University Of California-Irvine

NSF Awards · FY 2025 · 2025-02

Plastic waste poses a pressing environmental challenge, with over 90% ending up in landfills or incinerated, resulting in severe ecological damage. Traditional recycling often leads to "down-cycling," where plastics are converted into lower-quality materials with limited use. One promising recycling approach is hydrogenolysis, which transforms plastic waste into valuable materials and chemicals by breaking down plastics like polyethylene into smaller, higher-value chemicals using metal catalysts and hydrogen gas. Unlike conventional chemical recycling methods, hydrogenolysis operates under milder conditions without the need for harmful solvents, making it a more sustainable solution. However, there are challenges in adopting this technology for widespread use owing to low efficiency, undesirable byproducts, and difficulties in scaling the process for industry. This CAREER award aims to overcome these obstacles using advanced computational tools based on quantum mechanics, machine learning, and statistical thermodynamics. The goal is to uncover the molecular mechanisms of hydrogenolysis for the world's most common plastic polyethylene, and develop new predictive models for optimizing its efficiency and selectivity. This research promotes global environmental preservation and circular economies by designing sustainable chemical processes to reduce plastic waste. The proposed work also integrates research with educational and outreach activities to enhance computational literacy among students. This CAREER award focuses on uncovering the molecular mechanisms underlying the chemical upcycling of polyethylene, the world's most common plastic. As the amount of plastic in our environment steadily increases, there is an urgent need for efficient recycling methods. Chemical recycling based on hydrogenolysis offers a promising approach to managing plastic waste by deconstructing long-chain polymers with metal catalysts and hydrogen gas to create smaller hydrocarbon products, some of which are high-value chemical commodities. Despite progress in converting polyethylene into liquid fuels, challenges remain in controlling the reaction specificity and efficiency of waste-to-liquid fuel conversion, due to the limited understanding of the mechanisms responsible for this conversion. A comprehensive understanding of these complex systems has remained elusive because of the difficulties associated with ab initio methods in producing free energy landscapes relevant to operando conditions. The integrated education and outreach program seeks to enhance computational literacy among students,from K-12 to college. Proposed activities include developing computational materials science laboratory courses for chemistry, physics, and engineering undergraduate and graduate students, as well as creating open-source online tools based on the simulation code and data generated from this research. Additionally, workshops and curricula on materials sustainability and computational research will be designed for students in collaboration with a local nonprofit specializing in cognitive-behavioral training. This CAREER award advances the fundamental understanding of the sustainable chemical process of plastic waste deconstruction. This project is jointly funded by the Process Systems, Reaction Engineering and Molecular Thermodynamics (PRM) program in the ENG/CBET division, and the Chemical Theory, Model and Computational Methods program (CTMC) in the MPS/CHE division. 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 · 2025-02

Project Summary- R35 Application PI: Benjamin R. Morehouse Ph.D. The innate immune system is the critical first line of defense against microbial pathogens and is also a key player in the response to cellular stress. A thorough understanding of innate immune function and disfunction is necessary if we are to meet the healthcare challenges that face modern human society. Innate immunity is common to all life on this planet, however how exactly diverse organisms, especially those considered to be ‘non-model’, defend themselves from infection is an underdeveloped field of research that may yield clues to understanding of our evolutionary history and provide new insights into the organization and functioning of human immunity. Surprisingly, we find that parts of the human innate immune system have ancient origins that can be traced as far back as the antiviral defense pathways of bacteria and archaea. Among these evolutionary connections we find many mechanistic similarities but also significant differences in form and function. Our particular interest lies in the shared strategies of cyclic nucleotide second messenger signaling pathways of immunity, the use of specialized chemical compounds that mediate antiviral immune defense. The antiviral and antitumoral cGAS-STING (cyclic GMP-AMP synthase-stimulator of interferon genes) cyclic dinucleotide signaling pathway in humans is analogous to cyclic oligonucleotide-based antiphage signaling systems (CBASS) in prokaryotes and we have uncovered many striking elements of conservation between these defensive mechanisms. Pycsar (pyrimidine cyclase system for antiphage resistance), is a similar prokaryotic immune defense pathway that operates through production of cyclic pyrimidine mononucleotides. Through detailed exploration and characterization of CBASS and Pycsar immunity that generate cyclic nucleotide signals, we will delineate the modes of viral activation, probe the mechanisms of nucleotide selectivity for both cyclase enzymes and cyclic nucleotide receptors, and define the functional consequences of effector activity on cell growth and viral infection outcomes. Using a combination of bioinformatic analysis, biochemical testing, and structural biology approaches, we will confirm activities and phylogenetic links between diverse bacterial, fungal, and mammalian homologs to establish the relationships shared between cyclic nucleotide signaling pathways. We will examine the connections between evolutionarily distant immune systems, potentially leading to identification of new strategies for pharmacological targeting and destruction of antimicrobial resistant pathogens, and we stand to uncover more of the mechanisms that shape our own immune system which will benefit humankind in the fight against infectious agents, cancer, and autoimmune disease.

NIH Research Projects · FY 2026 · 2025-02

The Role of Interferon Lambda in Alpha Herpesvirus Neuroinvasion Project Summary: Virus infections typically begin in peripheral tissues and usually do not spread to the nervous system (NS) because it often represents a dead end for both the host and the pathogen. However, some viruses, such as alpha herpesviruses (e.g., Herpes Simplex Virus-1, HSV-1), have evolved mechanisms to efficiently enter the peripheral nervous system (PNS) and spread between connected neurons after replicating in mucosal epithelia. Although much is known about productive infection in epithelial cells and some details of the latency phase in neurons, the molecular events of viral invasion from epithelial cells to the nervous system and the responses of peripheral axons to this process are not well understood. This proposal focuses on the initial steps of alpha herpesvirus invasion of the PNS. We hypothesize that the response of peripheral nerves to cytokines, particularly interferon lambda (IFN-λ) produced by infected epithelial cells, affects the transport of viral particles in axons and ultimately determines the establishment of infection (quiescent or productive) in the neuronal nucleus, impacting the frequency of reactivations. Preliminary data suggest that axons of peripheral neurons respond to IFNs produced in infected epithelia, resulting in a non-canonical antiviral state specifically targeting alpha herpesvirus transport. Using this foundation, we have developed an in vitro latency model by infecting isolated axons with low multiplicity of pseudorabies virus (PRV) and HSV-1. This proposal aims to elucidate the mechanisms of local axonal responses to IFN-λ and their effects on viral invasion of the nervous system. Additionally, it seeks to determine how these initial virus-host interactions influence the mode of infection (latent vs. productive) in neurons and whether peripheral IFN-λ responses dictate the efficiency of infection establishment. Our primary hypotheses are: i) axons serve as front-line sensors and responders to viral infection and inflammation, ii) IFN-λ responses in axons affect alpha herpesvirus particle transport, the establishment of lifelong infection in neuronal nuclei, and viral spread within the nervous system. To test these hypotheses, we will leverage state-of-the-art technologies we developed during my previous studies, including: i) tri-chamber Campenot chambers that physically isolate axons from neuronal cell bodies, ii) live-cell optical imaging to track entry and subsequent axonal transport of individual virus particles in the presence or absence of cytokines, inhibitors, or injury, iii) identification of nascent RNA and proteins in neurons, iv) an in vitro latency model to study the early events in latency establishment. By understanding the immediate and local responses of PNS axons to incoming virus particles and inflammatory cytokines before new viral gene products are made, this research will provide deeper insights into how the nervous system is protected from infection. It will also reveal novel aspects of the molecular basis for latency, potentially leading to new therapeutic strategies against alpha herpesvirus infections.

