University Of California Santa Barbara

NSF Awards · FY 2024 · 2024-09

The deep sea is an epicenter of biogeochemical cycling that is globally important but poorly understood. Big data generated by emergent gene sequencing technology provides a new avenue to link genes with biological processes. In the deep sea, the vast majority of genes are unknown. This project will focus on methane seep systems. New microbial samples will be collected from methane seeps off the coast of Oregon and Washington. This research will employ a novel natural language processing artificial intelligence approach to predict what these unknown genes do. This will be a critical step toward quantifying oceanic ecosystem function based on genomics. The artificial intelligence models developed using these samples will be broadly applicable. They can provide a foundation to answer many questions across scientific fields ranging from ecology to human health. A tutorial for the models developed will be written and workshop run to explain the techniques. Further, artists will be involved in the research and a documentary will be produced to spread the results of the research. This research will build two new artificial intelligence models to use gene sequence data to understand ecosystem processes, and apply them to methane seep habitats. A new model incorporating genes and ribosomal amplicon co-occurrence will code genes and classify them into pathways. In parallel, generative models with text and sequence protein representation will be developed. Models will identify putative genes responsible for each of the cycles identified, or dl-genes. These two models will be applied to new samples collected from methane seeps offshore Oregon and Washington. Methane seep habitats are areas where methane is consumed by microbial activity and are also areas with strong redox gradients leading to diverse methane and nitrogen over a small spatial area. Both artificial intelligence models will be applied to these habitats, and the results used to empirically validate the dl-genes by testing if the dl-genes are transcribed when the associated geochemical process is observed. The main outcome will be a scalable approach with artificial intelligence that will advance key questions in earth system science. To broaden the use of the methods developed in this project to solve similar problems, a tutorial and workshop will help others learn and use the models. Further, the results of this work will include exhibits by artists involved in the research as well as producing a documentary about how artificial intelligence can harness big data to help advance the understanding of earth 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 · 2024-09

The BisQue Deep Learning (BDL) cyberinfrastructure (CI) project is set to transform scientific research across multiple fields, including materials science, environmental science, and bioimaging. Utilizing cutting-edge deep learning and computer vision techniques, the BDL CI offers a scalable, user-friendly platform for the management and analysis of vast, complex datasets. This initiative tackles significant challenges such as meticulous data curation, specialized domain expertise, and the need for scalable solutions for high-dimensional data. By facilitating scientific discovery and innovation, the BDL CI significantly enhances national scientific capabilities. Furthermore, it supports education and diversity through comprehensive training programs, making advanced analytical tools accessible to a wider research community and thereby promoting the progress of science. The BDL-CI provides a sophisticated cloud-based service with an intuitive web interface designed for analyzing extensive, unstructured datasets. It supports advanced functionalities such as spatio-temporal annotations, object detection, segmentation, localization, classification, and tracking, all underpinned by a robust database backend that ensures data integrity and provenance. The infrastructure is built for scalability and efficiency, supporting dynamic resource allocation, complex workflow orchestration, and high-volume data management. Core deliverables include a comprehensive software infrastructure tailored for multimodal imaging data, detailed documentation, a suite of deep learning workflows, and an accessible interface for discovering and utilizing data and models. This project, driven by a multidisciplinary team from UC Santa Barbara, UC Riverside, and the Smithsonian Institution, ensures broad access and long-term sustainability through strategic collaborations and the integration of community feedback into ongoing development. 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 High-quality semiconducting materials have been the driving force for the information and computing revolution in the past century. The rapidly increasing integration density, operational speed and power of electronic devices require highly efficient dissipation of heat from operating devices in order to reduce the risk of overheating and thermal failure. This project aims to develop a next-generation semiconducting material – cubic boron arsenide (BAs) – with intrinsically high electrical and thermal conduction properties. Preliminary studies have suggested that BAs can conduct heat at least 10-times better than silicon, in addition to having superior electrical conduction and optoelectronic properties. Despite its promising properties, currently, high-quality BAs can only be made in very small crystals using an inefficient growth method. The research team plans to develop new methods to grow BAs in form factors that are relevant for practical applications, such as thin films and large single crystals, and understand how defects can modify its properties. In addition, the research team focuses on developing a co-design platform to optimize material property and device design simultaneously using artificial intelligence. This study aims to demonstrate prototypal BAs devices building upon these fundamental advancements. This project also supports educational and research activities to train the next-generation semiconductor industry workforce with combined skills in theory and experiment. The team plans to achieve this goal by directly training graduate student researchers, incorporating research progress into new hands-on courses, hosting undergraduate researchers with a diverse background, and engaging industrial partners. Technical description The overarching goal of this collaborative project is to develop BAs as the next-generation semiconductor that combines an ultrahigh thermal conductivity, high bipolar charge mobilities, and a long hot photocarrier lifetime for microelectronic and optoelectronic applications. The project aims to bridge the knowledge gap between fundamental electron and phonon interaction properties in BAs and their impact on coupled electrical and thermal transport and practical device applications. The core strategy to achieve this goal is rooted in the principle of material/device codesign enabled by a "digital twin" of BAs-based devices that is powered by physics-integrated deep learning. To complement this platform, the research team plans to develop a mesoscopic modeling framework to establish theoretical understanding of electrical and thermal transport properties in BAs, synthesize and characterize high-quality bulk crystals and thin films of BAs with state-of-the-art techniques including high-pressure flux growth and molecular beam epitaxy, and obtain experimental data and knowledge that can feed back and refine the digital twin. Furthermore, the research team aims to develop novel experimental methods capable of directly probing electron and phonon interaction with point and extended defects at the atomic level and systematically examine doping strategies and heterostructures to enable practical device applications of BAs. The project paves the way for BAs to become a practical new semiconductor material and provides fundamental insights into transport and defect physics in emerging semiconductors with unusual electron and phonon structures. 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 · 2024-09

