Yale University

NIH Research Projects · FY 2025 · 2024-08

Project Summary / Abstract Over the last decade, high-throughput B cell and T cell receptor repertoire sequencing has become a fundamental method for investigating adaptive immune responses. The Immcantation framework, consisting of open-source Python and R packages, provides a comprehensive analytical ecosystem for this Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data analysis, covering critical steps like pre-processing, clonal relationship identification, lineage reconstruction, and somatic hypermutation analysis. This framework has gained widespread usage in infectious and immune-mediated disease research, with over 100,000 downloads in 2022. However, as sequencing technologies advance and datasets grow larger, there is a need for scalable computational workflows combining the individual analysis steps. To meet this demand and support the expanding user and developer community, we developed nf-core/airrflow (AIRRflow), a Nextflow workflow that integrates the individual Immcantation tools into a high-throughput analysis pipeline. The workflow offers parallelization, scalability, and compatibility with diverse computing infrastructures, including High-Performance Computing (HPC) clusters and commercial clouds. It is part of the nf-core project, a community-driven effort collecting Nextflow pipelines with an emphasis on robustness and reproducibility. This proposal aims to enhance AIRRflow usability, findability, accessibility, interoperability and scalability to cater to a broader audience in the infectious and immune-mediated disease (IID) research community. The proposed aims include adding new functionality to handle the integration of data from large numbers of subjects and facilitate interpretability, including embedding methods that translate receptor sequences to length-independent numerical vectors suitable for machine learning, determination of convergent responses across infectious and immune-mediated diseases, and annotation of receptor specificity leveraging public databases like IEDB. To enhance accessibility of data from public databases and ensure compliance with FAIR software principles, we will include automated data download from the Sequence Read Archive (SRA) and ImmPort, expand the supported data types to RNAseq and single-cell RNAseq, implement scalability tests, and make the workflow metadata accessible through suitable portals like the NIAID Data Ecosystem Discovery Portal. We will actively work towards expanding the user base by offering hands-on trainings, tutorials targeting relevant use cases for the IID research community, and community engagement events, gathering feedback through multiple channels including surveys, GitHub issue tracking and slack. These improvements will make AIRRflow an even more valuable resource for researchers in the IID community.

NIH Research Projects · FY 2025 · 2024-08

Project summary The translation of mRNAs into protein is a fundamental step in gene expression that is regulated by diverse cellular signals. How these regulatory mechanisms are used to orchestrate changes in gene expression and cellular function remains poorly understood. We have been studying this question through examination of the mTOR Complex 1 (mTORC1) signaling pathway, a master regulator of growth throughout eukaryotes. This pathway senses nutrient signals and responds by activating the cellular biosynthesis machinery to drive growth. Deregulation is linked to human diseases ranging from cancers to neurological disorders. A central function of mTORC1 is to activate “cap-dependent” translation through the eIF4F translation factor. Over the past five years, my laboratory has shown how this mechanism is used to control the translation and stability of hundreds of mRNAs with essential growth functions, including nearly all ribosomal proteins. More recently, we identified hundreds of mRNAs that are hyper-dependent on cap- dependent translation for unknown reasons and thousands that access “cap-independent” initiation mechanisms that remain unclear. Going forward, our research program seeks to answer several basic questions that emerge from these observations. First, what mRNA features define dependence on cap-dependent and cap-independent translation mechanisms and how are they detected? Second, how does regulation of cap-dependent translation trigger global and specific changes in mRNA stability? Third, how does variation in the structure of the transcriptome (e.g. alternative promoters) specialize the post-transcriptional regulation of mRNAs across tissues in vivo? We propose to tackle these questions using a combination of transcriptomic strategies, massively parallel reporter assays, and bioinformatic analyses in cells and in vivo that we have established over the last five years. Our ultimate goal is to fully understand the molecular systems that control growth-regulated gene expression, establish their function in the cellular growth process, and link their function to growth-related physiology. We expect these efforts to yield insights into basic principles of gene regulation that are used to adapt cells and organisms to changing nutrient availability.

NIH Research Projects · FY 2024 · 2024-08

Coxiella burnetii is an intracellular pathogen that causes the human disease Q-fever. This project is focused on the novel mechanisms Coxiella has evolved to manipulate the host cell. The Coxiella Dot/Icm type IVb secretion system is essential for intracellular replication. The goal of this project is to understand how effector proteins delivered into host cells by the Dot/Icm system enable Coxiella to replicate in a hydrolytic lysosomal organelle and evade host detection. We have developed genetic tools to identify the important Coxiella proteins that are required for host manipulation. This project will leverage these genetic tools in combination with molecular and biochemical approaches to investigate how these effector proteins interfere with host pathogen sensors, promote biogenesis of the unique Coxiella-containing vacuole (CCV), and facilitate infection in animals. Knowledge gained from these studies will lead to a greater understanding of pathogen adaptations that enable Coxiella to infect mammalian hosts and the immune pathways that are important for controlling intracellular bacterial pathogens. Specific goals include functional analysis of the proteins EmcA and EmcB that are involved in suppression of host immune responses, using newly developed genetic tools to define epistatic interactions between effectors required for CCV biogenesis, and to use bioluminescence imaging to measure virulence defects displayed by Coxiella effector mutants.

