Harvard University

NSF Awards · FY 2024 · 2024-10

This project will pilot a model for developing more ethical and equitable use of genetic tools through active engagements between scientists and society to guide the design, development, and deployment of genetic technologies. Genetic technologies, from genome sequencing to CRISPR gene editing, have far-reaching applications and implications for humanity and the planet. These intricate questions go beyond the immediate interests and influence of individual scientists and companies. Given the rapid pace of research and market dynamics, multidisciplinary community expertise is needed to examine the ethics of genetic technology innovations, considering both the intended and the unintended consequences. This project uses participatory design, actively involving stakeholders in the research design process, to provide scientists and technologists with new tools for working collaboratively with those impacted by the technology, to engage diverse perspectives, improve outcomes, and ensure maximum beneficial impact. By facilitating cross-sector and cross-perspective collaboration, the project will have the broader impact of producing novel insights and solutions to shape these rapidly evolving technologies, and to address societal challenges they may bring up early on. The project will examine whether a social impact accelerator model, that brings genetics scientists together with community leaders, communications experts and policymakers that have not typically been part of the scientific design process, can enhance understanding and yield unique insights to cultivate an ethical genetics future. This project will build an interdisciplinary team of experts in cross-sector genetics engagement (Personal Genetics Education & Dialogue program based in the Department of Genetics at Harvard Medical School) and social impact designers who lead participatory design accelerators nationally. Together, the team aims to (a) pilot and externally evaluate an early-stage participatory design accelerator model to mobilize cross-sector leaders—policymakers, funders, genetics innovators, filmmakers and community leaders who have historically been marginalized in scientific research and discourse—to emerge with recommendations on how to advance responsible design, development, and deployment of CRISPR-based innovations; (b) co-create a strategic plan to scale the genetics accelerator model nationally; and (c) disseminate early-stage findings through publications and a scientific conference presentation. A rigorous third-party evaluation will produce a process and recommendations for scaling and disseminating the accelerator model that will inform a potential Phase 2 project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Limb regeneration is a complex biological process not fully understood at the genetic level. Salamanders are the only vertebrates with limbs that can completely regrow a lost limb. However, some fish, like the African lungfish and the grey bichir (Polypterus), can fully regrow their fins, even if they are cut off at their base. This ability is not found in commonly studied fish such as zebrafish. The proposed research will use a multilayered, comparative approach, looking at salamanders, lungfish, and Polypterus to identify the key elements needed for limb and fin regeneration. The hypothesis being tested is that these species deploy a shared genetic program of regeneration. First, this proposal addresses whether a specific molecular signaling (the mTOR signaling pathway) is a common feature of both limb and fin regeneration. Next, a comprehensive dataset of gene expression information will be obtained from the animal models to search for a shared set of genetic and cellular tools for regrowing limbs and fins. Finally, DNA elements that control gene expression during limb and fin regeneration will be identified and the hypothesis that loss of the ability to regenerate is linked to changes in how tissues control gene activity will be tested. These studies using multiple species will help reveal general mechanisms that control the complex process of regeneration. This project will train researchers at multiple academic stages, from undergraduates to postdoctoral researchers. Outreach to middle school students will provide research opportunities to underrepresented populations and therefore contribute to broadening participation in STEM. Limb regeneration is a prime example of a complex biological trait for which the genetic and genomic underpinnings are poorly understood. Although salamanders are the only limbed vertebrate that can regenerate the entire limb, fishes such as the African lungfish (Protopterus annectens) and Polypterus fully regrow fins even when the amputation occurs at the very base of the fin, across the proximal endoskeleton. This ability to regrow entire fins is lacking in traditional fish models such as the zebrafish. This proposal uses a phylogenetically-informed, multi-scale approach, using the axolotl, the lungfish and the Polypterus, to identify the core components of a shared “toolkit” of limb and fin regeneration. The first aim of the project tests the hypothesis that a rapid activation of an mTOR-mediated translational program is a conserved feature of limb and fin regeneration and identifies transcripts differentially translated during the early event of wound closure that marks the onset of regeneration. The second aim is focused on the integration of bulk, single nucleus and spatial transcriptomics datasets to determine if our animal models activate an evolutionarily shared genetic and cellular “toolkit” for appendage regeneration. In the third aim, epigenetic profiling will be deployed to reveal shared gene regulatory networks of limb and fin regeneration and test the hypothesis that loss of regenerative capacity is associated with widespread divergence of tissue regeneration enhancers. The multi-species, systems-level studies proposed here will bring the field closer to uncovering the general mechanisms governing the complex trait of regeneration. This proposal is co-funded by the Division of Integrative Organismal Systems (via the EDGE program and the Developmental Systems Cluster), The Division of Emerging Frontiers, and the Division of Environmental Biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Summary: Many cellular processes are triggered by ligands such as ions, small molecules, peptides/proteins (e.g., hormones, cytokines), and viruses. However, rapid cellular adaptations that follow stimulation often fall in the blind spot of microscopy: limitations of spatiotemporal resolution prevent detailed exploration of nanoscale cellular dynamics. Motivated by the need to address this technological barrier, we aim to develop a biophysical tool that will allow stimulating biological cells with various chemical or biological compounds and then freezing these cells for subsequent analysis by high-resolution microscopy. We refer to this technique by an acronym: Vitrification Experiment Resolved In Time After Stimulation (VERITAS). Broad utility of this new method in biological research is envisioned, including investigations concerning the dynamics of cellular membranes and membrane-protein interactions, the dynamics of protein-protein and protein-nucleic acid interactions, and the dynamics of post-translational modifications. In addition, the need for the proposed instrument is supported by a user survey that we conducted. Our approach is conceptually innovative because it will permit imaging of nanoscale cellular dynamics with millisecond temporal resolution and because it establishes a connection between time-resolved cryo-vitrification and super-resolved fluorescence imaging. The approach is methodologically innovative because it will feature millisecond-level control over the stimulation-to-vitrification time, and reliable chemical stimulation of cells. Moreover, we will develop new protocols to integrate cryo-vitrification and nanoscale optical microscopy, and to validate the technique against live optical microscopy measurements. In Aim 1, we will develop the time-resolved cryo-vitrification instrument by first achieving millisecond-level control over the stimulation-to-vitrification time and then developing a method for precise chemical stimulation of the sample. This work will complete our instrumentation development efforts and allow proceeding to the validation stage. In Aim 2, we will develop post-vitrification imaging and data analysis protocols and use these procedures to validate VERITAS using two representative biological systems. These studies will test the boundaries of utility of the method because they use different types of cells and different imaging methods. Based on our preliminary data, the project is feasible. Our efforts will establish the groundwork for future biological studies using the proposed time-resolved cryo-vitrification technique. We suggest that this is significant because time-resolved cryo-vitrification will provide detailed molecular and structural understanding of how cells respond to a variety of stimuli of relevance both to normal physiology and disease. Furthermore, the proposal includes detailed plans for technology dissemination via direct collaboration, open-source sharing, commercialization, and integration with local instrumentation facilities.