NSF Awards · FY 2025 · 2025-01

As the complexity and diversity of modern computing workloads grow rapidly, existing computer systems with homogeneous processors or accelerators show critical limitations in computing these complex workloads. This urges the creation of next-generation heterogeneous hardware acceleration systems with diverse processors and accelerators – e.g., central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), neural processing engines, etc. – as well as their design automation and programming tools. However, existing heterogeneous computing systems face the "diversity crisis" where the heterogeneity of systems poses critical challenges on the system design and optimization. This project aims to mitigate these challenges by developing an efficient compilation and synthesis flow that compiles high-level programs into domain-specific reconfigurable heterogeneous acceleration systems. The design productivity improvement from this project can potentially mitigate the hardware design workforce shortage problem that society currently faces, and it can directly translate into a larger semiconductor market and increased domestic job openings. The advancement in hardware acceleration can innovate scientific discovery, artificial intelligence, and other fields. The education activities in the project can improved the skill sets of future semiconductor workforce. The objective of this project is to develop an efficient compilation and synthesis flow from high-level programs (e.g., Python) to domain-specific reconfigurable heterogeneous acceleration systems. This project aims to create a framework that features the following key innovations: (1) a heterogeneous and efficient acceleration system template that combines hardened digital accelerators, reconfigurable digital logic, and general-purpose processors; (2) an optimizing compiler and an agile design automation flow that partition and compile high-level application code, and generate the optimally configured heterogeneous hardware system design and programs; (3) a hardware-aware learning-based optimization engine that constructs global design spaces and searches for the optimal configuration of the heterogeneous system and program that meets application requirements; and (4) the optimal runtime scheduling of the computing tasks and runtime dynamic reconfiguration of the programmable logic elements inside the heterogeneous system. This project focuses on the fundamental research in electronic design automation (EDA) tools, and it is expected to accelerate the development of modern computing systems and design methodology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The genomes of plants often vary substantially in their size and organization, even among individuals within the same species. An enduring mystery is how genomes can differ so dramatically and yet still produce functional organisms that are recognizable as a specific species. In this study, we will investigate genome diversity in corn (maize) to test a specific idea. Genomes are like strings that fold into loops. We suspect that the ends of the loops define active (or functional) regions of the genome, and that these regions remain stable among individuals. We also hypothesize that the size and content of the loops themselves is more variable, because they are a mechanism to sequester regions that are less important to genome function. To test this hypothesis, we will measure folding structures of 25 corn genomes and compare those structures across different individuals. We will measure folding by two methods, one that focuses on the entire genome and another that focuses on small stretches of DNA. By testing our hypotheses, we will gain insight into the enduring mystery of plant genome variability and generate basic knowledge about how plant genomes work. This proposal focuses on measuring two types of genome variability among the parents of the maize Nested Association Mapping panel and two outgroup species (Sorghum bicolor and Setaria italica). The first type is DNA sequences that can form pre-miRNA-like stem-loops. These structures impact genome function when transcribed and when present in DNA; they are often the locus of small RNA production that with downstream epigenetic effects. The second type is chromatin folding into higher-order loops and domains. These domains may contribute to genome function by co-locating groups of expressed genes, thereby defining ‘transcription factories’. Despite the potential significance of both structure types, their variability within populations and the pace of their evolution remains poorly understood. Our goals are to catalog homology and variability of these structures within- and between-species, to assess which structures are conserved, to explore their epigenetic and functional contributions and, finally, to characterize the evolutionary forces that shape their underlying sequence variability. Since both structures have hypothesized roles in transcription and chromatin confirmation, we predict that they may colocalize in three-dimensional genome space. This project will also contribute to the training of project personnel and enhance the research experiences and retention of underrepresented and minority undergraduates. As part of the predoctoral research experience, they will learn bioinformatics and data analysis skills in the PIs research laboratories. 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 · 2025-01