SUMMARY Psychomotor-stimulant Use Disorder (PUD) is a chronic relapsing disorder, characterized by a high propensity for relapse even during protracted abstinence. In both humans with PUD & animal models, the intensity of cue- elicited drug craving & drug-seeking behavior increases or “incubates” during protracted withdrawal. The neurochemical underpinnings of drug craving & its incubation are not well understood. Drug cue-induced increase in metabolic hyperactivity within the medial prefrontal cortex (mPFC) is correlated with the intensity of drug-craving in humans. Consistent with this, we have reported a link between the magnitude of drug-seeking in a rat model of cocaine-taking & an increased activational state of several kinases, including mTOR, Akt, PI3K & PKCε. My dissertation work also identified increased indices of calcium-calmodulin-dependent kinase II (CaMKII) activation within the prelimbic cortex (PL) subregion of the mPFC, that appears to be selective for cocaine as it is not observed in rats exhibiting incubated sucrose-seeking. CaMKII has long been considered central to long- term plasticity implicated in learning/memory & substance use disorders. Despite this, no published report has examined the role for CaMKII activation within PFC subregions in cocaine-craving, let alone its incubation during protracted withdrawal. CaMKII can be activated by the influx of calcium inhibition through calcium-permeable ionotropic glutamate receptors, including NMDA and GluA2-lacking AMPA receptors, which I propose are activated by the cue-sensitized glutamate release in cocaine incubated rats. Activated CaMKII can regulate AMPA and NMDA receptor signaling and synaptic plasticity, which may drive the cocaine-incubated state. The objective of the F99 phase of this proposal is to investigate the role of NMDA-CaMKII in the mPFC in incubated cocaine-seeking using neuropharmacological techniques. The proposed project will also help the candidate, Ms. Laura Huerta Sanchez, achieve her career goal of becoming an independent investigator at a top-tier academic/research institution. This project provides training in valuable research techniques, including immunoblotting, cytology & histology & neuropharmacological approaches. Further, the proposed studies will provide professional & technical training to prepare the candidate to successfully transition to a postdoctoral position (K00) in a laboratory that studies the neural circuitry driving vulnerability to drug-seeking behavior. The complete plan proposed here for both the F99 and K00 phases has been designed to develop an independent neurobiologist prepared for a transition to a successful postdoctoral position and, ultimately, independent tenured investigator.

NSF Awards · FY 2024 · 2024-09

This project explores the relationship between commercial satellite launching and underrepresented minority communities. The study investigates how intensified commercial satellite launching is impacting local Indigenous groups, farmworkers, and incarcerated persons who live and work nearby. In the process, the project explores relations between aerospace, agriculture, and prison sectors in the community, local public education and workforce development, and environmental and public health effects of launch noise and emissions. The project’s significance is grounded in its integration of diverse community perspectives in understanding and evaluating the local effects of satellite launching. The study supports public knowledge of federally subsidized launch infrastructure and satellite technologies and provides collaborative research and educational opportunities for community members and university students. Reports, graphics, and publications from the study will be publicly available on the project website. Leveraging partnerships with community organizations, the study uses ethnographic methods to conduct focus groups, interviews, and correspondence programs that convey how minority communities think about and perceive increasing satellite launches in their midst. The major goals of the project are to: 1) understand how satellite launching impacts both the local community and the global satellite industry; 2) investigate the social, cultural, and environmental impacts of commercial satellite launching; 3) learn how members of minority communities think about the base, satellite launching, aerospace, and STEM education; and 4) draw on qualitative data to theorize how sociotechnical relations of adjacency, diversity, and cosmology alter understandings of launch infrastructure and satellite technology. The study will contribute to research on satellite technology and infrastructure, the aerospace sector, and population demographics and technology. 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

People from every nation, young or old, are filled with wonder at the night sky and invariably ask “Are We Alone?” We still don't know the answer! Over 5000 exoplanet detections have been confirmed in the last two decades, but finding a planet and knowing if it has life on it are very different things. To find life we need to go beyond the techniques that have allowed us to find these thousands of distant planets and develop instruments and techniques that let us detect the spectral features that are the signposts of life: oxygen, water vapor, methane, ozone, maybe even chlorophyll. In this project, the investigators will upgrade a powerful instrument for detecting and characterizing exoplanets called the MKID Exoplanet Camera. They have also started a YouTube channel, @ExperimentalAstrophysics, to show the world what it is like to design, fabricate, and use astronomical instruments. They will document their progress at: https://www.youtube.com/channel/UCfhgvAdHDxXEzX0E8OX4c8A/ Over the last half decade, the investigators have constructed and operated the MKID Exoplanet Camera (MEC), a z through J band (800-1400 nm) Integral Field Spectrograph (IFS) located behind the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) at the Subaru Telescope on Maunakea. MEC uses Microwave Kinetic Inductance Detectors (MKIDs) as the enabling technology for high contrast imaging. MEC is the first permanently deployed near-infrared MKID instrument and is designed to operate both as an IFS and as a focal plane wavefront sensor in a multi-kHz feedback loop with SCExAO. MEC was designed nearly a decade ago using the best MKID technology of the time, but in subsequent years, the team has made significant MKID breakthroughs. Most notable is an improvement in the spectral resolution at 1 micron from R~5 to R~20 (R~35 at 400 nm) in recent lab measurements. This project will upgrade the MEC instrument to use this latest MKID technology, boosting the spectral resolution and array fill factor. This is especially timely as SCExAO has recently been upgraded to use a new 3000 actuator first stage deformable mirror (DM), which allows the full use of the 2000 actuator DM inside SCExAO for coherent differential imaging to reach unprecedented contrast levels at small inner working angles. Students will be a key part of the development program, helping to train the next generation of instrument PIs, and the team have also started a YouTube channel, @ExperimentalAstrophysics, to show the world what it is like to work in an Experimental Astrophysics lab. This channel will document the process of building and installing MEC’, and this work involves undergrads from UCSB's Film and Media studies department as editors. 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