NIH Research Projects · FY 2024 · 2024-09

We propose to develop a synthetic biological system to manipulate subcellular RNAs and proteins in growth cones or synapses of subtype- and context-specific neurons. This system has both direct, powerful near-term experimental-investigative potential and future potential toward novel, uniquely specific forms of therapeutics/ bioactive delivery. The systems enable unique forms of subcellular functional investigation in subtype- and stage- specific neuronal circuitry, offering generalizable modularity for manipulation of RNA and/or protein localized to developing growth cones during circuit formation and to presynaptic compartments in maturing and mature circuitry. Subcellular functions of proteins regulating connectivity and (dys)function of subtype-specific circuitry are poorly understood. Based on successful preliminary studies, we propose to develop these tools for in vivo perturbation of molecular abundances by integrating subcellular localization motifs with inducible gene expression systems. This system will enable discovery of RNAs/proteins that function subcellularly to control overall organization, precision, and function of distinct neural circuits, and how they are dysregulated in disease. We aim to integrate motifs that selectively traffick proteins to GCs and/or presynapses with an inducible gene expression system, enabling subcellular-specific overexpression via direct motif fusion, and knockdown via motif fusion to hfCas13d, a refined mRNA degrader. We will use data from recently developed experimental and analytical approaches purifying subtype-specific GCs or presynapses and their parent somata or nuclei from developing or mature mouse cortex, and quantitatively “mapping” RNAs and proteins between these subcellular compartments. This enabled identification of molecules with subcellular localizations specific to subtypes and stages, and/or to disease, and measuring of local translation. However, such candidates can function in multiple subcellular domains, at multiple distinct stages/contexts. Current approaches for whole-neuron perturbation of molecular abundances produce off-target effects, complicating investigation of subcellular-specific RNA/protein function, thus preventing elucidation of molecular mechanisms regulating circuit formation and (dys)function. We aim to develop approaches for subcellular- and stage-/context-specific manipulation of molecular abundances in developing and mature circuits. In Aim 1, we will develop a system to subcellularly manipulate RNA/protein abundances in growth cones that regulate distinct developmental stages of circuit construction. In Aim 2, we will develop a system to subcellularly manipulate presynaptic RNA/protein abundances later in maturity that regulate synapse function, maintenance, and plasticity, thus are linked to memory, synaptic weighting, and circuit function. We will employ presynaptic-localizing motifs in mature circuits. These systems will enable elucidation of subcellular mechanisms controlling circuit formation and function; investigation of how subcellular dysregulation might cause developmental, degenerative, and neuropsychiatric disorders; and potentially toward novel therapeutics– circuit- and subcellular domain-specific, to minimize off-target effects.

NIH Research Projects · FY 2024 · 2024-09

Project Summary The diversity of cell states observed across development, aging, and disease is regulated by the epigenome, an intricate medley of chromatin states, chemical modifications, protein interactions, and three-dimensional structures in the nucleus. We and others have developed techniques such as ATAC-seq, CUT&Tag, and In situ Genome Sequencing (IGS) that use Tn5 transposase to selectively measure different components of the epigenome, increasingly at single-cell resolution. However, these techniques all suffer from poor yield due to an inherent challenge: the amount of physical space available for enzymatic reactions in the nucleus is limited. To address this challenge, we propose a new platform called Expansion Genomics, which will leverage physical expansion of biological samples to de-crowd the nucleus, vastly increasing the resolution of single-cell and spatial epigenomics methods. In Aim 1, we describe Single-cell Expansion Genomics, which will combine expansion with modern single-cell techniques to enable high-throughput, multi-modal measurements without the need for specialized microfluidic devices. In Aim 2, we describe Spatial Expansion Genomics, which will integrate expansion with multiplexed spatial imaging approaches to reveal the organization of the epigenome at nanoscale. These technologies will be designed with ease and accessibility as a guiding principle to facilitate widespread adoption and broaden our understanding of gene regulatory mechanisms.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Recent technical advances have enabled to record the activity of hundreds or thousands of neurons simulta- neously. To extract insight from this deluge of data, computational neuroscientists have turned to data-driven modeling approaches. In this paradigm, an artificial recurrent neural network (RNN) is first fit to imitate recorded activity, and is then dissected to reveal putative mechanisms. A prominent goal of this approach is to uncover the dynamics of computation in the trained RNN, including attractor structure that defines how the network could accumulate a signal or stably store a memory. However, a firm theoretical understanding of these RNN models is lacking. In particular, are they guaranteed to uncover true mechanisms, or can they find spurious structure? How does the RNN architecture chosen affect how they learn to imitate observed dynamics? The proposed research aims to resolve these foundational gaps in our understanding of a widely used approach to extracting insight from high-dimensional neural data. First, it aims to establish the fundamental domain of validity of recovering attractor structure from data by developing benchmarks that can be applied to any data-driven method. This represents a shift in how data-driven models are evaluated, from focusing on their ability to explain variance in test data to instead demanding that they robustly uncover underlying mechanisms. Second, it aims to advance our ba- sic understanding of how RNNs learn to mimic observed dynamics. Using the powerful toolkit of modern deep learning theory, I aim to build a more complete theory of how network architecture and training procedure interact to bias how an RNN imitates real neural dynamics. In total, this research will elucidate the limitations of one of the most popular approaches for extracting understanding from large-scale neural data, and advance our basic understanding of how recurrent computations are learned.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Despite decades of research on reading disabilities, the efficacy of existing interventions for improving reading in upper elementary students (Grades 3 – 5) remains limited. This is a significant public health concern as reading is a strong predictor of lifetime earnings, general health, and overall wellbeing. One significant limitation of the extant research has been the narrow focus on designing treatments that address solely students’ reading problems without adequately responding to other factors known to impact learning. To enhance the effectiveness of reading interventions, we propose investigating the benefits of integrating approaches that hold promise for improving reading outcomes. Attention difficulties are a critical target for such an integrated intervention, as a substantial proportion of students with reading difficulties (25-40%) experience elevated levels of inattention, which impedes their response to reading interventions. Despite substantial evidence linking attention difficulties with reading problems, researchers have largely overlooked attention as a target in reading interventions. This study is innovative in its approach of integrating evidence-based practices for attention and reading within a single, unified intervention called Supporting Attention and Reading for Kids (SPARK) for an understudied population, students with co-occurring reading and attention difficulties in Grades 3 - 5. Our study will address three specific aims. The primary aim is to evaluate the effects of the SPARK intervention on reading and attention immediately post-intervention and over time (two subsequent school years). We propose conducting a randomized control trial with participants assigned to one of three-conditions: SPARK, reading- only, and a documented typical practice comparison. This proposed 3-arm design will allow for examining the relative effects of a researcher-provided integrated intervention (reading and attention supports) with a researcher-provided reading only comparison and then also a typical practice comparison condition. Our second and third aims will identify factors that influence the strength of the intervention (moderators) and the mechanisms of action that drive changes in student outcomes (mediators). Collectively, these aims will contribute to theoretical understanding of the relations between reading, attention, and other related factors (e.g., processing speed, working memory). The findings will also have significant clinical implications that address a prevailing and significant public health issue: reading disabilities.