PROJECT SUMMARY NF-κB is a transcription factor activated in response to environmental genotoxic stress and important for driving DNA damage-induced inflammation. However, mechanisms linking DNA damage that occur in the nucleus to NF-κB activation in the cytoplasm remain poorly understood. NF-κB is rapidly activated after DNA damage through a signaling cascade regulated by the Ataxia-telangiectasia mutated (ATM) kinase, which is stimulated by DNA double-strand breaks. Once activated, ATM relocalizes to the cytoplasm to interact with the ubiquitin ligase TRAF6. In turn, TRAF6 induces the IKK complex to initiate IκBα proteasomal degradation, which sequesters the NF-κB heterodimer p65-p50 in the cytoplasm. The degradation of IκBα allows p65-p50 nuclear localization to promote specific inflammatory gene expressions. However, it is still unclear whether other signaling pathways are present in cells to rapidly activate NF-κB following DNA lesions that are not recognized by ATM. More importantly, the canonical innate immune response driven by NF-κB relies entirely on gene expression to release IFNs and other immunomodulatory proteins from the injured cells. However, DNA lesions that block RNA polymerases and thus stop transcription impede inflammatory gene expression in the injured cells, resulting in the inhibition of the innate immune response. Therefore, how cells can trigger an innate immune response in the context of DNA damage-induced transcription blockage is still unknown. Our goal is to identify a new innate immune signaling mechanism that is triggered specifically after DNA damage has blocked transcription. We hypothesize that DNA lesions that impede transcription trigger innate immune signaling by directly secreting specific factors to alert neighboring cells. In Aim 1, we propose to explain the mechanism by which environmental agents suppressing transcription trigger an innate immune response to alert neighboring cells of potential dangers and recruit immune cells without relying solely on gene expression. Then in Aim 2, we propose to determine the physiological impact of environmental agents suppressing transcription mediated by DNA damage. We aim to link genetic diseases affecting DNA repair pathways with chronic inflammation triggered by overactivated NF-κB in response to specific DNA lesions. The findings from this study will provide a foundation for future research utilizing mouse models with compromised DNA repair pathways to investigate how environmental factors influence chronic inflammation and to test NF-ⲕB inhibitors’ efficacy as a potential therapy.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY We introduce an innovative AI-Assisted Laser Capture Microdissection (AI-LCM) approach for Spatial Transcriptomics, designed to overcome the limitations of existing spatial biology methods. Current techniques like MERFISH, Visium, and traditional LCM have distinct strengths, but they fall short in providing both single- cell resolution and genome-wide coverage efficiently. Our AI-LCM technique combines microscopy, Next Generation Sequencing (NGS), and LCM to enable high-throughput, comprehensive spatial transcriptomics analysis. Our method utilizes DNA nanoballs tagged with unique molecular identifiers (UMIs), readable via both microscopy and NGS. This dual-readout allows for an 'imaging-followed-by-sequencing' workflow, ensuring each cell's omics profile is linked to its spatial coordinate through the UMI-tagged nanoballs. We will achieve this through three strategic aims: 1. Optimizing in situ generation and sequencing of DNA nanoballs for diverse sample types. 2. Enhancing AI-assisted LCM to efficiently dissociate nanoball-tagged single cells at high- throughput. 3. Refining sequencing protocols to accommodate nanoballs, linking genomic data with precise spatial origins. This approach promises true single-cell spatial resolution with genome-wide coverage, exploiting the dual-functionality of DNA nanoballs for concurrent processing of thousands of cells, vastly improving LCM's throughput. Building on established technologies, our protocol is both feasible and innovative. DNA nanoballs are already integral in microscopy-based spatial transcriptomics, and LCM's robustness for single-cell profiling is well-documented. Our integration of microscopy and genomics draws on the PIs' extensive experience in commercial SBS products, automated microscopy, and AI-based image analysis. The proposed AI-LCM method not only aims to transcend the boundaries of existing spatial transcriptomics but also seeks to expand the possibilities for a wide range of single-cell assays, adding valuable spatial context to complex genomic data. This project stands to revolutionize the field by providing a novel tool for comprehensive, high-resolution mapping of cellular environments.

NIH Research Projects · FY 2024 · 2025-01

Next-Generation In-Vivo Fetal Neuroimaging The overall objective of this project is to dramatically improve fetal magnetic resonance imaging (MRI) to advance research in early human brain development and neurodevelopmental disorders, the burden of which is, unfortunately, high because of their life-long impact and high prevalence. Fetal MRI has been the technique of choice in studying prenatal brain development. Fetal motion, however, makes MRI slice acquisition unreliable at best, as the fetus frequently moves while the prescribed slices are imaged. Uncompensated fetal motion disrupts 3D coverage of the anatomy and reduces the spatial resolution of slice-to-volume reconstructions. Repeating the scans does not ensure full 3D coverage of the anatomy, but increases total acquisition time. This, in turn, dramatically reduces the success rate and reliability of fetal MRI in studying the development of transient fetal brain compartments that are selectively sensitive to injury over the course of fetal development. To mitigate these issues and improve fetal MRI, we propose to automatically measure fetal brain position and prospectively navigate slices to each new position in real-time. The impact of this approach will be to dramatically increase the success rate and spatial resolution of fetal MRI for the in-vivo investigation of developing brain compartments, while, in parallel, reducing scan time, effectively making fetal MRI less burdensome for the mother, more accurate, and cost effective. By eliminating the manual re- adjustment of stack-of-slice positions, the time that elapses between scans will be virtually continuous. Our proposed technique will also make fetal MRI less operator-dependent and thus, more reproducible across sites, which is essential to conducting multi-center studies and clinical trials. Prospective navigation of fetal MRI slices to compensate for motion requires the development of novel, real-time image processing algorithms to recognize the fetal brain and its position and orientation; to track fetal motion to steer slices; and to detect and re-acquire motion corrupted slices. In this project, we will develop innovative deep learning models to process fetal MRI slices in real-time; will translate those models into an integrated system to prospectively navigate fetal MRI slices; and will validate the system on fetuses scanned at various gestational ages. To assess the utility and impact of the proposed technology, we will measure subplate volume in fetuses. The four specific aims of this study are to 1) assess fetal MRI via variable density image acquisition and reconstruction; 2) achieve real-time recognition of the fetal brain in MRI slices; 3) develop a system of real-time fetal head motion tracking and steering of slices; and 4) measure the subplate volume in the developing fetal brain using MRI. These aims will collectively translate and validate new imaging and image processing techniques to advance fetal MRI, and effectively eliminate a critical barrier to making progress in the fields of developmental neurology and neuroscience.

NIH Research Projects · FY 2026 · 2025-01

The APOBEC3 enzymes are a family of cytosine deaminases that convert cytosine to uracil on DNA or RNA and function as a vital part of mammals’ innate immune system. They provide an innate immune barrier against DNA and RNA viruses, retroviruses, retrotransposons, and other viral pathogens by inducing mutations in the virus genomes to stop their replication and protect cell integrity. APOBEC3 enzymes have evolved different preferences for DNA or RNA sequences and structures to fight against diverse viruses that cells may encounter. Many viruses, such as HIV-1, hepatitis B virus, and SARS-CoV-2, have been found to accumulate APOBEC- driven hypermutations in their genomes. However, mutations induced by APOBEC3 enzymes are a double- edged sword, as a high level of mutations blocks viral replication by inducing lethal alterations, but a lower level of mutations promotes virus evolution and the production of new viral variants with improved features, allowing them to escape cell defense mechanisms. Remarkably, APOBEC3 enzymes also protect cells against viruses through non-canonical pathways without mutating their genomes, suggesting that APOBEC3 enzymes have evolved other mechanisms to inhibit viruses without promoting their evolution, transcending the simple model of APOBEC3s inducing mutations in viral genomes to stop their replication. Yet, the different mechanisms by which APOBEC3 members suppress viral infection without editing their genomes are still poorly understood. Our goal is to identify novel APOBEC3B anti-viral functions that do not require their deaminase activity. We hypothesize that APOBEC3B RNA binding activity is critical in suppressing RNA virus replication by acting as a viral RNA sensor to promote the activation of the innate immune response. Our preliminary results showed that APOBEC3B promotes PKR activity after different types of RNA virus infections. Based on these results, we propose to 1) explain how APOBEC3B modulates the PKR signaling pathway to promote translation arrest, and 2) determine whether APOBEC3B suppresses RNA virus replication. This study will reveal for the first time that APOBEC3B is critical to protecting our cells against RNA virus infection without editing their genomes alongside its function against DNA viruses and retroviruses. The long-term goal resulting from this study is the development of therapeutic strategies to suppress RNA virus replication by exploiting APOBEC3B antiviral activity.

