vast cost reductions in DNA sequencing and increased read lengths, the latter afforded by development of new single-molecule sequencing technologies. As a result, much of the genome’s “dark matter” has been elucidated, and higher-quality reference genomes were made available. In addition to genome sequencing, these single-molecule methods have enabled new applications for probing chemical modifications in DNA, by either probing the kinetics of sequencing-by-synthesis using optical waveguides, or by electrically distinguishing modified bases using nanopores. Despite progress, a critical barrier in genomics is understanding the roles of RNA in biology, which demands methods for quantitative analysis of RNA molecules in a cell. The myriad of types of RNAs in a cell, their dynamic chemical modifications, and their elaborate structural and functional diversity, all hint at a tremendous level of regulation and biological significance. Traditional RNA sequencing methods have primarily relied on conversion to complementary DNA (cDNA) followed by cDNA sequencing using either high-throughput second-generation methods or third generation single-molecule methods, the latter of which offers long reads. Using these methods, some RNA modifications can be read through prior chemical functionalization of the RNA prior to conversion to cDNA (for example, m6A, pseudouridine, A-to-I editing, 1-methyluridine, and dihydrouridine). However, the chemical reactions involved in these methods are not 100% quantitative or specific, and further, detection is often done through incomplete reads due to reverse transcription blocks, which precludes detection of multiple modifications. The only available method for direct RNA sequencing, the Oxford Nanopore Technologies platform, suffers from several drawbacks that include high input requirements, limited ability to probe RNA modifications, and incomplete reads, particularly near the RNA 5’ end. We address these limitations by developing a new single-molecule method that can be scaled to allow long read direct RNA sequencing at high throughputs, all with very low input requirements of several picograms. Building on zero-mode waveguides (ZMWs) originally developed by Pacific Biosciences, we have recently developed electro-optical ZMWs (eZMWs) that allow low-input capture of DNA and RNA molecules. We demonstrated using these devices identification of DNA fragments from low inputs by rapid capture of single molecules and their flash sequencing. Together with the Pyle group, we are developing the integration of MarathonRT, an ultra- processive reverse transcriptase that converts RNA molecules to cDNA molecules with high processivity and accuracy, into our electro-optical eZMWs for direct RNA sequencing. We have already fused MarathonRT to a streptavidin protein and demonstrated its functionality in eZMWs. Here we will build on these developments to develop a direct RNA sequencing method that can detect single base edits and chemical modifications, all with high coverage from single-cell inputs of a few pg per run.

NSF Awards · FY 2024 · 2024-01

Humanoids are robots that mimic human form and function. Such robots can manuever in human-centered environments and handle human tools. This is important for dull, dirty, and dangerous tasks that are unappealing or risky for people, such as encountered in disaster response. The 2012 DARPA Robotics Challenge demonstrated humanoids mimicking first responder tasks like navigating rough terrain, climbing ladders, clearing debris, breaching walls, turning valves and driving vehicles. A decade later, these outcomes have translated into social benefits. Beyond disasters, people often suffer from tedious and strenuous work on assembly lines. The car industry is investing in humanoid robots to offset worker occupational injuries. Market forecasters thus see humanoids as a multi-billion dollar industry by 2034. However, current humanoids are still expensive, fragile, and move slowly. This demands more academic research to advance the state-of-the-art. This planning project assembles the research community to identify what is needed for the next generation of humanoids, ones that are more affordable, rugged, and moves with motions and speeds akin to people. The outline of this project's activities involves capturing and disseminating the needs of the research community. Three task forces will capture inputs from a diverse research community on (1) electro-mechanical design; (2) software architecture and control systems; and (3) mixed-reality and data-driven learning. These task forces will respectively hold hybrid workshops in universities in Lafayette (Purdue), Boston (Northeastern) and Philadelphia (Drexel). These workshops bring a diverse community in robotics, computer vision, machine learning, human-robot interaction, VR/AR digital twins, natural language understanding, brain-machine interfaces, advanced cloud and edge computing, high bandwidth communications, algorithmic and communication foundations for advanced operating systems, intuitive programming languages, and trustworthy computing. This process serves to identify both the hardware and software infrastructure the community needs to yield an affordable, durable, and customizable humanoid. Finally, the task forces will share community inputs at the flagship IEEE International Conference on Robotics and Automation (ICRA) in Atlanta 2025. The net effect will be a comprehensive list of technical design requirements. This will then be leveraged to propose a NEW or Enhance/Sustain (ENS) Medium or Grand infrastructure grant within the next 2-years. 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.