NIH Research Projects · FY 2025 · 2024-08

The HIV pandemic has grown to ~40 million individuals world-wide. Although Highly Active Anti- Retroviral Therapy (HAART) has been transformative for millions of people, less than half of those infected are on therapy. And while the current medications are typically well-tolerated and efficacious, because of both acute and chronic drug toxicities, drug-drug interactions, and virus resistance, there is always room for novel HIV therapeutics, especially for those considered first-in-class. Rev is an essential regulatory protein of the virus, which multimerizes on the Rev-response element or RRE to export intron-containing HIV mRNAs from the nucleus to the cytoplasm. Although there are some compounds in clinical trials that purportedly interfere with Rev function, there are no FDA-approved agents that specifically target Rev’s known mechanism of action. Notably, an anti-Rev compound should reduce the amount of both cytoplasmic and virion-associated unspliced or genomic RNA and also plasma viral loads in the absence of viral replication, an activity that none of the currently FDA-approved drugs possesses. We have developed a cell-based firefly luciferase (FFLUC) complementation system that quantifies Rev-Rev interaction but then transitioned over to a cell-free one that has an excellent Z’ (Z factor) of 0.64 in 384-well format. Here we first wish to further optimize the assay to then permit testing of compounds in 384-well format, in collaboration with Yale Center for Molecular Discovery (YCMD), which has multiple collections of compounds and requisite instrumentation to allow fully automated testing of ~125,000 small molecules. These compounds will initially be screened at a single concentration, and positives will be retested at a range of concentrations, in replicates. Secondary assays for specificity of inhibition of Rev-Rev interaction include non-inhibition of FFLUC activity and transcription, non-inhibition of unrelated protein-protein interaction, and inhibition of Rev function using a variety of assays, including cell-free multimerization, production of Gag, and both X4 and R5 HIV replication in vitro. Therapeutic index (TI) of any hits will be determined using a cell-based assay. Based upon these results, the physicochemical properties of any hits will be assessed and in silico hit expansion along with any necessary synthesis will be achieved by the commercial entity Life Chemicals. These select hit compounds will be retested in the above assays in order to identify even more potent and active drug-like compounds. At the end of two years it is hoped that a number of advanced hit and close-to-lead compounds will have been identified that have high TI (>100) and specifically prevent Rev-Rev interaction and thus interfere with known Rev function, which will allow future medicinal chemistry testing and iterative in vitro and in vivo studies to identify lead compounds of enhanced potency, activity, and specificity. Thus, a long-term goal of this work is to identify a first-in-class anti-Rev molecule that is capable of inhibiting the function of an essential, regulatory gene of HIV, which will expand the armamentarium of therapeutics against this critical virus.

NIH Research Projects · FY 2024 · 2024-08

ABSTRACT Protein kinases function in cell signaling through regulated phosphorylation of specific substrates. Current methods allow one to comprehensively identify the substrates of a given kinase, yet we lack general methodology for determining which substrates are critical for specific functions of that kinase. Likewise, approaches for probing the functional impact that phosphorylation has upon such key substrates are lacking. Here, we propose to develop technology allowing for directed phosphorylation of a single protein kinase substrate at defined sites. With this approach, we engineer a kinase mutant that can only phosphorylate a designer allele of one of its substrates. To accomplish this goal we will leverage knowledge of kinase phosphorylation site specificity gained through our recent comprehensive analysis of the human serine-threonine kinome and consequent understanding of the structural determinants of kinase selectivity. We will establish the feasibility of this technology using the tumor suppressor kinase LKB1 as a model system. LKB1 has a well-defined substrate repertoire, phosphorylating and activating a set of 14 downstream protein serine-threonine kinases exclusively on Thr residues. We will engineer LKB1 so that it instead phosphorylates Ser residues and does not act on its endogenous substrates. We will then engineer compensating mutations in one of its key substrates, the AMP- activated protein kinase (AMPK), to restore phosphorylation and activation by mutant LKB1. Human cancer cell lines co-expressing these alleles will be analyzed for LKB1-dependent activation of AMPK to the exclusion of other LKB1 substrates. To determine whether this system faithfully recapitulates endogenous signaling, we will examine phosphorylation of established substrates downstream of AMPK, and we will globally map changes to the phosphoproteome in response to AMPK activation. At the outcome of these studies, we will have established a system in which a single substrate of a kinase is phosphorylated with identical dynamics as the native substrate. Future studies will expand this approach into other LKB1 substrates, facilitating studies of how LKB1 functions as a tumor suppressor. We will ultimately apply this technology generally to other protein kinases implicated in cancer whose critical substrates are currently unknown.