A BioFoundry is a facility that enables breakthroughs in understanding biological systems by eliminating bottlenecks in research. This is achieved primarily by automating processes that researchers perform manually. A collaborative team from UC-Santa Barbara (UCSB), UC-Riverside (UCR), and CalPoly-Pomona (CPP) will establish the NSF BioFoundry for Extreme & Exceptional Fungi, Archaea and Bacteria (Ex-FAB). ExFAB will house unique equipment and infrastructure that enable researchers to understand how microbes that thrive in extreme environments function. Traits of these microorganisms include novel pathways, proteins, and structures. These represent an untapped resource for biotechnological advances. ExFAB will also nurture the next generation of life science and biotechnology leaders through hands-on training and educational outreach activities. ExFAB will accelerate the translation of products, processes, and intellectual property from the biofoundry to support start-up ventures and industrial use in Central/Inland California and nationwide. A collaborative team from UC-Santa Barbara, UC-Riverside, and CalPoly-Pomona will establish the NSF Biofoundry for Extreme & Exceptional Fungi, Archaea and Bacteria (Ex-FAB). Ex-FAB focuses on organisms that exist in under conditions that would exceed the limits of adaptability for most organisms. These conditions include extremes in temperature, pressure, salinity, pH, or radiation levels. As such they defy our current understanding of biology. Traits of these microorganisms include novel pathways, proteins, and structures. These represent an untapped resource for biotechnological advances. ExFAB user facilities will be built to establish 3 interconnected research hubs that serve both in-house and external research projects: (1) bio-prospecting and microbial library generation, (2) genotyping and phenotyping, and (3) rapid prototyping. Computational tools to design cross-species genetic engineering tools and the computational power to analyze the large data sets generated at the biofoundry support the hubs. Systems will be built to accomplish three major objectives. First is to characterize novel bio-prospected organisms from the environment (e.g. soil, gut, ocean). The second will automate phenotyping of fungi, anaerobes, and microbial consortia for bioremediation and bioproduction. The third is to prototype engineered microbial systems for direct application in biotechnology, sustainability, and agriculture. ExFAB will also train and attract researchers and users from the 23 campuses of the California State University system through a master’s student education program. Hands-on training, as well as remote participation options to learn automation in biology along with machine learning, will be offered through a summer school program. 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 · 2024-09

Modified Project Summary/Abstract Section Youth today face a high risk for serious mental health problems (e.g., suicidality, depression, anxiety), which is exacerbated by significant barriers to mental health care. mHealth interventions have been developed to address the needs of youth and their caregivers, but are underutilized. Lay health worker (LHWs) are individuals who use their lived-experience, language and/or culture to support patients and/or families in mental health service access and engagement. In response to PAR-22-109 and NIMH Strategic Objective 4: Strengthen the Public Health Impact of NIMH-Supported Research, we seek to increase mental health care access for youth by enhancing the dissemination of evidence-based mHealth interventions through the design of a training intervention for LHWs – Getting Access on the INternet (GAIN). Specifically, we will use human centered design (HCD) to adapt a mental health provider training intervention (developed by MPI Price K23MH124670) based on data from MPI Barnett (R01MH117123-02S1) on the needs of youth and their parents receiving LHW services. The proposed R21 study involves working with community partners (i.e., youth, their parents, and LHWs) to (Aim 1) co-design GAIN - a mechanism-driven training intervention for LHWs, then (Aim 2) build the LHW training intervention via usability testing. This study will result in an acceptable and refined training intervention ready for testing in a large, multisite R01 study.

NSF Awards · FY 2024 · 2024-09

Brown dwarfs, often referred to as "failed stars," occupy a fascinating middle ground between stars and planets. As these objects age, they undergo a dramatic transformation known as the L/T transition, during which their atmospheres and observed properties change rapidly. This transition is crucial for understanding the evolution of brown dwarfs, as well as for drawing parallels with many imaged exoplanets, which often resemble these brown dwarfs. This research team will illuminate the mysteries of the L/T transition using the technique of polarimetry. Graduate and undergraduate students will gain hands-on experience in modeling, observation, and data analysis. The team will also develop educational resources, including a website and interactive tools, to engage and inform high school and university students about polarimetry and its applications in astrophysics. The L/T transition in brown dwarfs is marked by rapid atmospheric changes that affect their color and variability in light. This project seeks to address the underlying causes of these changes by using polarimetry to provide new constraints on the models of brown dwarf atmospheres. The research will involve creating a comprehensive grid of models that incorporate both cloud formation and chemical processes, assessing their impact on both flux and polarization signals. This grid will be instrumental in interpreting both existing and new polarimetric observations. The primary goals are to refine the understanding of the L/T transition and to develop tools that will aid in the design and analysis of future polarimetric observations of brown dwarfs and exoplanets. The project includes a significant training component, supporting two graduate students and 1-2 undergraduates annually at UCF, JHU, and UCSB. The team will develop a dedicated website and educational materials to promote the technique of polarimetry and its applications, thereby fostering greater engagement with the broader scientific community and the public. 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