NIH Research Projects · FY 2026 · 2025-01

Proposal Summary/Abstract Myeloproliferative Neoplasm (MPN) is a group of chronic blood disorders. The majority of MPN patients have acquired a JAK2V617F mutation which leads to constitutive signalling of myeloid growth factor receptors resulting in excessive production of mature myeloid cells. MPN patients have additionally exhibited dampened IL-10R signalling responses that has shown to give the JAK2V617F mutant cells a competitive advantage for outgrowth. This blunted IL-10 response is linked to increased TNF-a production, leading to persistence of inflammation and proliferative stress in non-mutant HSCs resulting in HSC exhaustion. As JAK2 canonically does not participate in the IL-10R pathway, the mechanism by which JAK2V617F mutant cells rescue this pathway is still unknown. We hypothesise that mutant JAK2V617F is able to utilise this transactivation mechanism and trans- activate JAK1 in order to induce signalling via the IL-10R pathway, allowing the JAK2V617F selective advantage in MPN dampened IL-10 signalling. In understanding the mechanism JAK2V617F positive cells are able to use to take advantage of the dysfunctional IL-10 response seen in MPN patients, may open up a new therapeutic target as to not only fight against disease progression but also to take away the aforementioned advantage of the mutant JAK2V617F. A key component of our study is the use of a murine pro B-cell, Ba/F3, that is cytokine dependent as a means of not just analysing cell signalling but to also observe the transformative ability of the JAK2V617F mutant. Aim 1 we want to identify the mechanism of JAK2V617F’s non-canonical IL-10R signalling. Both Western blotting and Co-immunoprecipitation techniques will be used to analyse JAK-STAT signalling and hypothesised non- canon JAK1/JAK2 heterodimerisation under IL-10 stimulation and in the absence of cytokine. For Aim 2 we want to determine JAK1’s dependency, role and how it mediates this signalling pathway. To study this siRNAs against mJAK1 will be developed and used, followed by western blot analysis in order to study the JAK-STAT signalling occurring under IL-10 stimulation as previous, but to also determine decrease in JAK1 expression. Aim 3 will be identifying means to inhibit this non-canonical signalling. JAK2 inhibitors, like Pacritinib, will be used as a means to potentially ablate the transactivation mechanism of JAK2V617F and to return the IL-10R signalling back to normalcy.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY One in five thousand babies are born with Hereditary Hemorrhagic Telangiectasia (HHT), a rare disease that results in vascular malformations with a high risk of rupture and bleeding. Most patients suffer from recurrent, spontaneous and prolonged (30+ minutes) nose bleeds, leading to anemia and great social anxiety. Arterio- venous malformations (AVM’s) in liver lead to reduced liver function and ultimately high-output heart failure. Lung AVMs circumvent the clot filtering function of lung capillaries leading to increased risk of stroke. Ruptured AVMs in the brain are rapidly fatal. As for many rare diseases treatment options are limited. This proposal will leverage our recently generated in vitro model of HHT to screen a de-risked drug library for compounds that can be developed or re-purposed to treat patients with this debilitating disease. The cause of HHT has been mapped to two genes that account for over 95% of cases. Alk1 (ACVRL1) and endoglin (ENG) form a heterodimer that acts as a receptor for circulating BMP9 and BMP10. Signaling through this complex engages SMAD4 (mutations in which account for the majority of the remaining patients), which then drives a quiescence phenotype in peripheral endothelial cells (EC). Patients are globally heterozygous for mutations in the receptor genes, with local loss-of-heterozygosity leading to over-activation of the EC and lesion development. There is no evidence that lesions are substantially different in different tissues or that their growth is driven by different mechanisms. A 2018 report co-authored by the PI was the first to show that a small molecule VEGFR2 inhibitor (Pazopanib) could reduce bleeding in patients and this has led to a follow-up study. While this drug looks promising it does not appear to be effective on all patients suggesting that a continued search for alternatives is worthwhile. In this study, we will utilize our recently developed in vitro HHT-on-a-chip microphysiological model, which recapitulates vascular lesions of HHT patients, including the small vascular tangles (telangiectasias) and the larger AVMs. Importantly, lesion formation is blocked by pazopanib, mirroring its effects in patients. Our hypothesis is that: the HHT-VMO platform can be used to identify drugs that could be repurposed to treat HHT. To test this hypothesis we will screen a compound library enriched for drugs with known cardiovascular activity. Many of these are either FDA-approved or otherwise de-risked. We will look for drugs that either block lesion formation (and) or regress already-formed lesions. Our Aims are: 1. Optimize protocol with a 5 compound sub-library. 2. Screen a 1000-compound library. 3. Re-screen hits at additional concentrations and timepoints. The patient advocacy organization CureHHT has already pledged to help move any FDA-approved drugs into clinical trials.

NIH Research Projects · FY 2026 · 2025-01

ABSTRACT This application, submitted in response to NIH PAR-19-162 “Accelerating the Pace of Child Health Research Using Existing Data from the Adolescent Brain Cognitive Development (ABCD) Study”, aims to probe the devel- opmental origins of mental illness, a current public health crisis. This proposal will focus on the developing hy- pothalamus, a historically understudied region of the brain responsible for integrating the brain and bodily states. Specifically, this proposal will test the hypothesis that the structure and connectivity of the hypothalamus is im- pacted by early life adversity, and contributes to maladaptive brain function and behaviors underlying mental illness. The hypothesis is tested via three independent and innovative aims capitalizing on large-scale archival datasets from representative samples of ~11,000 adolescents from the Adolescent Brain Cognitive Development (ABCD, 9-15 years old) study and up to 7,500 children from the HEALthy Brain and Child Development (HBCD, 0-4 years old) study. Specifically, Aim 1 will develop normative MRI-based measures of hypothalamic size and shape, and structural and functional connectivity from birth until adulthood using two additional large-scale ar- chival datasets from the Human Connectome Project (HCP-Development, n~1,300, 5-21 years old; HCP-Young Adult, n~1,200, 22-35 years old) and cutting edge analytical methods. Aim 2 will assess the impact of early life adversity (e.g., economic hardship and adverse childhood experiences) on the structure and function of the developing hypothalamus. Aim 3 will elucidate the contribution of the structure and function of the developing hypothalamus to the motivated behaviors (i.e., reward and inhibition) emerging during adolescence that typically underly complex disorders and mental illness. Collectively, this proposal will capitalize on large-scale archival datasets from representative samples to discover understudied brain-body connections and potentially provide the basis for the subsequent development of strategies and policies aimed at mitigating the risk for mental health disorders.