NIH Research Projects · FY 2025 · 2024-08

This proposed R25 program, Comprehensive Advancement in Research Education and Training for Social Determinants of Health (CARES), at the Yale University School of Nursing is designed to address existing gaps in the education and training of nursing researchers in Social Determinants of Health (SDOH). The purpose of this program is three-fold: First, to orient emerging researchers to recognize, appreciate, and incorporate into their research concepts and principles of SDOH that influence and shape individual and population health. Second, research methods will be provided and developed toward disentangling multiple policy and program influences and their health effects. Third, approaches will be shared and developed toward structural interventions on these upstream factors as they influence health. The curriculum is intensive and includes the development of academic and community networks, which are designed to accelerate them becoming established investigators. The education and training activities are in three segments that surround an intensive summer research residency. The first segment involves a monthly online introduction (over five months) to key concepts and frameworks for SDOH research. The second is a month-long intensive in-residence (in-person and hybrid) Summer Institute that delves into research method topics while networking with and mentoring and culminating in a proposal concept development. The third segment is regular and on-demand coaching with mentors and SDOH research community-building with CARES cohorts through engagement in an active online network hub during and post-program. The outcome of the program is the delivery of the SDOH research courses, with an expansion of a social network of researchers that manifests peer-reviewed publications and competitive grant proposals. With our recruitment plan (via targeted social network and professional and academic networks), we will host three training cohorts (6-8/cohort) of early career nursing scholars over the three-year program period. Eligibility and selection for the program include the applicant’s research interest on SDOH research, evidence of institutional support, and a match of CARES’ mentor and scholar’s interests. Evaluation includes ongoing assessment of progress, mentoring, and productivity as reviewed by the Steering Committee and the External Advisory Board. Innovative aspects of the program include a focus on interdisciplinary, simulation-based, and community-based training and mentoring approach. The CARES program is grounded in the Healthy People 2030 framework and the NIH SDOH Research Framework and seeks to establish a robust foundation in SDOH research, synthesize evidence across disciplines, enhance research methodology competencies, and cultivate effective participant engagement and ongoing support strategies. Ultimately, the CARES program will empower nurse researchers to conduct rigorous and impactful SDOH research and promote population health.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Severe infection is one of the top three causes of neonatal mortality, according to the World Health Organization. Of infection-related deaths, viruses cause 6.5% in neonates and 19.4% in infants. In children under five years of age, infections with respiratory syncytial virus account for particularly high morbidity and mortality rates. There is an unmet need to understand better and modulate the unique biology of early childhood immune cells, including cytotoxic CD8 T cells that act as a double-edged sword by protecting from intracellular pathogens including viruses while also contributing to immunopathology during severe viral bronchiolitis. The functional properties and the molecular basis for the unique features and remodeling of CD8 T cells in early childhood remain largely undefined in humans, limiting the advancement of therapeutic modulators. Neonates have lower CD8 T counts than adults, and their CD8 T cells have a shorter life span. Moreover, studies in the field have found that neonatal CD8 T cells undergo rapid proliferation and terminal differentiation as effector cells at the expense of forming long-lived memory cells. Both neonates and patients with an immune deficiency called Activated PI3K-Delta Syndrome (APDS) suffer from severe viral infections and have similarly atypical CD8 T cell function. We hypothesize that early-life CD8 T cells have a decreased threshold for PI3K/mTOR activation, which enhances effector activity and augments short-lived effector cytotoxic T cell fates that can contribute to immunopathology in severe viral bronchiolitis. By comparing human neonatal, child, young adult, and moderate vs. severe RSV bronchiolitis patient CD8 T cells in vitro, Aim 1 will define mechanisms driving increased proliferation, glycolysis, and cell death in early-life CD8 T cells and Aim 2 will determine roles of PI3K/mTOR pathways in T cell function during RSV bronchiolitis and their relationship with disease severity. This research will lead to further insights into how early-life CD8 T cells differ functionally from those of adults and what pathways could serve as targets for interventions to improve immune responses and outcomes of severe early-life infection. Nina Brodsky, M.D., is an Assistant Professor in Pediatric Critical Care at Yale University School of Medicine. Her career goal is to become an independent principal investigator leading translational research efforts and to become proficient in developing targeted translational therapies to improve outcomes for patients who suffer from severe infection and immune-mediated diseases. This career development award will allow Dr. Brodsky to 1) hone her skills in immunologic, omics, and translational research methods under the direction of a mentoring team that is at the forefront of research in T cell function, signaling and metabolism and 2) identify promising molecular targets for intervention to improve outcomes of severe infection for her future research.