Long term research- the continued measurement of a set of variables that build towards addressing a research goal over decadal scales- has played a unique role in environmental biology. Long term research has revealed dynamic ecosystem processes and informed management decisions across the U.S. and beyond. The Long Term Ecological Research (LTER) Network has enabled many important discoveries at individual sites. By synthesizing across multiple sites and biomes, general patterns of ecosystem functioning at regional to continental scales can be discovered. Working at these spatial and temporal scales requires additional coordination and dedication to synthesis activities. The LTER Network Office (LNO) catalyzes synthetic research, education, and engagement activities across the twenty-seven sites of the LTER Network. The LNO creates the collaborative space needed to share, challenge, and test new approaches to synthesizing and learning from LTER data and other types of long-term data. The LNO adds value to long term research by: (1) training and engaging LTER graduate students and early career scientists in synthesis, team science, and reproducible methods; (2) assisting LTER sites to become more accessible, safe and welcoming for students, staff, and investigators from all cultural and economic backgrounds; (3) facilitating the synthesis of existing long term data; and (4) maintaining robust connections with other research networks. The LNO fosters shared visioning and governance essential for maintaining collaborative relationships within the network. The LNO also works with partner organizations to ensure that ecological science continues to provide important and useful perspectives. The LNO delivers a suite of demonstrably successful synthesis and coordination activities. The LNO enables synthesis working groups for 1-2 years; shorter-term hybrid-model synthesis groups; the network website and newsletter; and databases of site information, participants, and products. LNO activities focus on four key needs: (1) technical and interpersonal skills related to team synthesis for early career researchers, (2) identification of practices that build an inclusive, safe, and supportive culture for research, (3) new toolkits and mentoring communities as well as staff to help implement these practices across the LTER Network, and (4) connection of LTER participants with each other and with the broader environmental biology research community to promote synthesis and shared practices. 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

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Prof. Thuc-Quyen Nguyen of the University of California Santa Barbara will lead a research project focused on developing a new class of sustainable carbon-based semiconductors, named conjugated polyelectrolytes (CPEs), which could be used in electronic devices like smartphones, sensors and solar panels. Unlike traditional semiconductors made from silicon, CPEs can be processed using environmentally friendly methods, such as water-based solutions at room temperature. This approach will help reduce the negative environmental impact of semiconductor manufacturing. The project aims to enhance fundamental understanding of how the structure of CPEs affects their property and performance in various devices, which could lead to the development of more efficient and cost-effective electronics, paving the way for breakthroughs in healthcare, neuromorphic computing, and energy storage. Additionally, the research will contribute to educational and outreach programs, engaging students and teachers from diverse backgrounds in cutting-edge science and promoting STEM education in the local community. This research project aligns with the goals of the US CHIPS and Science Act, supporting the development of sustainable semiconductor technologies and contributing to the American leadership in semiconductor research, production, and workforce development. The project will investigate the impacts of different chemical structures and processing conditions on the self-assembly, morphology, and electronic and ionic transport properties of CPEs using a combination of characterization techniques including dynamic light scattering (DLS), atomic force microscopy (AFM), cryogenic transmission electron microscopy (cryo-TEM), X-ray scattering, four-point-probe, and impedance spectroscopy. The combined set of studies will provide a thorough understanding of structure-function-property relationships, leading to fundamental guidelines for synthetic chemists to design a new generation of CPEs with tailored transport properties for various emerging technologies. 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

Active dynamics encompasses a wide range of collective behaviors exhibited by flocks of birds, schools of fish, layers of cells, and networks of filamentous proteins. In all these examples, out-of-equilibrium organization emerges spontaneously from interactions among active agents that consume energy from an internal reservoir or derive it from their surroundings. This project supports the development of scalable and automated software to simulate active dynamics and to infer the laws that govern it from the analysis of state-of-the-art experimental data, such as high-resolution microscopy videos. The goals of this project are to empower researchers to reliably extract hidden rules from noisy experimental observations of active dynamics, lower the barrier for analyzing large microscopy videos, reduce the time-consuming reimplementation of simulation and estimation, and promote cross-disciplinary collaborations. The dynamics of active matter, such as collections of fibroblasts or epithelial cells, is intrinsically stochastic and out of thermal equilibrium, and affected by a variety of complex processes, such as cell division. The large intrinsic fluctuations present in active matter systems hinder the efficient extraction of signals from noisy experimental data and thus it urgently demands the development of data-science-enabled tools to accelerate the analysis and improve the reproducibility of the findings. The development of the Dynamics Lab ecosystem addresses this urgent need by establishing two classes of interconnected software packages for real-space and scattering-based analysis of microscopy data. Together, these tools enable visualization and integration of physics-based simulations and statistical machine learning in both real space and Fourier space. Furthermore, to address the practical concerns of data sharing, such as size limit, this project supports the development of an efficient paradigm for data acquisition of active dynamics, where large raw data are stored offline, and small online data sets that sufficiently capture the raw data set are easily transferred and used for most research purposes. A major goal of Dynamics Lab infrastructure is to achieve sustained impacts on basic and applied sciences, ranging from biophysics to biomimetic materials. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research and the Division of Mathematical 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