NSF Awards · FY 2024 · 2024-09

Earthquakes are powerful and unpredictable forces of nature, capable of causing immense destruction and loss of life. Despite advances in understanding the Earth's movements, predicting earthquakes remains a challenge. This project aims to revolutionize the field of earthquake science by using artificial intelligence (AI) to unravel the mysteries hidden within seismic data – the vibrations caused by earthquakes. Imagine an AI system that can "read" the unique signals from each earthquake, much like a detective deciphers clues. This AI, trained on massive amounts of data, will learn to recognize patterns in seismic waves, revealing details about the earthquake's source and the path the waves took through the Earth. By gaining a deeper understanding of these patterns, scientists can develop more accurate tools for earthquake monitoring and potentially even predict earthquakes with greater accuracy. This advancement will be crucial for preparing communities, improving early warning systems, and ultimately saving lives. The knowledge gained from this project could also be applied to other natural hazards, such as volcanic eruptions and landslides, further enhancing our ability to understand and prepare for these events. This research project proposes the development of a foundational AI model for advanced seismic data analysis. The model will be trained on a vast archive of seismic data to identify and characterize earthquake signals, utilizing cutting-edge AI techniques like transformer models. This will involve the development of specialized neural network modules optimized for seismic data and the implementation of a modular and sparse multi-path framework for efficient waveform analysis. The project will focus on improving earthquake detection, localization, and characterization, with potential for broader applications in geophysics. By fostering collaboration between AI experts and geoscientists, the project will contribute to the training of a new generation of interdisciplinary researchers. Importantly, the findings, tools, and computational resources from this project will be made available to the scientific community through open-source platforms, promoting transparency and facilitating further research and innovation in the field of earthquake science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The project aims to develop innovative artificial intelligence (AI) tools to study polyhedra, which are fundamental in various fields such as combinatorics, discrete geometry, and optimization. The complexity of polytopes makes it challenging for researchers to gain insights and draw connections between their structures and properties. This project addresses this challenge by leveraging AI to enhance mathematicians' abilities to generate polyhedral samples, discover new conjectures, and conduct rigorous reasoning on polyhedral geometry. This research is significant as it not only advances the mathematical field, but these innovations are expected to significantly advance the understanding and application of polyhedral geometry in various scientific and engineering domains, as well as advance the potential of AI for mathematical reasoning. The project also supports education by creating tools that can be used in teaching. Additionally, the project promotes diversity and inclusivity in STEM by engaging underrepresented groups through workshops and mentoring programs, thereby inspiring a broader range of students to pursue careers in these fields. The technical scope of the project includes developing new methods for data generation, knowledge discovery, and formal reasoning in polyhedral geometry. The project will use AI techniques such as diffusion methods and reinforcement learning to create diverse, high-quality polyhedral samples. A key innovation is the development of Polyhedral-GPT, which integrates large language models to provide clear, interpretable outputs using a polyhedral transformer. The project also aims to enhance computational efficiency by combining fast, informal AI techniques with rigorous formal verification. Additionally, a black-box interpreter will automate the translation of polyhedral knowledge into natural language, minimizing human intervention and streamlining the process from conjecture generation to formal proof verification. The integration of optimization, algebraic geometry, and SAT solvers will further facilitate automatic proof processes, contributing to the project's overall efficiency and accuracy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Summary/Abstract The brain evolved to move the body, i.e., to implement sensorimotor control. Understanding the brain, then, is inextricably linked to understanding how it coordinates the joints and muscles of the body to generate competent behavior in dynamic and unpredictable environments in the service of goals. A tantilizing consequence of this is that understanding sensorimotor control will shed light on overall brain organizational and computational principles, including, potentially, higher-level cognition, which evolved much more recently by adapting the circuits already in place to control movement. This promise has not yet been realized, however, because standard approaches to studying motor control seek to reduce complexity by, for example, isolating simple circuits, studying artificial tasks, or constraining movements. These approaches thus avoid the sine qua non of motor control in biology: multiple interacting brain regions, multiple simultaneous goals, and multiple muscle coordinations, all in the presence of many sources of noise and sensory delays. Here, we propose to embrace this complexity rather than reduce it and are enabled to do so through the use of 'virtual rat' models that comprise deep neural network controllers designed to be analogous to biological brains and biomechanically accurate bodies that are instantiated in simulators with real physics. Using a high-throughput easy-to-use ‘virtual neuroscience’ platform that we are developing for our own use, and the use of the broader research community, we will train these models to imitate freely-behaving real animals such that they internalize the statistics of naturalistic behavior and then train them to solve goal-directed tasks. This novel ‘deep neuroethology’ approach has two crucial features: highly biologically realistic behavior and the full transparency of a model. We will then apply this approach to generate and test longstanding hypotheses about motor control and learning. For example, we will interrogate: (i) how the learning and execution of complex behavior are influenced by certain circuit motifs such as laterality, reciprocal inhibition between antagonistic muscle pairs, feedback architecture, sensor delays, cortical–subcortical interactions, and dopamine-mediated plasticity; (ii) how feedforward outputs and feedback inputs – in the setting of noise and sensory delays – coordinate movement; (iii) how animals learn to adapt their behaviors quickly such that they can generalize across novel environments and tasks; and (iv) what roles the distinct neural representations and circuit motifs found throughout the hierarchy of the motor system play in neural computation. The results of these studies will drive previously unachievable refinements to our theories of sensorimotor control and will thus spur new research directions in motor neuroscience and, potentially, in robotics and other fields. Finally, and perhaps most importantly, we will have demonstrated the power of virtual neuroscience, inspiring future similar research programs, potentially using virtual animals of many species, to probe the mysteries of neural computation.