NSF Awards · FY 2024 · 2024-08

The PI studies problems of geometry using methods imported from particle physics. This project is divided into two major parts. In the first part, the PI aims to prove new formulas for solutions of an equation first studied by Einstein, describing the geomery and curvature of space-time. The second part concerns the physics of two-dimensional systems which have "scale invariance," meaning they look the same both at short and long distances. The PI aims to study these systems by reducing them to simpler ones which can be solved exactly. The results of this work will be disseminated broadly both in the mathematics and high-energy physics communities, helping to bring these two areas closer together. The PI will continue outreach through expository lectures and articles. The project will also contribute to the training of graduate students in mathematics. In joint work with Davide Gaiotto and Greg Moore, the PI introduced a conjectural picture of the hyperkahler geometry of moduli spaces of Higgs bundles. Parts of the conjecture have been verified over the last several years, in the work of various authors, including the PI. Building on this recent progress, the PI will make a direct attack on proving the conjecture, as well as a detailed numerical study of the hyperkahler metric in a particular example. The PI will also employ a new approach to the construction of conformal blocks for the Virasoro vertex algebra and more generally the W(gl(N)) vertex algebras. Conformal blocks are much-studied functions arising in two-dimensional conformal field theory, which are notoriously difficult to describe in explicit terms. The new approach the PI will use involves a new technique of abelianization, which relates complicated vertex algebras to simpler ones. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Abstract This career development award proposal aims to create an immersive training experience in the context of studying the genetic etiology of substance use disorders (SUDs). The completion of the research and training aims will provide the applicant with a unique skillset at the intersection of psychiatric genetics, SUD epidemiology and health disparities, SUD psychopharmacology, clinical informatics, and bioinformatics. Genome-wide association studies (GWAS) have been valuable for genetic discovery and dissecting the biology of SUDs, but improvements to study design are needed. First, SUD GWAS typically account only for diagnostic status for the focal SUD of interest; however, substance co-use and SUD co-occurrence are common and may impact interpretation of findings. Second, SUD GWAS often rely on diagnostic codes that are included in electronic health records (EHRs) but miss other substance use not captured by a SUD diagnosis. EHR-based substance toxicology data can provide superior resolution of substance use and assess if someone has been exposed to a specific substance. Third, substance exposure information is important – a person must initiate use of a substance for a SUD to develop. To assess a person’s genetic liability for a SUD requires knowing if that individual has been exposed to that substance. Defining substance-exposed controls solidifies that cases and controls are accurately designated and allows for the isolation of the genetic effects specific to SUD risk. Fourth, GWAS have been largely performed in European-ancestry samples. Efforts have underscored the need to extend GWAS to diverse ancestries, but insufficient attention has been given to racial disparities in SUD GWAS. The inclusion of genetically diverse populations combined with examining social determinants of health are important for addressing health disparities in SUD GWAS. This proposal seeks to address these limitations using the Million Veteran Program (MVP) sample – a large and diverse biobank that includes genetic, environmental, and medical information including EHRs that contain SUD diagnoses and drug toxicology data. EHR data will be used to identify diagnosed SUDs and co-occurring SUDs for each MVP participant. Drug toxicology data will be used to assess for additional substance use. Combining EHR SUD diagnostic codes and toxicology results will provide a comprehensive summary of substance use for each MVP participant. This will benefit SUD GWAS in terms of: (1) modeling patterns of SUD co-occurrence and substance co-use; (2) providing substance use specificity that often goes undocumented by EHR codes alone; and (3) the ability to identify substance-exposed controls that have used a substance but do not have a SUD diagnosis. Reducing health disparities in SUD GWAS will be addressed through the inclusion of all available genetic ancestry groups and examining disparities in rates of toxicology test administration across self-reported racial and sociodemographic backgrounds. This proposal will provide multidisciplinary training to help launch the applicant’s independent research career and holds promise to advance our understanding of SUD etiology.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Human immunodeficiency virus (HIV) acquisition often co-occurs with drug use disorders (DUD), particularly in the context of injected drug use. Individuals with DUD or those living with HIV/AIDS have been found to commonly experience changes in the balance of gut microbiota (GM) in their gastrointestinal tract. The gut, which is considered the body's largest immune organ, harbors trillions of microorganisms that influence how our body responds to neurological and immune challenges or inflammation. Thus, DUD, HIV, GM, and immune responses form a complex interplay, influencing each other. To elucidate the causal relationship of HIV acquisition, we will leverage genome-wide association studies (GWAS) summary statistics and existing datasets. Our primary analytical strategy involves employing Mendelian randomization (MR) combined with mediation analysis. Utilizing MR analysis, which relies on GWAS summary statistics, we will deploy static GWAS-identified genetic markers as instrumental variables to address confounding factors while inferring causal effects. We will examine four distinct DUDs, including use disorders of cannabis, cocaine, tobacco, and opioids. Our investigation will analyze a large-scale human GM GWAS, GWAS of target immune-related biomarkers, and our ongoing HIV GWAS. Within the MR framework, our outcome trait is HIV acquisition, and DUDs are exposure variables. Through univariate MR and multivariate MR, we aim to identify drug-specific causal effects on HIV risk and immune responses. We will also investigate genetic correlations among these traits. Furthermore, we will infer the causal mediation relationship among DUD, GM, and immune responses in HIV risk, providing insights through the assessment of causality and mediation effects. In summary, finding causal effects between DUD and HIV acquisition is essential for designing effective prevention and intervention strategies, guiding public health policies, understanding the mechanisms involved, and ultimately reducing the burden of HIV in populations affected by drug use.