With the support of the Chemical Synthesis program in the Division of Chemistry, Professor Trevor Hayton of the University of California Santa Barbara will study the development of the triphenylmethyl (trityl) group as a carrier for a diverse array of molecular cargo. The cargo molecules include enzyme intermediates, mammalian signaling molecules, and metal atoms. The trityl forms weak carbon-element bonds due to the high stability of its free radical form, thus it is expected to release these cargo molecules under mild conditions. These cargo molecules will be used to study chemical processes relevant to the global nitrogen cycle, mammalian bioregulation, and the synthesis of magnetic nanomaterials. This research will provide excellent training to undergraduate and graduate students in the synthesis and characterization of air-sensitive inorganic and organometallic clusters and complexes, preparing them for careers in academia and industry. In addition, Professor Hayton will develop a new undergraduate organic chemistry laboratory describing the synthesis and characterization of the trityl radical. This laboratory will introduce undergraduate students to free radical chemistry, which is infrequently covered in the undergraduate chemistry curriculum, but is finding an increased prominence within the modern chemical sciences. This project will explore the synthesis and development of triphenylmethyl-containing small molecule delivery agents for a variety of applications. The triphenylmethyl (trityl) group is stable in its neutral (radical) form, which enables facile small molecule release upon application of external stimuli, such as oxidation and photolysis. Specific targets include delivery agents for carbon disulfide and sulfur monoxide. The former is a poorly-understood mammalian gasotransmitter, whereas the latter is a proposed intermediate in the nitrous oxide reductase catalytic cycle. Professor Hayton will also develop trityl-containing carrier molecules for vanadium, chromium, and cobalt, which will be used to generate new magnetic materials and metal nanoclusters. These materials are of interest for a variety of applications, such as memory storage and quantum computing. The Hayton group will achieve their research goals via new synthetic methodology development, spectroscopic characterization, and magnetism measurements. 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 Award will fund a research project to investigate whether post-conflict economic recovery programs to reestablish livelihoods and rebuild local economies alleviate one of the root causes of conflict---low social cohesion---by increasing economic interdependence, increasing intergroup contact, and generate economic activity that increases the opportunity costs of crime. This project will study these dynamics in an economic recovery program spearheaded by the United Nations in an environment where years of conflict has led to significant social tensions. The focus of the study is on how these programs indirectly affect social cohesion through individuals’ social networks. The researchers therefore will engage in painstaking data collection of social network data for the analyses of these indirect channels. The research will inform the broader understanding of how aid to rebuild livelihoods impacts sociopolitical development and the prevention of conflict relapse after civil war. The indirect mechanism studied will provide input to guide the development and implement more efficient policies. This Award will fund a research project that seeks to understand whether and how post-conflict economic recovery programs affect economic opportunities and social cohesion in the communities. The project will use a randomized controlled trial (RCT) methodology to investigate three interrelated research questions: (i) How do economic livelihood programs improve or erode social cohesion between hosts, returnees, and internally displaced persons (IDPs)? (ii) how do these programs have broader effects on displacement-affected communities through social and economic networks? (iii) When traditional approaches to estimating spillover effects are not available due to program features, costs, or ethics, what do feasible alternatives recover? The PIs will answer these questions by conducting a series of innovative and well-designed RCTs in a large post conflict area. The answers to these questions have the potential to inform both research and program design, especially of US foreign aid policy, in numerous contexts where similar social dynamics or interventions exist. 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

Seismic tomography plays a central role in our understanding of the substructure of the Earth. The analysis of various seismic data produced by natural earthquakes or artificial seismic sources has important applications in practice, such as characterizing fractured bedrock, and searching for oil and gas deposits. The study of seismology also has a close connection with the transport theory in classical mechanics, which models the behavior of a large number of particles. An essential question in transport theory is to recover the hidden properties of the particles and medium from various physical measurements. This arises in a wide range of applications, including medical imaging, optical tomography, remote sensing, seismology and atmospheric science. This project will address both the theoretical foundations and applications of important challenges arising in seismic tomography and transport theory. The project will provide training opportunities for graduate students, especially those from underrepresented groups. This project aims to address the applied analysis of several linear and non-linear inverse problems. It contains two major lines of research. The first topic is on the travel time tomography arising in geophysics, which consists of reconstructing seismic sound speed from the travel time of seismic waves propagating through the Earth. The goal is to study the uniqueness and stability of the travel time tomography in anisotropic elasticity, which is essentially the boundary rigidity problem in Finsler geometry. The investigator will also address the uncertainty quantification of the Bayesian inversion method for travel time tomography as well as carry out numerical experiments. The second topic addresses inverse problems for time-dependent transport equations, which concerns the recovery of time-independent or time-dependent coefficients or sources inside a bounded domain from the boundary measurements of the solution to the transport equation. The investigator will study both the theoretical aspects, including the uniqueness and stability estimates, and the applied aspects, such as the reconstruction methods and numerical implementations. The outcomes of the project are likely to lead to new developments on related research topics and techniques, both inside and outside the mathematical community. 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 support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 2 project aims to enhance student engagement in office hours within STEM courses, particularly focusing on biology courses at multiple HSIs and emerging HSIs within Southern California. Office hours are a common resource for student learning and represent a space where students can engage with other students and instructors. However, office hours remain underutilized by STEM students. The “hidden curriculum”, a set of unspoken norms, practices and values that influence academic success, compounds disparities in office hours attendance and engagement, exacerbating retention and graduation gaps of all students. Thus, this project will examine office hours practices and develop evidence-based interventions that can reduce disparities in office hours engagement. Such efforts will positively impact retention and persistence within STEM by creating structural changes to better support student engagement and success. The project unites a network of seven Hispanic Serving Institutions (HSIs) and emerging HSIs, including both 2-year and 4-year colleges within Southern California. The specific aims of the project are: 1) characterize student and faculty perceptions of office hours; 2) investigate the impact of different office hours practices on student behavioral, cognitive, and affective engagement; 3) identify factors that promote office hours engagement; and 4) design and assess evidence-based interventions aimed at increasing student engagement in office hours. We will conduct surveys and focus groups for students (including students who have and have not attended office hours) and instructors to gather insight into student perceptions, motivations, and barriers as well as instructor perceptions and practices. We will form a collaborative learning community across our network that will use data gathered to design evidence-based interventions to foster office hours engagement. Through application of our findings, we will create and disseminate evidence-based practices for faculty that support student engagement in office hours. Because of the ubiquitous nature of office hours, our interventions will be applicable to instructors across institutions and STEM fields, and we will lead workshops and develop evidence-based teaching guides to share our work. Thus, our project will promote student success and belonging in STEM for all students across higher education through structural changes that support student engagement. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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