NSF Awards · FY 2024 · 2024-09

Atoms and molecules are the microscopic building blocks of the world. Their behavior and interactions are governed by the theory of quantum mechanics, which describes at a fundamental level much of modern science and technology. Advancing quantum science and technology with atoms and molecules requires cooling to temperatures around one millionth of a degree above absolute zero and exquisitely controlling their structure in the quantum mechanical realm. Over the past several decades, powerful laser cooling techniques have been developed to reach these “ultracold” temperatures with atoms; in turn, a number of discoveries were made that shed light on the intricacies of quantum physics in complicated systems and made progress toward the creation of a useful quantum computer. These techniques have more recently been extended to diatomic molecules (containing two atoms), and very recently to larger, “polyatomic” molecules. Now, Professor John Doyle and his research team of graduate and undergraduate students and postdoctoral researchers will use laser-cooled CaOH (calcium monohydroxide) molecules to study the complex physics governing polyatomic molecules at ultracold temperatures. There are two primary aims of the research. The first is to study collisions of ultracold CaOH molecules, which will shed light on the quantum physics underlying molecular interactions and ultracold chemistry. The second is to control arrays of CaOH molecules at the level needed to build a quantum computer, both by controlling individual molecules in the array and by engineering their interactions. The resulting quantum computer could be used for powerful quantum simulations, e.g. for the development of new technological materials. Additionally, students will be trained in advanced, highly technical experimental methods, adding to the scientific human infrastructure of the nation. Professor Doyle and his research team will carry out this research using ultracold CaOH molecules in optical traps (bulk optical dipole traps as well as individual molecules in optical tweezers), using experimental techniques recently developed by the team. They will study the collisions of triatomic molecules in detail and develop techniques for quantum control of collisions, in particular by using the specific structure of polyatomic molecules to shield molecules from lossy short-range interactions, with the vision of carving a path towards a degenerate quantum gas of polyatomic molecules. Direct tests will be made of theory, leading to a better understanding of the relatively unknown territory of quantum-controlled collisions of polyatomic molecules. Researchers will also develop methods to control polyatomic molecules in optical tweezers for use in analog quantum simulators and as qubits in quantum information processing systems. The specific goal is to first characterize the coherence time of potential qubit states that take advantage of the unique structures present in polyatomic molecules. Professor Doyle and his research team will then use dipolar interactions to entangle CaOH molecules in adjacent optical tweezers and use these interactions to engineer quantum gates between polyatomic molecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Quantum mechanics has unlocked countless technologies that benefit society, including lasers and precise measurements of time that enable the precision of GPS. These technologies rely on the quantum properties of atoms and molecules as though each particle is in isolation, whereas technologies reliant on the entanglement between particles are still in development. Exciting applications include quantum simulators, which may discover new materials with quantum properties, such as the absence of electrical resistance. Under a prior award, the PI and her team developed techniques to engineer individually controlled interactions between molecules. The current project will expand the number of individually controlled molecules and use these to model simple quantum materials for calibration, so that ultimately the team will be able to study useful complex materials. This process of pursuing new techniques in the control of quantum particles will also train and educate students and postdocs, contributing to the quantum workforce in industry, academia, and national labs. Furthermore, training will be enhanced by a new modern course on quantum molecular physics to be developed by the PI. Quantum simulation has excited many in the AMO and quantum science community with the idea that arrays of controllable, interacting particles can reveal the physics of correlated many-body systems by reproducing their Hamiltonians. This project aims to create a quantum simulator of spin dynamics consisting of a configurable array of ultracold dipolar molecules, assembled atom by atom and held in individual optical tweezers. In particular, the PI and her students will use Floquet-engineered microwave pulse sequences to tune the molecular Hamiltonian to match that of quantum materials like the antiferromagnetic bosonic t-J model, the XXZ spin model, and synthetic dimension lattices. Properties, and the efficiency of producing these quantum phases, will be studied. Supported by a prior award, the research group has prepared molecules in single quantum states and their rotation has been coherently controlled. In this new work the research team will entangle the rotation of adjacent molecules, and develop new microwave pulse sequences to control molecular rotation in order to engineer target Hamiltonians. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Nontechnical Description: Optical metamaterials are a class of materials that leverage features smaller than the wavelength of light to engineer their optical transmission characteristics. Optical metamaterials have already enabled transformative applications, including flat lenses, optical cloaking, and super-resolution imaging. Due to these broad successes, researchers are now developing active optical metamaterials: metamaterials whose optical transmission characteristics can be changed on the fly, for example, at high speeds (gigahertz) enabled by electrical circuits used to control them. In this project, we aim to leverage a new class of active metamaterials to improve the energy efficiency of large-scale computing applications running in data centers, such as training large language models (LLMs) for artificial intelligence (AI),and performing scientific computing workloads. Specifically, we will leverage active metamaterials to improve the energy efficiency of optical data communication in datacenters, by using electrical control signals for high-speed reconfigurable communication between large networks of computing and memory systems. For many of today’s data centers, the overheads of data communication limit the overall power and performance of computation, often referred to as the “communication wall” or “memory wall”. Using active metamaterials to improve energy efficiency of communication, will directly translate into energy efficiency benefits for humanity’s largest computing applications. Technical Description: We propose a disruptive technology for high-performance optical switches based on optical active metamaterials – optical metamaterials whose transmission characteristics can be electrically modulated – for improving the energy efficiency of data communication in large-scale data centers. We are targeting communication in data centers, since overall computing performance is often limited by data communication overheads within the large networks of sub-systems that comprise today’s data centers, including racks of general-purpose processors, application-specific hardware accelerators, and memories. Thus, improving the energy efficiency of communication directly improves the energy efficiency of computing. In this project, we will explore the benefits of active metamaterials to enable a new class of energy-efficient Active Metamaterial Optical Switches (AMOS), targeting high-performance communication and computation in data centers. We will explore three areas of focus: (1) Design and simulation of AMOS devices, considering three potential device structures (details in the full proposal), which we will compare based on their relative power, performance, and area. (2) Experimental fabrication of AMOS devices in our cleanroom (Harvard’s Center for Nanoscale Systems),and experimental measurements to calibrate our device models. (3) System-level projections to quantify the benefits of our AMOS devices for overall power consumption and execution time of computing applications in data centers. Importantly, our analysis will account for interactions between AMOS devices and the power/performance overheads of electrical circuits required to modulate them. This