NSF Awards · FY 2024 · 2024-08

Reverse osmosis (RO) is recognized as the most energy-efficient seawater desalination technology. To address increasing societal demands for water, RO is being used more broadly for water recovery from unconventional sources such as brackish groundwater and industrial wastewater. Despite RO’s advantages, mineral scale formation on RO membranes remains a critical challenge as it adversely affects membrane performance and lifespan. Scale formation is even more problematic for unconventional RO applications. This is due to the increased prevalence of silica-based scales in unconventional water that are resistant to commonly used scale inhibitor chemicals. The goal of this project is to address this problem by developing effective inhibitors for silica scales through an international collaboration with researchers at Ben Gurion University (Israel). The project aims to develop molecular design principles for high-performing polymeric inhibitors. The work is guided by the principle that reactive silica acid-containing molecules polymerize to form clusters (i.e., “scale”) that cover the RO membrane. This scale can thus be controlled by polymeric inhibitors that stabilize silicic acids, thus limiting the rate of silica scale formation. The key to winning the chemical “battle” between silica scale deposition and polymer inhibition is to better understand the molecular-level interactions between the antiscaling polymers and the soluble silica species at the membrane interface. To that end, the project will investigate a host of polymer chemical and physical design properties, followed by synthesis and evaluation of attractive candidates. Successful completion of this project will benefit society by improving RO separation technology for increased water supply, as well as the promotion of clean energy technologies to address critical environmental challenges. Additional benefits to society result from teaching, mentoring, and outreach activities that will create educational opportunities for a diverse group of students to improve the Nation’s STEM workforce. The development of precisely engineered polymers capable of inhibiting silica scale holds the potential to significantly improve the efficiency and extend the lifespan of RO membranes. The goal of this project is to elucidate the essential design principles for effective silica scale inhibitors under operational RO membrane conditions. The study will investigate the individual impact of polymer chain length, composition, and conformation on inhibition efficiency, as well as their detailed mechanistic role in stabilizing soluble silica species. Researchers will investigate scale formation utilizing a state-of-the-art quartz crystal microbalance in conjunction with localized surface plasmon resonance to elucidate mechanistic differences between homogeneous and membrane-based heterogeneous scaling processes. The research project encompasses functional polymer design and synthesis, antiscaling performance analysis in both homogeneous solutions and RO membranes, kinetic and computational studies on scaling and inhibition mechanisms, and the evaluation of RO desalination performance. The combined outcomes of this research will contribute to establishing a scalable and precise synthesis methodology for obtaining high-performance polymeric inhibitors for silica scaling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY / ABSTRACT The proposed stage II intervention efficacy trail is designed to address two significant co-occurring issues for fathers with substance use (SU) problems: Intimate partner violence (IPV) and child maltreatment together defined as family violence (FV). SU treatment programs are an important avenue to reduce family violence because SU treatment alone does not result in an end to these behaviors. Currently available interventions have had little success in reducing male FV. Fathers for Change, an integrated outpatient intervention, shows promise as an intervention model targeting the intersection of SU and FV. The intervention uses men's roles as fathers as a motivation for change and targets emotional dysregulation as the common factor associated with all three problems. It is composed of 9 core topics designed to increase motivation and reflective functioning and target poor emotion regulation followed by 4 co-parent topics and 5 father-child topics for a total of 18 sessions. This project will test the efficacy of Fathers for Change model in reducing SU and FV compared to a comparable dose of Individual Drug Counseling post-treatment, 3- and 6-month following treatment for fathers with substance use disorders seen in either community or VA SU treatment settings. Emotion dysregulation will be examined as the mechanisms through which Fathers for Change reduces SU and FV. The study will test differential outcomes for high and low risk clients in preparation for implementation in community settings. If Fathers for Change demonstrates efficacy in reducing SU and FV simultaneously the intervention can readily be integrated into the more than 2500 outpatient SU treatment programs nationally. Reductions in SU and FV will have major public health and criminal justice implications.