NIH Research Projects · FY 2025 · 2025-01

Project Summary / Abstract The UC Irvine Center for Neural Circuit Mapping (CNCM) focuses on anatomical, functional and omics analysis of neural circuits, and neurotechnology development. Our Center members are interested in defining molecular and neural mechanisms that underlie neurodevelopmental, neuropsychiatric and neurodegenerative disorders. We work collaboratively to establish transformative and translational research programs that focus on our ultimate goal to develop cures for human diseases of the nervous system. The CNCM has become a hub for cutting-edge technology and resource development in biomedical research. We disseminate our technology resources in coordination with our educational and research initiatives such as seminars, symposiums, and conferences. The 2025 summer conference “The Changing Brain” (August 18-21, 2025) jointly sponsored by the CNCM, Cajal Club and Allen Institute for Brain Science will be held in Irvine, California, USA; this will be our biggest conference yet with an increased focus on international reach. The 2025 meeting topics are timely and significant. The Scientific Program Committee led by Dr. Liqun Luo at Stranford University, has planned six sessions: 1) the Evolving Brain, 2) the Developing Brain, 3) the Learning Brain, 4) the Dynamic Brain, 5) States of the Brain, 6) the Disordered Brain. Our confirmed speakers represent a distinguished roster of leading researchers at various career stages in neuroscience. The speakers will offer diverse perspectives and in-depth insights on the theme of integrating the different facets of neural circuits. We expect vibrant discussions ranging from the latest technologies in neural circuit mapping to the implications of circuit dynamics in health and disease. In addition to the main conference proceedings, attendees will have opportunities to participate in onsite specialized workshops held on the UC Irvine campus. Workshops will offer more hands-on experiences that allow for deep dives into specific subtopics and methodologies in conjunction with specific interests of the attendees. Aligned to the NIMH mission, our meeting will promote the understanding and treatment of mental disorders through basic and clinical research, and will support communication between scientists, physicians, health-care providers, and the public about brain science advances and mental health research progress. We will comply with the NIMH policy, and requested NIMH funding will facilitate the attendance of students, trainees and members of underrepresented groups through waivered registration fees and travel awards.

NIH Research Projects · FY 2026 · 2025-01

Project Summary Since 2014, Yemen has been embroiled in a protracted conflict that has left over 80% of its population in need of humanitarian aid and protection. Yemen faced significant vulnerabilities before the conflict, including a poor economy, food insecurity, fragile healthcare system, and insufficient water, sanitation, and hygiene infrastructure. Now, over 4 million individuals have been displaced, and most of the population is impoverished. Communicable diseases have increased during conflict; the clearest example of this is the cholera outbreak that began in 2016, which is considered the largest cholera outbreak in recent history with over 2.5 million cases and 4,000 related deaths. Only half of Yemen’s health facilities are considered fully functional, making the health system insufficient to meet this increased demand for care. Much of this reduced functionality comes from air raids on the facilities, which have become a central component of the conflict’s violence. However, existing measures do not capture the full extent of conflict’s effect on infrastructure, which is critical in the mechanism between conflict and increased disease incidence. This includes infrastructural rebuilding, which is an important consideration in protracted conflicts as it can help reduce further spread of disease. The overall goal of this proposal is to quantify and assess the association between conflict and cholera in Yemen. The first Aim towards this end is to build a model that measures the association between conflict and cholera while incorporating environmental, economic, demographic, and spatial factors relevant to cholera’s epidemiology. The second Aim will utilize earth observation tools, including nighttime light and synthetic aperture radar, to measure infrastructure changes and assess whether this conflict measurement better predicts cholera cases. I will also use data from this aim to incorporate infrastructural rebuilding into the first Aim’s model. Lastly, my findings from Aims 1 and 2 will be used to project future cholera outbreaks under different conflict, climate, and economic scenarios. Overall, these findings will have important implications for understanding conflict’s role in driving Yemen’s cholera epidemic, how we measure conflict, infrastructure’s role in disease dynamics, and enhancing preparedness efforts. This F31 training fellowship will enable me to expand upon my background in conflict and health research and learn new skills in disease ecology, earth observation, and advanced statistical methods during my PhD dissertation work at the University of California, Irvine. I will be mentored by a team of subject-matter and methodology experts who will help me develop the skills necessary to complete this proposed research. This fellowship will also allow me to gain invaluable experience to prepare for a career as an independent researcher, including grant writing and team management, which would otherwise be difficult to obtain due to teaching commitments. Together, this fellowship and supported training will aid in my career goals of robust research on the conflict and health nexus that can fuel preparedness, support advocacy, and reduce morbidity and mortality.

NSF Awards · FY 2025 · 2025-01

Understanding and interpreting the world around us with precision is essential for many modern technologies that enhance safety, efficiency, and quality of life. Radar systems play a particularly important role because, unlike other sensors such as cameras or lasers, they can operate effectively in all weather conditions, including heavy rain, fog, or complete darkness. Despite their promise, current radar and imaging systems face significant challenges, including low-resolution, high-power consumption, and slow calibration, which hinder their full deployment across critical applications. Moreover, the radar signal processing becomes very complex when the radars are deployed in large quantities, which impacts the scalability of radar arrays. This project addresses these issues through innovative designs of scalable radar transceivers and bio-inspired super-resolution techniques. By adopting machine learning algorithms, the data processing inside large arrays is simplified. By enhancing the temporal efficiency, an almost-real-time perception of the environment can be realized. These breakthroughs will enable transformative applications, such as safer autonomous systems, real-time security monitoring, precise environmental analysis, and cutting-edge biomedical diagnostics. An integral component of the project is its commitment to education. Comprehensive outreach programs, including STEM competitions, hands-on demonstrations, and mentorship, will inspire students to pursue careers in science and engineering and will cultivate a skilled workforce, ready to lead in high-tech fields. The research focuses on designing scalable, fully integrated radar transceivers operating at millimeter-wave (mm-wave) and near terahertz (THz) frequencies to address critical challenges in modern radar systems. Leveraging segmented phase-locked architectures to generate synthetic wideband signals, these radar transceivers will achieve enhanced resolution and sensing capabilities. The project aims to: 1) design transceivers capable of operating across multiple frequency bands with precise phase and frequency synchronization for improved range resolution; 2) integrate multi-band polarimetric radars with rapid calibration methods to extract detailed object information, including material properties and surface characteristics; and 3) apply bio-inspired resolution enhancement techniques with multiple-input multiple-output (MIMO) radar configurations based on a novel MIMO setup with built-in synchronization for highly accurate imaging. The radar system will utilize advanced machine learning models to further simplify calibration and data fusion from multiple frequency bands and polarization states, enabling near real-time, high-resolution imaging in noisy or cluttered environments. Validated through extensive simulations and experiments, the research will address limitations such as high power consumption, large form factors, and low resolution to transform radar applications in security, healthcare, and industrial monitoring. Complementing technical advances, the project includes education and outreach programs, featuring graduate-level courses, industry collaborations, and K-12 STEM initiatives. Students will engage in hands-on activities such as radar design competitions, generating training data for machine learning models, and workshops to prepare themselves as the future generation of innovators in mm-wave and THz 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 2025 · 2025-01