is essential for realistic performance projections of real-world applications. If successful, AMOS devices will enable strictly better trade-offs in switching time, latency, bandwidth, power consumption, and physical size of optical network switches. The resulting energy efficiency benefits of communication will translate directly into energy efficiency benefits of computation for large-scale data centers employing AMOS devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Understanding the environmental conditions that Earth experienced over the course of its lifetime is a story recorded in ocean sediments and rocks deposited over that time. In certain rare instances, those repositories offer a glimpse of what that ancient environment (and, in particular the atmosphere) looked like. It has been proposed that a certain type of oceanic mineral (called sulfate evaporites) directly reflects the composition of atmospheric O2 – critical to life on the planet – at the time it forms. Through this work, the researchers will explore the biogeochemical and microbial process behind the production of these sulfate minerals to better understand their role as a time capsule for the development of the atmospheric conditions conducive to life as we know it. This work will involve both detailed microbiological experiments paired with novel geochemical measurements and will provide interdisciplinary training opportunities for two doctoral students. Further, the researchers have partnered with Salish Kootenai College to provide immersive summer research internships for multiple Native undergraduate students at both research institutions. On geologic timescales oxidative weathering or minerals on land regulates atmospheric CO2, O2, and Earth's redox budget. Reconstructing these temporal records then falls to proxies, and of interest here, a powerful record of the triple oxygen isotope composition of sulfate minerals. As conceived, the isotope composition of sulfate can be used to assay paleo-atmospheric compositions and global biogeochemical features like gross primary production. The thread that ties all this together is the requirement for oxygen atoms in tropospheric O2 be transferred to sulfate through the oxidative weathering of pyrite minerals. Although seemingly true in the Proterozoic, new data now reveal that this string is broken with the dawn of land plants. The researchers posit that this fundamental change in Earth's sulfur cycle is buried in the details of pyrite oxidation itself, which has recently been shown to be mediated by microorganisms in circumneutral pH conditions. They propose to conduct targeted microbial experiments to untangle the physiologic and isotopic consequences of the steps involved in microbial pyrite oxidation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to estimate fault slip rates across Japan over the past 25 years using geodetic data and computational models. Combining three-dimensional fault system models with statistical tracking techniques, Meade, Loveless, and their students will estimate daily slip distributions on major fault zones in Japan. This comprehensive model will help to show how fault movements are interconnected over time and space, and how slip rates vary with time between large earthquakes. This research is inspired by detailed geodetic observations since the 1990's that revealed complex earthquake processes, such as episodes of fault slip that are too slow to cause ground shaking. The research findings will provide insights into the motions of fault systems, contributing to improved earthquake hazard assessments in Japan. The analysis and modeling codes being developed as part of this project will be made freely available to scientists studying complex fault systems elsewhere in the world. The project will provide training in earthquake science and technical computing for a graduate student and several undergraduates at Smith College and Harvard University. This study aims to image fault slip rates throughout the Japan fault network by combining high-resolution spherical three-dimensional kinematic block models with state-space estimation approaches that link past, current, and potential future distributions of fault slip with data uncertainties. This approach will yield estimates of daily slip distributions on the Japan-Kuril, Sagami, and Nankai subduction zones and crustal fault system with a unified model that includes a consistent representation of fault system geometry, GPS station locations, and estimation approach across the entire observational era spanning nearly three decades. The result of this study will be a more complete characterization of earthquake cycle kinematics not as isolated interseismic, coseismic, postseismic, or slow slip processes but as part of a continuous spectrum, which will enable assessment of spatial and temporal links among earthquake cycle behaviors. Specifically, the work will probe the extents of spatiotemporal variation in 1) coupling patterns prior to and following large subduction zone earthquakes; 2) rates and degrees of partitioning of deformation across the crustal fault system through time, drawing connections to paleoseismic constraints on Quaternary fault activity; and 3) the potential for aseismic slip on subduction zones to trigger and/or arise from nearby deviations in fault activity, including variations in coupling and the occurrence of earthquakes. These investigations will form the bases for characterizing the frequency spectrum of earthquake cycle activity, the spatial and temporal overlaps across different earthquake cycle stages, and improved understanding of the fundamental behaviors that inform earthquake hazard analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT The COVID-19 pandemic precipitated intermingled health and economic shocks, which were felt deeply in the service-sector. The service-sector workforce is large, with nearly 20% of the U.S. workforce employed in retail, grocery, food service and related sectors, and is predominantly low-income and disproportionately women and people of color. These underserved workers bore the brunt of the pandemic’s unemployment shocks, with potentially dire consequences for health disparities across the life course. However, the economic crisis was met by a safety net expansion that could have buffered these negative health consequences. For those who remained employed, many found themselves on the frontlines of the pandemic, staffing grocery stores, pharmacies, and fulfillment centers. Adherence to coronavirus mitigation practices, including masking and staying home when sick, shaped the further course of the pandemic as workplaces such as grocery stores and pharmacies, remained essential, but risky, locations for the public, especially older community dwelling adults. Community-dwelling older service workers’ risks depended on both their own adherence to mitigation strategies as well as that of their younger co-workers and their customers. Yet, there are gaps in knowledge about how the safety net response buffered any negative health consequences of unemployment for these underserved workers and about how both public and company masking and paid sick leave policies shaped adherence to coronavirus mitigation practices in service sector establishments. To fill these gaps, we propose to draw on novel employer- employee linked data we collected as PIs of The Shift Project from 153,175 service-sector workers surveyed via 12 repeated cross-sections 2017-2023 and 42,734 person-interview observations in three longitudinal panel surveys 2019-2023. We propose to use these data to accomplish three key aims. First, we match the Shift Project micro-data with contextual data at multiple levels to construct time-place varying measures of safety net generosity, workplace risk environments, and company policies. We will prepare a harmonized and integrated data file that meets FAIR standards to encourage discovery and use of the data. Second, we use quasi- experimental methods to estimate the degree to which the safety net buffered negative effects of unemployment on the health of underserved workers. Third, we examine the effects of both public and company paid sick and masking policies on adherence to coronavirus mitigation practices and how these effects varied by age. In sum, we propose to harmonize, link, and disseminate innovative and timely large-scale data for a population subgroup that is underserved and vulnerable to COVID-19 shocks and in a setting of considerable relevance for public health, and to generate rigorous evidence on the buffering effect of the safety net on unemployment’s negative health effects and provide a novel view into the role of both public and company policies on adherence to mitigation practices in the workplace across the life course.