NSF Awards · FY 2024 · 2024-08

Compounds that contain a transition metal connected to a hydrogen atom, referred to as transition metal hydrides, are critical for the synthesis of fine and commodity chemicals, pharmaceuticals, and precursors to plastics. In this project, Professor Nilay Hazari from Yale University is developing a fundamental understanding of how transition metal hydrides transfer their hydrogen atom to organic compounds, which is a key step in many catalytic cycles. This knowledge is crucial for designing improved and new transition metal hydrides, which will transfer their hydrides faster and with more selectivity to organic compounds. Both experimental and theoretical methods are used to understand the reactivity of transition metal hydrides and one hypothesis being tested is that a model currently used to explain electron transfer reactions can also be used to explain hydride transfer reactions. The research is complemented by Professor Hazari’s involvement in a series of outreach activities related to chemistry generally and catalysis specifically, which cater to students from underrepresented minorities in science. These activities include educating the general public about chemistry through demonstrations, organizing a five-lecture summer program on catalysis for high school students in the New Haven area, and hosting undergraduates to perform research in Professor Hazari’s laboratory. With funding from the Chemical Structure, Dynamics & Mechanisms-B of the Chemistry Division, Professor Nilay Hazari of the Department of Chemistry at Yale University is developing understanding of hydride transfer reactions involving transition metal hydrides. This knowledge provides guidance about the design of catalysts and optimization of reaction conditions for the plethora of reactions that involve hydride transfer, such as carbon dioxide reduction to methanol. Specifically, by measuring the rate of hydride transfer from a metal hydride to an organic acceptor, the kinetic hydricity of a metal hydride is determined. Independently, the thermodynamic hydricity of the same metal hydride is measured using equilibrium exchange reactions or electrochemistry. By correlating thermodynamic and kinetic hydricity, linear free energy relationships are developed. These relationships enable the evaluation of hypotheses relating to whether secondary coordination sphere effects such as hydrogen bonding or electrostatic effects stabilize the transition state for hydride transfer and whether hydride transfer reactions can be modelled using Marcus theory. Density Functional Theory calculations are carried out in parallel with the experimental work and probe the structure and energy of transition states and intermediates. Professor Hazari is actively engaged in outreach programs focused on increasing the representation of students from underrepresented minorities in chemistry. He hosts a public lecture that explains fundamental principles of chemistry through demonstrations, runs a short course on catalysis for high school students, and enables undergraduates from diverse backgrounds to perform research in his laboratory. 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

Large Language Models (LLMs) like GPT-4 have transformed natural language processing and related fields by demonstrating unprecedented capabilities in interpreting human instructions and completing complex reasoning tasks. Representing a paradigm shift in statistical machine learning, these models are trained on extensive text corpora and can perform novel tasks without modifications to their parameters. This project seeks to develop a comprehensive theoretical framework to understand mainstream methods used with deployed LLMs through a statistical lens. The anticipated broader impacts of this research include enriching educational curricula at participating institutions and providing significant training opportunities for both graduate and undergraduate students. Moreover, the project's outcomes are expected to enhance high-impact applications in sectors such as robotics and transportation systems, thereby improving the practical deployment of LLMs in complex decision-making scenarios. Specifically, this research explores the statistical foundations of various prompting methods utilized in LLMs, including in-context learning (ICL) and chain-of-thought (CoT) prompting. The study is organized around three main thrusts: first, deciphering how LLMs perform ICL and CoT as forms of implicit Bayesian inference and understanding how transformer architectures' attention mechanisms approximately encode these Bayesian estimators. Second, the project will develop algorithms to analyze the statistical errors—incurred during pre-training and prompting stages —associated with these prompting-based estimators. The third thrust aims to apply this theoretical framework to real-world applications like robotic control and autonomous driving, formulating principled methods that utilize pre-trained LLMs for complex decision-making. By establishing a robust statistical foundation for prompting-based methodologies, this research aims to advance the field of prompt engineering and contribute to the development of principled methods for using LLMs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