Computer simulations are a powerful tool for understanding, predicting, and discovering soft material formulations such as polymer systems. Polymers are composed of long molecular chains and are ubiquitous in both synthetic (e.g., nylon, polyethylene, polyester, Teflon) and natural (e.g., DNA, proteins, cellulose, nucleic acids) settings. In this project, computational tools will be developed that combine machine learning and scientific computing for the exploration and prediction of polymer systems, which will also help to accelerate the discovery of new materials. More broadly, this project will provide a framework for similar computationally costly problems that could be dramatically expedited by using machine learning. Last but not least, the project will serve as an anchor for the interdisciplinary training of both undergraduate and graduate students in an emerging field of much demand. Parameter space exploration for a soft material is an instance of the forward problem: given a set of parameters, find the corresponding stable morphology. But the inverse design problem, which consists of obtaining the formulation parameters that stabilize a given target morphology, is also of great technological importance as it facilitates the design of new functional materials with highly-tuned and desired properties. The numerical solution of both forward and inverse design problems requires the repeated evaluation of the computationally costly functions. The research team will develop efficient computational methods to enable polymer self-consistent field theory with machine learning to accelerate the solution of both forward and inverse design problems aimed at facilitating the discovery of new structures and the design of polymers and polymer 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.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT Gastrulation is a pivotal process in mammalian embryogenesis that is essential for the establishment of definitive germ layers and the formation of the body plan. Despite decades of research, the precise mechanisms that regulate cell differentiation, migration, and patterning during mammalian gastrulation remain poorly understood, particularly in primates. The study of primate gastrulation is especially challenging because this process occurs after the embryo has implanted into the uterus, making it difficult to directly observe. Recently, several three-dimensional (3D), stem cell-based models of primate embryos, termed “stembryos”, have been developed for in vitro studies of embryonic development. However, most approaches for generating stembryos exploit the inherent self-organizing capacity of stem cells to form 3D constructs and are unable to precisely control the number of cells and cell types per construct. A lack of tools for engineering stembryos has resulted in low yields and has limited the ability of stembryos to faithfully recapitulate primate gastrulation. We have recently developed a stembryo model using induced pluripotent stem cells (iPSCs) from chimpanzees along with an optofluidic (i.e., combination of optogenetic and microfluidic) device for performing Chip-based, High-throughput Investigations into Morphogenesis in Primates, called the OptoCHIMP platform. Our OptoCHIMP platform utilizes microfluidic encapsulation to place cells into hydrogel droplets. A downstream droplet sorter unit allows us to select droplets of interest with prescribed cell numbers of cell ratios. This enables us to fabricate stembryos in a precise and high-throughput manner. In this proposal, we will utilize the OptoCHIMP platform to define the roles that 3D structure, local tissue mechanics, and spatiotemporal signaling dynamics play in symmetry breaking during primate gastrulation. By utilizing optogenetic tools to stimulate signaling pathways in chimpanzee stembryos, we can recreate the complex interplay of signaling pathways between embryonic and extraembryonic tissues, induce symmetry breaking in our stembryo, and develop a model for understanding how morphogen signaling dynamics impact tissue patterning. This proposal will be the first time that microfluidic droplet encapsulation of stem cells, which offers precise control over the initial conditions, has been combined with optogenetic, providing a promising approach to uncovering the mechanisms that drive primate gastrulation.

NSF Awards · FY 2024 · 2024-07

This research team studies the atmospheres of galaxies, which has many parallels to studying Earth's atmosphere. Earth's atmosphere is fundamental to life. The water cycle, when water changes phase, is very important for life. To study this, climate scientists run computer simulations at many different scales to get accurate results. Similarly, galactic atmospheres are fundamental to star formation in galaxies. The baryonic cycle, when gas changes phase, is very important for making stars. Galaxy models cover an enormous range of scales. The investigator will run high-resolution supercomputer simulations of galactic atmospheres on small scales to understand physics that is unresolved in larger scale simulations. He will also focus on outreach to high school students, who are at a particularly formative stage. To inspire their interest in science, especially in under-represented communities, his team will give public talks, teach weekend classes, and supervise research. The investigator will run high-resolution idealized simulations to better understand: i) turbulent fragmentation and coagulation of clouds, as well as other physical processes underlying power-law cloud mass distributions. ii) The influence of magnetic fields on key processes such as ‘shattering’, survival and growth of cold streams and clouds, cloud sizes/densities and absorption line statistics, turbulent fragmentation and coagulation. iii) The observed motion of multi-phase gas in the solar corona, resolution requirements for numerically converged absorption line column densities, and the physics behind the ubiquity of OVI in galactic halos. The deeper physical understanding of multi-phase processes can then be fed back into sub-grid prescriptions for larger scale simulations. 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

Large language models (LLMs) have been widely adopted for various tasks, including question-answering, code generation, and addressing complex challenges based on user instructions. Their extensive applications highlight their ability to store, process, and deliver knowledge, revolutionizing the fields of natural language processing and artificial intelligence. Despite the superior performance of LLMs in harnessing knowledge, three research challenges remain that prevent LLM techniques from being applied to a wider range of real-world applications and use cases: 1) understanding how knowledge interacts with LLMs’ behavior; 2) surgically and precisely correcting outdated and incorrect knowledge in LLMs without affecting other knowledge; and 3) efficiently imparting new knowledge to adapt LLMs to different tasks and domains. Limited access to LLM parameters, pre-training data, training recipes, and computational resources hinders conventional methods from addressing these challenges. Hence, there is a strong need to develop innovative algorithms that can improve the understanding, correction, and adaptation of LLMs’ knowledge despite these constraints, which is the main focus of this project. Additionally, this research will be integrated into education through new teaching modules in developing graduate LLM courses, promoting education for undergraduate research, delivering workshops and tutorials at major conferences, and outreach to students from underrepresented communities to promote their awareness and skills in utilizing and comprehending LLMs. This project is structured from a knowledge-oriented perspective and is organized into four thrust quadrants based on two dimensions: the location of knowledge (within model parameters or introduced through model inputs) and the end goal (enhancing understanding for better interpretability or updating the knowledge base for corrections or task adaptation). Specifically, thrust 1 focuses on improving the understanding of how LLMs leverage external knowledge by investigating the impact of 'task' and 'knowledge' information in in-context learning. Building upon the understanding of how knowledge is delivered in in-context examples, thrust 2 focuses on advancing in-context selection mechanisms that enable LLMs to learn from their own mistakes and to be deployable in scenarios where the data for in-context selection may change over time. Thrust 3 aims to understand where knowledge is embedded in LLM model parameters, specifically with different levels of knowledge granularity. Finally, thrust 4 aims to deliver methods to update knowledge surgically through model parameters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