NSF Awards · FY 2024 · 2024-09

This project will fund research that strives to enable swimming robots with novel capabilities customized to a specified range of objectives and environments. Fish, shaped by hundreds of millions of years of evolution, display a diversity of body structures and neural circuits in response to ecological pressures. This project will build upon modular representations of these evolutionary solutions to implement a robot design process emulating natural selection. The project envisions design features grouped into the following three categories: (i) external flow sensing, decision-making, and power management, (ii) body and fin actuation, shape and internal state sensing, and buoyancy control, and (iii) body and fin shape and compliance control. Automated printing and packaging will allow rapid prototyping of candidate robots from this design space. Each robot will undergo physical tank trials using reinforcement learning to develop control policies for a set of characteristic movements, including speed and acceleration of turning, forward, backward, and sideways motion, and energy efficiency during sustained forward motion, which will then be evaluated by physical flow testing subject to an anticipated range of operating conditions. Candidates will compete against each other to accomplish movement-based tasks in relevant flow conditions, with high-scoring designs selected as the starting point for the next round of testing, and low-scoring designs eliminated from further consideration. After multiple such rounds, the winning configurations will be equipped with fluid flow sensors, gyros, and accelerometers, and will learn decision-making and feedback strategies for choosing and blending individual motion primitives to effectively achieve higher-level guidance and navigation objectives. This work will accelerate the application of intelligent underwater robots to address national needs and grand challenges, including search and rescue, disaster recovery, pollution and ecological monitoring, and infrastructure inspection. Associated outreach and STEM education efforts include developing a plug-and-play robot kit and a science class at the Harvard Museum of Natural History. This research will create modular robotic swimmers capable of artificial evolution, to enable novel swimming capabilities such as stable swimming in turbulent flows, navigation towards wakes of underwater objects, performing stable rheotaxis, and dynamic energy savings via real-time adjustment of robot body and caudal fin stiffness and shape. The project will first modularize fish-inspired robotics to create a Modular, Mutational, Morphing Underwater Robot (M3UBot) design space. Next, asynchronous evolution will be performed directly in the physical M3UBot design space for evolving body morphologies and learning motor control programs for modular swimming behaviors (e.g., rapid turning, acceleration, steering, forward or backward swimming). The large-scale robot evolution in physical space and the “plug-and-play” robot assembly will be enabled by innovating 3D-Printing and Electronic packaging (3DPE) for rapid design and automatic fabrication of M3UBot modules. Finally, selected prototypes from the evolved M3UBot population will be equipped with hydrodynamic pressure sensors; they will then undergo reinforcement learning for feedback control and decision-making that combines modular behaviors to navigate in challenging hydrodynamic conditions. Together, this project will transform the fundamentals and applications of underwater robotics, culminating in next-generation intelligent robotic swimmers capable of hydrodynamic perception, active shape morphing and stiffness tuning, and versatile motor skills in challenging hydrodynamic conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Current artificial intelligence (AI) and machine learning (ML) technologies do not match the adaptability and efficiency of biological systems. This project will integrate brain organoids with soft, bioelectronics to create a system capable of complex computational tasks. These systems will inspire novel AI algorithms, enhance data processing efficiency, and advance machine learning beyond current capabilities. Ethical considerations will guide all research activities to ensure that the project aligns with the highest standards and complies with current laws and regulations. This project will impact AI, bioelectronics, and bioengineering by enabling precise understanding of cellular activity and scalable bio-computation. The collaborative, multidisciplinary approach integrates expertise from bioengineering, electrical engineering, mechanical engineering, ML, statistics, and control theory. Educational efforts will include seminars, undergraduate research, K-12 outreach, and curriculum development emphasizing interdisciplinary learning and bioethics. Programs at Harvard and UT Austin will recruit and mentor students, focusing on underrepresented communities. K-12 outreach will involve science mentorships and online content sharing on bio-inspired computation and AI integration with biology. This project will develop seamless integration of neuron-soft brain organoid-computer interfaces for long-term 3D neural network computing through following thrusts: (A) developing neuron-soft bioelectronics with over one thousand electrodes for seamless 3D integration with brain organoids. This thrust will implement mechanics-driven soft bioelectronics design and 3D microfabrication to enable long-term, stable recording and stimulation of cellular activities. (B) modeling and facilitating the maturation and specialization of brain organoids with bio-inspired online learning and sensing-AI-actuation loop. This thrust will utilize embedded sensors and actuators, along with reinforcement learning algorithms, to create a closed-loop system that generates spatiotemporally evolved stimulation patterns as feedback to the recorded signals, which can direct the maturation and specialization of brain organoids with precise sub-regional and functional specificity. (C) providing a novel framework that integrates the computational power of ML and brain organoids. This thrust will design task-specific stimulation patterns, deliver stimulations through embedded actuators, and receive the organoids’ responses via embedded sensors, which will be further processed by the downstream ML algorithms for efficient and effective computation. (D) conducting in-depth ethics research, integrated with the science research and experiments, applying the method of Collaborative Ethics throughout the research process. This thrust will include conceptual as well as applied ethics research, ensuring adherence to the highest standards of responsible conduct of science in compliance with current laws and regulations pertaining to all stages of the research process. This project is jointly funded by the Emerging Frontiers in Research and Innovation Program (BEGIN OI) and the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Natural history museum specimens vary tremendously in size and preservation style, ranging from giant squid kept in vats of ethanol, to butterflies pinned in trays, to ticks mounted on glass microscope slides. At the Museum of Comparative Zoology (MCZ) at Harvard University, the collections of Invertebrate Zoology, Malacology, and Entomology house about 50,000 slide-mounted specimens. While restoration and digitization efforts have improved access to macroscopic specimens, those mounted on glass microscope slides are in dire need of rehabilitation and updated record keeping. By creating digital records of these slides and making the data available online, the specimens and their biological data will be made discoverable to researchers and the public worldwide, and fixing and rehousing them will ensure that these slides are available for posterity. Methods developed as part of this project for slide handling and restoration will be shared with other museums to expand the impact of this work. This project will engage the public through an exhibit at the Harvard Museum of Natural History, a platform to showcase the global importance of our research and the insights that slide-mounted specimens provide. This project will also offer paid internships for undergraduate students, providing them with hands-on experience in the curation of biological collections. Slide-mounted specimens enable examination of morphological structures of invertebrates, and also serve as a long-term preservation method for whole or parts of animals. Slides in the three living invertebrate collections at MCZ suffer from a range of problems: 1) the lack of standardized storage systems, with many slides stored in trays and drawers scattered throughout the collections, 2) decaying mounting media that are sensitive to environmental conditions, necessitating restoration efforts, and 3) absence of any digital record in a database, which combined with the distributed storage of slides, makes it difficult to discover and locate specimens. To address these challenges, this project will locate all slide-mounted specimens, repair slides as needed, rehouse them in modern cabinets to ensure long-term preservation, and create digital records of each slide. High-quality pictures will be taken of the approximately 3,000 slide-mounted type specimens. These are of particular value to the scientific community because each type is the physical specimen from which a species was originally described. All specimen data acquired in this project will be made publicly available via the MCZbase database, which also connects to repositories such as iDigBio, GBIF, and the InvertEBase Symbiota portal. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Explosive objects in the distant universe can now be studied by simultaneously combining information from multiple messengers - gravitational waves, particles, and light. The investigators will develop software to deliver new discoveries and physical constraints concerning the nature of explosive objects. The investigators will provide students opportunities for cross-institutional internships and collaborations with amateur astronomers and citizen scientists. The research, methods, and visualizations will be directly included in developing courses at multiple institutions. The work will provide training for students in critical areas for astrophysics and beyond, including robust application of machine learning. The team will partner with the LIGO Science Education Center and The Baton Rouge: Bringing Youth Technology, Education and Success programs to utilize multimessenger astronomy to inspire K-12 students in the state of Louisiana. A 4-year research program led by investigators at the Louisiana State University, Harvard University, University of Minnesota-Twin Cities, and University of Maryland, College Park will improve our understanding of explosive transients. The exotic zoo of explosive transients is still being explored, and the overlap of signals seen at different wavelengths is key to their taxonomy. Explosive transients occur at the extremes of physics, beyond the reach of terrestrial laboratories. Multiwavelength and multimessenger observations of these transients enable advances in areas including gravity, fundamental physics, dense matter, cosmology, and the origin of the elements. The proposed work will enable new discoveries through the power of the Vera Rubin Telescope with concurrent observations provided by high energy and gravitational-wave observatories. The research team will combine observations of compact objects with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time with space-based gamma-ray burst monitors and ground-based gravitational-wave interferometers. Focusing on gamma-ray bursts and supernovae, the team will construct new optical transient classifiers, develop the formalism to associate distinct signals across wavelengths and messengers from the same event, characterize these events through dedicated follow-up, and enable global discovery via public alerts. The result will be an end-to-end multiwavelength and multimessenger discovery machine. 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