SUMMARY Undiagnosed disease remains a major burden for millions of Americans and their families. Here, we propose the Yale Diagnostic Center of Excellence (YDCoE) as a new site for the Undiagnosed Diseases Network (UDN). The YDCoE builds on the outstanding track record of human genetics, next generation sequencing (NGS) and rare disease discovery at Yale over many decades. To expand access to the UDN, we will partner with multiple community healthcare organizations that provide services for poor individuals who are under/uninsured, all of whom see >90% patients belonging to racial and ethnic minority groups. These partnerships are mutually beneficial, as the YDCoE will provide training and education opportunities in undiagnosed and rare diseases for these organizations. We will work with the DMCC to develop an enrollment plan that utilizes a tiered evaluation strategy. Our highly efficient clinical evaluation approaches and infrastructure will allow us scale clinical capacity to engage more participants over time, a significant portion of which will be covered by community and third-party payer support. For patients who remain without a diagnosis, we will leverage our extensive expertise in NGS approaches, cutting-edge bioinformatics and functional genomics – including in vivo and MPRA approaches - to increase diagnostic yield. Finally, we will conduct additional site-specific studies to enhance clinical evaluation efficiency, enrollment and patient experience. These include novel machine learning approaches to extract clinical information from EHR for more efficient evaluation and diagnoses, an augmented enrollment approach for uniquely vulnerable individuals, and a novel tool to measure patient-reported measures of discrimination in care. We have further secured additional institutional and industry support, which will allow us to prioritize NIH funds for under/uninsured patients. Altogether, our in-house clinical genetics operations together with our extensive network of community and industry partnerships, our expertise in NGS and bioinformatics innovation, our innovative track record in rare disease discovery and the vast clinical knowledge base across multiple specialties at YSM to form a DCoE that is fully equipped to engage a diverse cohort of underserved patients at an unprecedented clinical scale.

NIH Research Projects · FY 2025 · 2024-08

Project Summary Chlamydia trachomatis (Ct) is the most common bacterial sexually transmitted infection worldwide, and public health measures including test-and-treat strategies have been ineffective at curbing incidence and prevalence. As a consequence, Ct pelvic inflammatory disease and sequelae of ectopic pregnancies and infertility continue to be important medical issues. Currently there is no therapy that prevents PID-associated infertility. The central hypotheses of the grant based on Chlamydia muridarum (Cm) mouse model data is that selective TNFR2 blockade will prevent chlamydia PID associated immunopathology and infertility. Enabling this investigation are a humanized inhibitory anti-human TNFR2 monoclonal antibody developed by the Johnson lab and a new humanized TNFα/TNFR1/TNFR2 (hTNF) mouse model developed by Biocytogen. Aim #1: To establish a hTNF mouse breeding colony and compare Cm bacterial shedding and immunopathology scores in hTNF mice with wild type C57BL/6 mice bred in the same facility. Aim #2: Use gene synthesis technology to rapidly generate subclass variants of the successfully humanized rat anti-human TNFR2 26C09 monoclonal antibody to investigate their inhibitory activity in human PBMC activation assays and determine efficacy of mouse equivalents for preventing immunopathology in the Cm-hTNF mouse model.