This award provides support for the 30th Southern California Geometric Analysis Seminar (SCGAS) meeting, which will be held during the Winter quarter of 2025 at the University of California - Irvine. The SCGAS is an annual two-day conference that rotates between the University of California - Irvine and the University of California - San Diego. The conference features talks given by acclaimed researchers and promotes interactions among members of the Southern California mathematics community working in the field of geometric analysis and related areas. It further seeks to introduce to graduate students and postdoctoral fellows some of the most exciting recent developments in geometric analysis. This funding award is geared towards supporting and encouraging the participation of graduate students and recent PhDs, especially women and under-represented minorities, by providing them the necessary travel support to attend the conference. Geometric analysis is an important area of modern mathematics and is related to many other branches of mathematics. Using analysis as its main tool along with differential geometry, topology, and algebraic geometry as foundations, geometric analysis has solved a large number of problems in global geometry, topology, several complex variables and mathematical physics. As the success of the first 29 meetings have demonstrated, the SCGAS conference has now become an important and anticipated event for the Southern California region and has also attracted a substantial number of participants from the rest of the country each year. Indeed, it is the unique annual meeting of its kind in the Southern California area, and continues to foster interests in geometric analysis at all levels. In the years past, interactions among the participants during the conference have led to a number of new collaborations and research projects. The web site for the 30th Southern California Geometric Analysis Seminar may be found at https://www.math.uci.edu/~scgas/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project focuses on making industrial processes more sustainable and efficient, particularly for creating propylene, a key chemical used in many commercial products. Producing propylene is challenging because current methods waste carbon and require significant energy, which is not sustainable. This research explores a novel way to improve the process by studying how induction heating, which is a method of generating heat using magnetic fields, can enhance the process reaction and make the process more efficient. This will result in a reduced carbon footprint and additional investments in renewable energy technologies. The project also includes educational outreach to inspire students to learn about sustainability and decarbonization, helping build a greener future. The goal of the CAREER project is to understand the influence of induction heating on the bonding and activity of industrially relevant catalysts via propane dehydrogenation (PDH) to propylene. The objectives of the project are: (1) to determine the effects of localized heating on PDH activity; (2) to elucidate the surface reconstruction of platinum atoms under induction heating; and (3) to establish the impact of the magnetic field on the spin states of electrons on the bonding. This investigation will isolate thermal and magnetic effects to clarify the reaction mechanism and kinetics, as well as ascertain the local coordination environment of platinum and cobalt in the presence of magnetic fields. The CAREER project will validate the impact of the electromagnetic field on the spin state during PDH, which can minimize further hydrogenation and enhance the selectivity of propylene. Such efforts will serve as a foundational pillar toward the investigator’s long-term career objective of comprehensively understanding the dynamic environment of active sites, facilitating precise analyses of reaction pathways and kinetics, in addition to the development of sustainable catalytic processes aimed at decarbonizing the chemical industry. 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 · 2024-12

PROJECT SUMMARY People who identify as Black American have higher rates of cognitive impairment, and are twice as likely to be diagnosed with Alzheimer’s disease and related dementias (ADRD). However, Black communities have largely been excluded from clinical trials and research studies delaying research progress of ADRD risk factors and interventions. One area to improve participation in ADRD research and clinical trials from Black individuals is community engagement. The goal of this R34 proposal is to generate socially- and culturally-aligned approaches for successfully engaging Black communities in brain health research, and apply these techniques to build a Black Aging Cohort in Southern California (Los Angeles County and Orange County). The approach will utilize a mixed-methods research design (i.e., qualitative and quantitative) to identify structural and social determinants of health (SDOH) that directly affect Black communities and the individualized neighborhoods they reside in, and how these factors impact their willingness to participate in ADRD research studies and clinical trials. First, we plan to identify and engage stakeholders in Los Angeles County and Orange County to share and align health equity strategies for brain health. The goal of these relationships is to develop citizen panels (a small set of community stakeholders who possess a knowledge of community contexts) and a community equity board (community members who represent the community) who we can collaborate with to build awareness and understanding of brain health in the community and develop best practices for community engagement. Second, we will utilize existing online databases and tools to assess SDOH that are directly impacting Black neighborhoods. This research will provide census- and government-based indicators of structural factors and SDOH that may be barriers for research participation. Finally, we will conduct focus groups to determine SDOH barriers and facilitators that relate to participation in ADRD research and/or clinical trial studies. This aim will provide direct perspectives from Black American residents about available resources, the inequalities currently impacting their neighborhood, and how they affect their health and ability to participate in research. Focus group findings will provide context for identified structural factors and SDOH identified from the Citizen Panels, community equity boards, and online databases. Completion of these aims will generate neighborhood-specific, culturally tailored community engagement practices to further enhance our relationships with Black communities. Sustained relationships will further help the investigators develop a community informed health equity research plan for a future R01 application.

NSF Awards · FY 2024 · 2024-12

This project will quantitatively assess the influence of salt on the aqueous partitioning of water-soluble organic gases in the atmosphere. The work will be conducted through field and laboratory measurements, reanalysis of prior atmospheric chemistry field campaign data, and the development of parameterizations for implementation in chemical transport models to improve predictions of atmospheric trace gases and aerosols. This work could improve the understanding about how polluting aerosols and gases are formed in the atmosphere. The research has the following objectives: (1) Measure the effects of atmospherically relevant inorganic salt mixtures on partitioning of ambient water-soluble organic gases in diverse environments; (2) Characterize the salting effects on partitioning of key oxygenated organic compounds through detailed laboratory experiments; (3) Explore in detail evidence for salting impacts on organic compound partitioning through reanalysis of atmospheric chemistry field campaign data; and (4) Develop a salt-influence parameterization for organic partitioning based on laboratory findings and conduct simulations of the U.S. This effort will support the training of a postdoctoral scholar and a graduate student at the University of California Irvine and both graduate and undergraduate students at the University of Maryland Baltimore County. 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 · 2024-12