Project Summary Thiyl radicals lie at a perilous nexus between essential biological function as cofactors in enzyme catalysis and toxic products of oxidative stress. As a cofactor, cysteine thiyl radicals facilitate a wide range of C‐X (X= H, C, N, O, S, P) bond cleavage and formation reactions with high fidelity, requiring selective thiyl radical generation, active site chemistry, and reduction. Their reactivity, particularly within an active site with many weak C‐H bonds and oxidizable amino acid sidechains presents several alternative fates, clearly manifest in thiyl radicals generated as the product of oxidative stress “off‐ pathway,” where they can form new S−S and S−C bonds, or catalyze other chemical modifications that are deleterious to biological function. Understanding the structure/function relationship that dictates thiyl radical fate holds promise in developing better therapeutics and antibiotics that target thiyl radical enzymes, informing biomimetic or biocatalytic synthetic biology, and mitigating their role in oxidative stress. Understanding the mechanistic and contextual aspects of how thiyl radicals are generated and what dictates their fate in proteins are essential to addressing their impact on human health. Our research group is seeking to define how thiyl radicals that enable catalysis are selectively generated and maintained, how this might inform therapeutic developments, and what determines the fates of orphaned thiyl radicals. To these ends, we are employing genetic, chemical, and spectroscopic tools to understand thiyl radical chemistry within proteins, from generation to termination, with kinetic and thermodynamic resolution. As our proof‐of‐concept, we are applying these tools to the development of mechanism‐based inhibitors for prominent thiyl radical enzymes in the gut, targeting Clostridioides difficile, with precision. More broadly, however, by advancing our understanding of thiyl radical chemistry in diverse protein milieu the resulting discoveries will provide new opportunities to improve or target thiyl radical catalysis and rationalize oxidative stress pathways. Through developments in each area, we will form the foundation of a holistic understanding of thiyl radical chemistry in biology that we anticipate will have wide ranging implications, both in basic and applied life and physical sciences.

NSF Awards · FY 2024 · 2024-07

Liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) is a hot topic at the interface between biology, physical chemistry, and biophysics. Of particular interest is the possible role played by LLPS in the pathological aggregation of proteins into amyloid fibrils. This award aims to elucidate how protein-rich droplets, known to play important roles in normal cellular function, can, under certain conditions, promote the pathological aggregation of proteins associated with neurodegenerative diseases. At this time, the basic properties of the protein droplets have been scarcely characterized, and the processes leading to droplet formation promoting protein aggregation are unknown. Using coordinated experimental and computational research, carried out by a team of scientist in the US and France, this project will explore factors governing droplet formation and aggregation of two proteins: tau and alpha-synuclein. The research will generate fundamental knowledge in protein science and provide new insight into the molecular processes that are at the origin of protein aggregation diseases. Support will enable the exchange of junior (graduate student and postdoctoral researchers) between institutions, furthering the education and professional development of those young scientists. The research conducted as part of this award will explore the role of LLPS in the aggregation of the IDPs tau and alpha-synuclein. The research will employ an innovative combination of computational and experimental techniques, namely electron paramagnetic resonance (EPR), Overhauser Dynamics Nuclear Polarization (ODNP), neutron scattering (NS) and multiscale computer simulations. Simulations will employ a unique combination of molecular dynamics and field theory approaches to gain novel insights into protein conformations and hydration water dynamics, and to map phase behavior of the IDPs. The consortium is composed of three French and two American partners that form a multidisciplinary team of structural biologists and biochemists, biophysicists, physicists, and computational chemists with computational and experimental expertise in protein sciences. The contributions of all five partners are essential to create the synergy necessary to tackle the challenging objectives. Besides generating fundamental knowledge in protein science, this project will provide new insight into the molecular processes that are at the origin of protein aggregation diseases. This award will enable the exchange of junior (graduate student and postdoctoral researchers) between institutions, furthering the education and professional development of those young scientists. Collaborative activities will include physical exchanges of junior researchers from the US visiting the IBS in Grenoble and University of Bordeaux in France, and junior researchers from France visiting Boston University and UC Santa Barbara in the US. Visits will include active research activities, involvement in group meetings, and presentation of research seminars. This collaborative US/France project is supported by the US National Science Foundation (NSF) and the French Agence Nationale de la Recherche (ANR), where NSF funds the US investigators and ANR funds the partners in France. The US investigators are jointly funded by the Physics of Living Systems program in the Directorate for Mathematical and Physical Sciences and the Molecular Biophysics program/Division of Molecular and Cellular Biosciences in the Directorate for Biological 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 · 2024-07