Title Video Disease Activity Index: a video measure to monitor rheumatoid arthritis in telemedicine Project Summary/Abstract Rheumatoid arthritis (RA) is a degenerative autoimmune condition of the joints that is treated with medications that suppress the immune system. Medications must be regularly adjusted according to disease activity, based on clinical examination. Telemedicine has gained a key role in rheumatology during the COVID-19 pandemic, with rheumatologists amending treatment based on patient reports of disease activity and signs of swelling observed on video. However, numerous studies have proven that telemedicine may miss critical information that affects how disease activity is treated. The proposed project aims to improve the accuracy of assessing disease activity and functioning in RA during telemedicine visits by using laptops and smartphone standard cameras. This will also help reduce the costs associated with objective evaluation for RA. The overall goal of this exploratory and developmental R21 project is to assess technical feasibility and patient usability of camera- based remote assessment system. The first aim is to develop a web-based system that leverages computer vision to quantify joint range of motion and joint thickness as an indication of joint swelling in RA to determine disease activity. We also aim to modify and improve the current method of assessing functional impairment by incorporating isometric grip strength using an in-house squeezable ball. During this first phase, the vision-based system and the squeezable ball will be validated on young and older adults through comparison with gold- standard techniques (e.g., motion capture). The second aim is to evaluate a new scoring system called the Video Disease Activity Index (VDAI) in a cross-sectional feasibility investigation with RA patients (n = 50). The VDAI scoring system will be produced by quantifying joint range of motion and joint thickness to determine the number of swollen joints. This will provide a measure of disease activity that aligns with the clinically endorsed Clinical Disease Activity Index (CDAI), which will be measured by a rheumatologist in the clinic. Two tests of the VDAI will be conducted: one with a researcher present to evaluate the system's sensitivity and reliability against clinical examination (CDAI), and another where RA patients will use the web-based application alone, while still in the clinic, with a vision-based feedback algorithm to perform the required activities. As the VDAI and CDAI are on the same scale (0–24), the study will use joint-level power calculations to permit standard limits of agreement analysis between the two measures (e.g., Bland-Altman). The correlation between grip strength and functional questionnaire scores will also be evaluated using Pearson's tests. These analyses will provide a robust evaluation of the sensitivity and reliability of the VDAI system and its potential to improve the accuracy of assessing disease activity and functioning in RA during telemedicine visits. By leveraging ubiquitous cameras, this investigation has the potential to significantly increase the quality of disease activity and functioning in RA during telemedicine visits.