This award supports research in differential geometry focusing on Ricci flows. These flows are defined on manifolds equipped with a metric, that is to say, a way of measuring distance. A Ricci flow is a geometric partial differential equation for Riemannian metrics. The Ricci flow tends to evolve an initial metric into a more homogeneous one. The singularity analysis of Ricci flow is a central subject, as it helps to understand the geometry and topology of manifolds. The most remarkable application in this direction is the resolution of the Poincare conjecture and the Geometrization conjecture by Perelman. Many of the Ricci flow singularity models are Ricci solitons. Recent examples of solitons constructed by the PI look like flying wings. The PI will study the geometry of all 3-dimensional steady Ricci solitons and try to classify them by their asymptotic limits. In addition, the PI will study the higher-dimensional steady Ricci solitons and see if they can arise as singularity models. The research project is split into two projects. The first project is to prove the O(2)-symmetry of all 3-dimensional steady Ricci solitons. This includes showing that the Bryant soliton is the unique 3-dimensional steady Ricci soliton that is asymptotic to a ray. This extends a previous result of the PI in which one assumes the O(2)-symmetry of the soliton. The PI developed some methods that may be extended to the more general class of ancient collapsed Ricci flows in dimension 3. In particular, the PI will investigate the symmetry of the ancient collapsed Ricci flows in dimension 3 and aim at classifying them by certain 2-dimensional limits. The second project is a continuation of the PI's work on the existence theory of Ricci flows coming out of non-compact initial manifolds. The PI will investigate the applications of non-compact Ricci flows in topology and geometry. 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 · 2024-12

Calcium oxide is a highly abundant, inexpensive material that can be used to capture CO2 from the atmosphere and from combustion exhaust. The key drawback to using calcium oxide is that the material undergoes changes during its use, which reduces its service lifetime. This project will use computational modeling and experiments to study the fundamental chemical reaction between CO2 and calcium oxide. The results of these studies will be used to design processes that increase the amount of CO2 calcium oxide absorbs in practice and to increase calcium oxide’s service lifetime. The research team will collaborate with local middle and high school teachers to develop new educational activities and will introduce students and teachers to concepts in materials science, energy storage, and CO2 capture. This proposal integrates computational modeling with atomic-scale in situ electron microscopy to advance understanding of chemical ‘looping’ reactions between CO2 and scalable earth-abundant sorbent materials—namely CaO-based sorbents. The guiding thesis is that sorbent cycle life and CO2 uptake capacity can be extended through rational design of thermal schedules and compositions. This ‘precision temperature control’ approach is expected to thermally activate calcination (sorbent regeneration) and carbonation reactions without excessive heating known to cause sorbent deactivation by particle sintering and surface area loss. By coupling atomic-scale simulations and in situ experiments under reaction conditions, thermodynamic driving forces and key pathways governing kinetics will be elucidated, enabling design of sorbents, heat treatments, and efficient thermal processes for long sorbent life and compatibility with concentrated solar thermal energy. Research is structured in four aims. Aim 1 is to develop a multiscale computational framework to obtain atomic-level mechanistic understanding of CaO+CO2↔CaCO3 looping cycles and validate the framework experimentally using atomic-resolution and in situ gas cell (scanning) transmission electron microscopy. Aim 2 is to extend and experimentally validate the modeling framework to elucidate the role of key humidity derived calcium hydroxide (Ca(OH)2) intermediates on looping reaction mechanisms and cycling stability. Aim 3 is to extend and experimentally validate the framework to assess the role of performance-enhancing chemical additives/dopants on looping reactivity and cycling stability. Aim 4 is to extend and validate the framework to assess the role of particle-particle GB interfaces on looping reactivity and cycling stability. 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 · 2024-12

Non-native species have been introduced to ecosystems worldwide by accident and intentionally. For example, mountain lakes have been stocked with non-native fish that are ecologically disruptive predators. While much is known about the impacts of these fish on the sensitive alpine ecosystems, there is little known about the recovery trajectories once fish are removed. After fish removal, a succession of different prey species may reestablish in the lakes, resulting in some lakes that look like natural fishless ecosystems, while other lake communities may not recover to a pre-invasion condition. This project examines the return of the former prey species and delves into the functional traits of those prey species and ecosystem functions crucial for ecosystem resilience. The research will provide new insights into the connections between taxonomic and functional recovery. The project’s focus on functional redundancy as a vulnerability indicator will contribute to the broader understanding of ecosystem resilience. Using a combination of large-scale lake surveys and experiments, this project will elucidate fundamental principles of community recovery and establish a robust foundation for trait-based conservation in the face of unprecedented and pressing environmental challenges. Additional impacts include educating local communities about natural history and ecological research and creating an educational resource to train the next generation of ecologists in multivariate data analysis. Community assembly frameworks, centered on taxonomic units, inadequately predict ecosystem function due to context-dependent species traits, leaving a gap in our understanding of ecosystem dynamics. This project bridges the gap between taxonomic and functional perspectives in ecology, recognizing the importance of traits in driving ecosystem function. The mountain lakes of the Sierra Nevada in California will be used as a natural laboratory for studying community reassembly following non-native fish removal. Plankton and trait data from 421 lakes will allow a robust comparison of the recovery trajectories of zooplankton community taxonomic and functional structures following fish eradication. To complement the survey data, a replicated whole-lake fish removal will examine the short-term dynamics of zooplankton recovery. Finally, a large-scale mesocosm experiment will determine how communities with varying levels of functional redundancy respond to disturbance, testing the utility of functional redundancy as a community vulnerability indicator. 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 · 2024-12