In order to predict the future evolution of Earth’s climate, it is important to understand and quantify the processes that govern vertical mixing of heat and CO2 in the ocean, such as tides, internal waves and others. The degree to which the motion of small organisms such as zooplankton can contribute to this mixing is currently unknown, although several hypotheses have been forwarded that arrive at very different conclusions. The current project aims to improve the understanding of such biogenic mixing via a collaborative investigation that combines theoretical analysis with laboratory experiments and detailed computer simulations. In this way, the project results will enable more accurate predictions of future climate trends. The project will involve significant educational and outreach activities, including research by undergraduate and high school students. While it has been proposed that biologically generated turbulence plays an important role in oceanic turbulence, the range of zooplankton swimmer sizes that can contribute to such mixing is currently unknown. Recent research indicates that the minimum swimmer required depends on the nature of the flow field in which the swimmer moves, so that the capability of a swimmer to produce sustainable biogenic turbulence is not an inherent and static characteristic, but rather, it is modulated by the swimmer’s orientation in relation to the local shear and the intensity of the ambient hydrodynamic shear. The objective of the proposed research is to employ both laboratory experiments and direct numerical simulations (DNS) to reveal the minimum size and the corresponding biogenic turbulence production mechanism in a space spanned by the strength of the background shear, the orientation of the swimmer with regard to this shear, and the swimmer size. On this basis, models for the incorporation of these effects into ocean simulation tools will be developed. The experiments will use a unique system that can produce accurate on-demand migrations of zooplankton via phototaxis in a background shear in a controlled laboratory setting. The computational methodology is based on a well-validated immersed boundary method approach, and it employs an established squirmer model to represent the individual organisms. The proposed research will reveal how the swimmer’s agitation produces turbulence and dissipation. It will be the first systematic experimental and numerical study of biogenic turbulence considering both swimmer and background flow. Although the research is motivated by ocean flows, the insights gained from the project will deepen our understanding of how physical perturbations affect turbulent flows in general. 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

This IUSE Computer Science Level 2 Engaged Student Learning project aims to serve the national interest by preparing software engineering students to make high-quality contributions to team software projects. The goal of this project is to develop and evaluate a pedagogical approach for undergraduate software engineering courses that include team software development projects. In these courses a significant problem is assessing the contributions that a student makes to their team. This problem will be addressed by deriving the desirable qualities of four key software engineering artifacts that team members contribute to a software project. Based on these qualities, students will learn how to contribute high-quality artifacts by assessing and reflecting on their and their peers’ artifact contributions. This project will strengthen software engineering education by developing rigorous methods for assessing the quality of software project contributions. This project will iteratively refine and empirically evaluate a valid, reliable, practical, and just approach to assessing the software engineering artifacts that individual software engineers contribute to team software development projects. By taking a rigorous empirical approach to deriving the desirable properties of individuals’ contributions, developing a valid and reliable method for assessing individuals’ contributions against those properties, and making assessments practical through a software tool that enables assessments to be easily performed on samples of individuals’ contributions, this project will (a) contribute a novel pedagogy for fostering software engineering competence through peer review and reflection; (b) improve instructors’ ability to assess their students’ learning, and (c) enhance researchers’ ability to study and evaluate pedagogies for team projects in software engineering education. 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 · 2024-07

With the support of the Chemistry of Life Processes (CLP) Program in the Division of Chemistry, Justin J. Wilson of Cornell University is developing new tools to study the role of the gas hydrogen sulfide in biological settings and human health. Hydrogen sulfide (H2S) is a small gaseous molecule, sometimes referred to as a gasotransmitter, that is biosynthesized and plays an important role in regulating a number of processes associated with normal and healthy cellular function. Because hydrogen sulfide is a gas, its controlled delivery to biological systems, in vivo, is challenging. Therefore, a deeper understanding of how this gaseous signaling molecule operates in livings systems is of great interest. This project will develop new chemical tools that can deliver small, biologically relevant amounts of hydrogen sulfide to cells upon activation by selective stimulation. If successful, these tools will enable researchers to probe the biological relevance and importance of H2S in human health. In addition, this project will develop new educational activities for high school STEM (science, technology, engineering and mathematics) teachers. These activities will be designed to highlight how compounds such as hydrogen sulfide, that can be toxic, may be present in “natural” or “organic” foods at sufficiently low concentrations that do not present a health hazard. Rather, this compound has a critically important role in human biology and these activities will be designed to educate the public about the fascinating roles of gasotransmitters that are so critical to heathly human biology. The Wilson research group at Cornell University will design new ruthenium-based hydrogen sulfide-releasing molecules as chemical tools to study the biological roles of the gasotransmitter, hydrogen sulfide (H2S). Hydrogen sulfide regulates a wide range of critical biological processes in eukaryotic cells and is known to elicit therapeutic effects for the management of heart disease, stroke, and cancer. The direct administration of H2S to biological systems, however, is limited by its gaseous nature and toxicity at high concentrations. To overcome these challenges, researchers have developed easily handled compounds that slowly release this gasotransmitter to study its biological function. These efforts have been focused on organic compounds that are designed to release hydrogen sulfide via uncontrolled hydrolysis, blue or ultraviolet light irradiation, oxidation by reactive oxygen species, enzymatic activity, and reactions with thiols. The objective of this project is to expand the toolkit of hydrogen sulfide-releasing molecules by developing compounds based on the transition metal ruthenium. By using ruthenium coordination complexes, the Wilson team aims to gain access to hydrogen sulfide-releasing agents that are selectively activated by stimuli that are inaccessible with conventional organic compounds. Through these efforts, the redox chemistry and photochemistry of ruthenium coordination compounds will be leveraged to make hydrogen sulfide donors that are triggered by chemical reduction and light irradiation, respectively. Lastly, the Wilson group will use these new tools to understand the mechanisms by which hydrogen sulfide can protect against ischemia-reperfusion injury in vitro. Collectively, this research will expand the toolkit of available hydrogen sulfide donors and will demonstrate their value by applying them to identify new roles for this gasotransmitter in healthy human 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.