NSF Awards · FY 2024 · 2024-08

This project supports the development of more efficient and sustainable machine learning methods using inherent structure in the data. Structured data arises in many scientific and industrial applications, including relational structure in complex social and biological systems, hierarchical structure in information and language systems, as well as symmetries in scientific data that derive from fundamental laws of physics. The project aims to develop methods for identifying, characterizing, and leveraging such structure in machine learning and data science applications. Research findings will be incorporated into graduate courses and graduate and undergraduate students from potentially diverse backgrounds will be mentored as part of this project, contributing to the training of the next generation of applied mathematicians. In addition, ideas and concepts with direct relation to the proposed research will be incorporated into STEM outreach activities with the goal of sharing the research findings with the broader community. The project aims to develop a novel computational framework for leveraging geometric structure in data that is applicable to settings without pre-existing knowledge on data geometry. Geometric representation learning will be formalized as a model selection problem, where the respective geometric characteristics are learned from data. The project’s results will contribute to the field by providing a systematic analysis of the benefits of geometric machine learning methods compared to classical Euclidean approaches. With that, the project aims to develop a deeper theoretical understanding of geometric machine learning and offer practical, empirically validated guidelines for the application of such methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project aims to serve the national interest by investigating conditions under which mathematics departments adopt and adapt evidence-based instructional materials for improving STEM education. To date, most research into dissemination of modeling curricula focuses on barriers and failures. This project plans to complement this work by studying successful adaptation processes at multiple sites and will produce an Evolution Blueprint. The Evolution Blueprint will summarize successful case studies and serve as a tailored resource to support faculty and institutions seeking to adopt the modeling curriculum. The focal instructional materials are from UCLA's Life Sciences 30 course, which teaches the importance of modeling feedback loops, positive and negative, in ecology, physiology, and molecular biology, all without a calculus prerequisite. The project seeks to examine the adaptability of these materials to new settings and deepen the field's understanding of how evidence-based curricular materials can be successfully disseminated. By understanding the conditions that facilitate successful dissemination off innovative curricular materials, future work can put them in place. In this way, the project contributes to improving the introductory mathematics experience for life science majors. The project will bring together mathematicians, mathematical biologists, and educational researchers to address a critical problem in STEM education related to the dissemination and uptake of evidence-based curricular materials and pedagogies. The project aims to achieve three goals: (i) identify and understand the contextual features that facilitate or impede successful implementation of a modeling-based curriculum, (ii) advance understanding of how ongoing, dynamic institutional conditions shape the adaptation of a modeling curriculum in response to local needs, and (iii) lay the groundwork for a multi-site impact study by developing assessment tools to measure broader learning outcomes. The project will draw on qualitative methodologies to document stakeholders' perceptions of the impact of the instructional materials, including the experiences of mathematics and science faculty, teaching assistants and tutors, and students. The project will develop local causal models that explain how and why aspects of the curricular materials are adapted to suit the instructional context. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

Planets form in disks around young stars. Disk chemistry affects the outcome of planet formation in multiple ways, including elemental composition and access to water and prebiotically interesting organics for habitable zone planets. The chemistry of outer disk regions, beyond the water snowline, where comets and most giant planets likely form, can be studied using molecular emission lines at millimeter wavelengths. Molecular lines also offer some of the best probes of disk gas mass, density, temperature, and ionization. This project will use the Submillimeter Array to conduct a series of broadband line surveys towards 40 different disks. This project will form the thesis work of a graduate student. The team will also participate as YouthAstroNet STEM mentors and will answer student questions about astronomy as well as STEM career pathways. The research team will survey 120 GHz of bandwidth between 210 and 370 GHz at high spectral resolution towards 40 disks. They have already obtained 120 tracks (nights) of SMA time to pursue a ~120 GHz spectral line survey towards 40 disks around solar mass T Tauri stars and Herbig Ae stars in clusters ranging from <1Myr to >5Myrs. The team will use the survey data to address the C/O elemental ratio, quantify D/H ratios, and obtain a comprehensive inventory of the organic reservoir in the comet-forming disk regions. A grid of astrochemical models using the disk chemistry code DALI will be run to determine abundance structures, which will be converted to column density profiles and disk-averaged column densities and column density ratios for easy comparison with the observations. This unique data set will be highly significant for interpretative frameworks of exoplanets habitability and the origins of solar system volatiles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY: Axolotl salamanders are model organisms offering researchers unique opportunities to explore fundamental mechanisms underlying a variety of biomedically-relevant phenomenon. The Harvard Axolotl Facility (HAF) houses one of the most robust research colonies of axolotls in the U.S. This is a shared-use facility overseen by Dr. Jessica Whited. This proposal requests support to modernize the HAF, increasing its efficiency and capabilities by the addition of three double-sided flow-through racks for axolotl aquaria featuring real-time water quality measuring and adjusting functions. Axolotls have limbs which are anatomically and functionally similar to human limbs, yet axolotls can completely regenerate limbs throughout life. Elucidating how axolotls regenerate limbs will provide critical clues for scientific efforts aimed at therapeutic limb regeneration in human patients in the future. The precise genes whose activity enables limb regeneration can now be defined with the advent of appropriate experimental tools and genomic resources in axolotls. On the Harvard campus, axolotls are being used collaboratively to address fundamental questions such as: potential metabolic trade-offs associated with appendage regeneration (Dr. George Lauder); connections between regeneration and cancer (Dr. Brian Haas); how stress pathways intersect with stem cell activation following amputation (Dr. Isaac Chiu), and central nervous system regulate— or respond to—limb regeneration (Dr. Jia Liu). Our use of axolotls goes beyond limb regeneration. Researchers are using HAF’s axolotls to study the evolution of novel sensory traits (Dr. Nicholas Bellono), evolutionary aspects of pain sensation (Dr. Will Renthal), and evolution of skeletal morphologies such as pelvis (Dr. Stephanie Pierce). Thus, the HAF is truly a multi-use facility that has supplied a variety of labs with high- quality research organisms for study. Axolotls are permanently aquatic, and they are also cannibalistic. To generate and grow axolotls to appropriate sizes for experiments, they must be bred in-house and raised in separate enclosures, ensuring they are completely naïve to injury at the onset of studies. We are also generating targeted alleles of axolotls and transgenics. These animals must be raised to adulthood to establish lines, yet the axolotl generation time is one year. Thus, there is significant need for axolotl housing in the facility that is standardized and robust in the face of environmental fluctuations. Automation is key to the management of a modern axolotl facility, yet equipment for automation can be expensive and difficult to fund from traditional sources. Equipment requested will provide hundreds of individual enclosures for live axolotls to live with minimal environmental fluctuation and minimal human caretaker intervention, thereby simultaneously improving rigor and reproducibility while also improving efficiency. Together, these improvements will greatly enhance the rigor, reproducibility, and efficiency of research programs on campus using the axolotl model system.