NONTECHNICAL SUMMARY This award supports theoretical and computational research and education to advance the understanding of materials in which strong interactions among electrons lead to interesting and useful properties. Among these is superconductivity, a quantum state of many electrons in a metal that is characterized by the absence of all electrical resistance. Understanding how superconductivity arises in a family of materials called the cuprates, which are composed of stacked planes containing copper and oxygen atoms, is particularly important because the superconductivity occurs at relatively high temperatures, accessible with inexpensive types of refrigeration. Physicists try to understand such strongly correlated materials using simplified models, where if the model is correct, the solution of the model matches the experimental properties. For the cuprates, since their discovery in 1987, most of the attention has focused on one model, the Hubbard model. Unfortunately, the Hubbard model can only be solved approximately through numerical simulation on supercomputers, and even that is extremely challenging. Conflicting results have been obtained by different groups and simulation approaches for decades. In the last few years, this situation has changed; now, by combining different simulation approaches, consensus on this problem is emerging. In work supported by the previous award, the PI in collaboration with several other groups worldwide has numerically solved the Hubbard model accurately enough to answer the question: Can the model give a correct but simplified description of cuprate superconductivity? The answer turns out to be yes, it does: the simulations obtained superconductivity of the right type with stronger superconductivity for the materials where it should be stronger. This current renewal grant will continue this project, investigating other microscopic properties in addition to superconductivity, and relating all the properties in more detail to what experiments show for specific cuprate materials. One of the most challenging difficulties in programming either a quantum computer or a supercomputer to solve the quantum mechanical equations of molecules, for applications such as drug design, is the complexity of the equivalent of the Hubbard model, called the "molecular Hamiltonian", which has millions or billions of terms. The investigator and his group have been working on unconventional “diagonal" models with orders of magnitude fewer terms. Previous work has demonstrated the effectiveness of this on very simple molecules, and the current project will generalize this to arbitrary molecules, with the goal of improving both quantum and ordinary computer simulations substantially. With previous NSF support, the PI and his group created the ITensor software library, the most widely used library for tensor network calculations, used in the calculations described above. ITensor continues to grow in its use worldwide, and the team continues to aid its development and plan its future development. TECHNICAL SUMMARY This award supports theoretical and computational research and education in the exciting area of condensed matter physics focused on the study of strong correlation effects in low dimensional systems. These systems exhibit a wide range of behavior, such as high temperature superconductivity, antiferromagnetism, and striped and spin liquid phases. Simulation techniques are increasingly necessary to understand these systems, as they have strong coupling and competing types of order. The PI is the inventor of the density matrix renormalization group(DMRG), one of the most powerful techniques for studying these systems. The PI's group will apply DMRG and related tensor network techniques to a variety of strongly correlated systems. One focus during this period will be studies of stripe order, pairing, and pseudogap behavior in models describing the cuprates. Another focus will be applying time dependent DMRG techniques to study dynamical and finite temperature properties of two-dimensional Hubbard and t-J models, and frustrated spin liquid systems. A third will be developing our Gausslet basis sets for chemically more realistic descriptions of strongly correlated systems. The use of 2D DMRG on cylinders with finite but increasingly wide circumferences has contributed to major recent progress that has been made in the ability to simulate 2D quantum systems. The PI and his group have pioneered these methods, which can produce accurate results on 2D doped or frustrated systems with 200-400 sites. They will increasingly focus on finite temperature and dynamical properties of these systems in order to better connect with experiments. These studies will utilize the minimally entangled typical thermal states algorithm, developed by the PI, to study the relationship between pseudogap behavior, Fermi surface reconstruction, and stripes in Hubbard models, while generating spectral functions for close comparison to experiments. Similar techniques will be applied to study new frustrated magnetic systems with the potential for spin liquid behavior. For the study of the electronic structure of strongly correlated solids and molecules, the nested-Gausslet basis sets developed by the group allow an efficient diagonal approximation for the molecular Hamiltonian. This greatly reduces Hamiltonian complexity, potentially speeding up a wide variety of calculations both on classical and quantum computers. Previous work could only treat simple, linear molecules. The team will continue to develop these methods to apply to any molecule or solid. The algorithms and software developed by PI and his group have had a very broad impact on a variety of fields, including chemistry, computer science, numerical analysis, and machine learning. Under previous NSF support, a notable success was the development of the Intelligent Tensor (ITensor, available at ITensor.org) library, now broadly used for DMRG, matrix product state, and related tensor network methods. The project will continue to support the development of ITensor, particularly to utilize continuing advances in the software field. 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 · 2024-12

This project aims to investigate both the security and robustness of Collaborative Autonomous Driving (CoAD) to improve the safety and resiliency of connected and autonomous vehicles (CAVs). Despite being an emergent trend, CoAD systems, consisting of collaborative CAVs and Roadside Units (RSUs), are a new type of cyber-physical systems (CPS) that have received little attention in the research community, especially in terms of their security and resiliency. To conduct the proposed research, the proposers will build upon the team’s complementary expertise in a wide range of topics including vehicle security, Vehicle-to-Everything (V2X) security, adversarial attacks to the AI-powered perception subsystem, formal methods and verification, robust control, and end-to-end evaluation. The project will take a systematic approach and develop a comprehensive framework when examining new attack vectors/surfaces in the CoAD systems, and propose novel mitigation and defense mechanisms. This project is an integrated effort by two PIs from the University at Buffalo (UB), and UC Irvine (UCI) from the US side, and two PIs from the Indian Institute of Technologies (IIT) at Kharagpur (IIT-KGP) and Jodphur (IIT-J) from the India side. The project is expected to result in joint publications as a part of dissemination efforts, joint mentoring of students by the US and India PIs, and new datasets, as well as increased public awareness of cyber-security threats and trust in the resilience of autonomous driving. In addition, new course and publicly available materials based on research results will be developed to attract and train students, including under underrepresented minority students. 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 2022 · 2024-11

PROJECT SUMMARY Asprosin, a newly identified glucogenic hormone through study of neonatal progeroid syndrome (NPS, or Marfan lipodystrophy syndrome), may have both therapeutic and diagnostic implications for obesity and diabetes. The gene encoding asprosin, FBN1, has unique extreme 3’ mutations resulting in hypoglycemic symptoms and low plasma insulin levels in a few patients with the rare NPS. Considerable evidence has recently demonstrated asprosin’s direct role in hepatic glucose production modulation, and asprosin immunologic neutralization in the treatment of obesity and diabetes in mouse models. The direct effect of asprosin on type 2 diabetes (T2D) incidence in humans has not been investigated in human populations. The current application aims to investigate the potential causal role of asprosin for obesity and T2D development in two large and high-quality prospective cohorts of men and women. We plan to integrate data on relevant genetic variations, biochemical markers, and clinical phenotypes of obesity and T2D incidence in the national Women’s Health Initiative (WHI) and the men’s Health Professionals Follow-Up Study (HPFS). Both WHI and HPFS are long-term prospective cohorts that have been funded by the NIH with detailed and high-quality dietary, lifestyle, clinical, biochemical, and genomic data, and a large number of well-characterized and validated incident T2D cases using standardized protocol consistently over 20 years of follow-up. All incident T2D cases with existing genetic data and fasting blood samples (n1=2,615) matched by an equal number of controls (n0=2,615) randomly selected from the same WHI cohort of women, and 600 case-control pairs of men (n’=1,200) will be included using the identical nested case- control design. Genotyping and validation analyses will be performed in an additional 1,076 T2D case-control pairs (n”=2,152) without existing genetic data. Adopting both standard statistical methods and the cutting-edge Mendelian randomization method for causal inference, we will leverage these exceptional resources and the substantial investment of time and effort by WHI/HPFS study investigators over the past two decades to investigate, in a most cost-efficient and timely manner, the effect of this novel and promising hormone – asprosin – for obesity and T2D development. We will be the first to investigate 1) the distribution of plasma asprosin levels in men and women, 2) the genetic variations affecting asprosin functions in diverse human populations, and 3) the potential causal relation between asprosin and obesity and T2D in human populations of diverse ethnicity including white or Caucasian American (CA), African American (AA), and Hispanic American (HA).