NSF Awards · FY 2024 · 2024-08

Modern artificial intelligence (AI) and machine learning (ML) systems are trained using massive datasets and complex models combined with optimization algorithms. Traditional "greedy" methods, which make incremental improvements at each step, often fall short in both efficiency and adaptability when faced with problems at this scale. This project proposes a novel framework for algorithm design based on the Hamiltonian dynamics, a fundamental concept in physics and mathematics that uses the conservation principles to describe the interaction of multiple objects. Such dynamics appear naturally in many branches of computational sciences but are rarely used as a fundamental principle in algorithm design. Motivated by emerging challenges in ML, this project aims to develop a systematic methodology that leverages Hamiltonian conservation to solve problems in optimization, random sampling, and game theory. This project has the potential to revolutionize our understanding of computational and statistical problems by introducing a new class of algorithmic principles for training modern ML systems. This project will also advance the curricula for algorithms in computer science and electrical engineering, with unique training opportunities for undergraduates and graduate students, the development of open-source software, and a dissemination of ideas via joint workshops. This project will explore a framework called “the LCP scheme”, which stands for Lift, Conserve, and Project. This proceeds by taking a parameterized decision space, appropriately lifting the problem to incorporate additional variables, applying the conservation property of the Hamiltonian dynamics to update the problem state in the augmented parameter space, and finally projecting the state back into the original space. This scheme provides a fresh perspective for analyzing several known algorithms, and developing new ones, in the domains of optimization and random sampling, as well as to understand the behavior of players in multi-agent systems. This project will develop a robust algorithmic complexity theory for implementing the continuous-time Hamiltonian dynamics as discrete-time sequential procedures with an emphasis on the large-scale modern applications. By introducing concepts such as invariance, conservation, and the principle of least action, this project will provide a more nuanced view of the state evolution of computational objects that can help overcome many limitations of the standard algorithm design paradigm. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY/ABSTRACT There are currently over 100,000 older (ages 50+ years) women living with HIV/AIDs (WLWH) in the United States. Advances in antiretroviral therapy treatment have led to a decline in mortality caused by HIV/AIDS, however, experiences of HIV-related and gender-based stigma continue to adversely impact health outcomes in this population. People living with HIV who experience HIV stigma are more likely to delay seeking care and have depressive symptoms and lower quality of life. Yet, older adult women are understudied in research on stigma and HIV. The purpose of this proposed study is to examine the cross-sectional and longitudinal impacts of HIV stigma and gender-based stigma on cognitive function among older WLWH and identify whether social support is protective against the adverse impacts of stigma on cognitive function. We will use data from the Women's Interagency Study (WIHS), a longitudinal, prospective cohort of WLWH in the United States. WIHS was established in 1993 and has enrolled almost 5,000 WLWH to date. Cognitive function was assessed using multiple standardized and validated neurocognitive assessments. Stigma and social support were also assessed using validated survey scales. For the proposed study, we will include all participants who have completed baseline stigma and social support questionnaires before the age of 50 and at least one (cross- sectional analysis) or two (longitudinal analysis) cognitive assessments. The proposed work will test whether HIV stigma and gender-based stigma are independently associated with current cognitive function and change in cognitive decline, and whether there are interaction effects between HIV and gender-related stigma. We will also examine whether social support is protective of cognitive decline despite experiences of stigma. We hypothesize that 1) self-reported HIV stigma or gender-based stigma will be associated with worse cognitive performance and greater cognitive decline; 2) experiences of both types of stigma will be associated with worse cognitive performance and a greater cognitive decline compared to experiencing either type alone, and 3) among those who experienced high levels of stigma, those who report higher levels of social support will have better cognitive performance and lower cognitive decline compared to those who report low levels of social support. These analyses will be done using mixed-effects cross-sectional and longitudinal regression models adjusting for known risk factors for cognitive decline. Findings from this study will provide much needed information about the extent to which individual and compounding effects of stigmatizing experiences should be considered in health promotion interventions for older WLWH, as well as insight into a potential protective factor (social support) that could be leveraged to improve health outcomes in this population.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract Liver disease is the 10th leading cause of mortality annually. Although most of these deaths are related to cirrhosis from known causes, an estimated 10-30% of individuals have liver disease of unknown etiology. Whole exome sequencing (WES) can provide an actionable diagnosis in 10-30% of these patients. The Vilarinho Laboratory recently used WES to diagnose a cohort of patients with non-cirrhotic portal hypertension with loss of function mutations in the gene GIMAP5. GIMAP5 is a small GTPase that has previously been implicated in immune cell development but had never been associated with liver disease. Using a mouse model of Gimap5 loss of function, we determined that liver sinusoidal endothelial cells (LSECs) capillarize and lose their organotypic features in GIMAP5-mediated disease. In my preliminary work I performed pseudotime analysis on single cell RNA sequencing data to determine that LSECs dedifferentiate into capillarized endothelial cells. Endothelial capillarization is a pathogenic process that occurs in numerous liver disorders and involves loss of fenestrae and development of a basement membrane. Additionally, in order to better understand the organ wide dysfunction caused by endothelial capillarization, I performed confocal microscopy to evaluate zonation in hepatocytes. This demonstrated severe disruption of normal metabolic zonation of the liver. Collectively, these findings suggest that GIMAP5-mediated liver disease is an LSEC-intrinsic disease process that consequently effects hepatocyte zonation and regulates liver homeostasis. I hypothesize that Gimap5 is critical to maintaining LSEC identity and subsequently hepatocyte zonation and function. My first aim is to determine the role of Gimap5 in LSECs. I have created a novel mouse model, that uses a Cre-Lox system in order to knockout genes within LSECs in an inducible and selective manner. I will use this model to knockout Gimap5 within LSECs and then evaluate endothelial capillarization as well as organ-wide dysfunction. I will use a combination of flow cytometry, confocal and electron microscopy to visualize changes due to this dysfunction. My second aim is to determine the role of endothelial Gimap5 in maintaining hepatocyte zonation and function. It has been well recognized that endothelial derived Wnt signaling is necessary for the proper maintenance of hepatocyte metabolic zonation. Preliminary data shows that these Wnt signals are significantly reduced in Gimap5 loss of function endothelial cells. I will perform single cell RNA sequencing to investigate transcriptional alterations of hepatocytes in Gimap5 loss of function mice. I will also isolate mouse hepatocytes from Gimap5 loss of function mice to evaluate alterations in hepatocyte metabolic activity using functional assays. If successful, this proposal will identify the pathomechanisms behind GIMAP5- mediated liver disease and potentially elucidate a novel mechanism of endothelial capillarization in other more common forms of liver disease.

NSF Awards · FY 2024 · 2024-08

Generative machine learning models are currently revolutionizing the artificial intelligence (AI) community with their significant capabilities in creating innovative images and text. At its heart, generative AI fundamentally addresses a high dimensional density estimation problem. Alternatively, it can be perceived as a transport problem, transforming a simple and known distribution/noise into a complex and unknown distribution. Despite the development of numerous successful algorithms, the literature lacks statistical guarantees to theoretically underpin these algorithms, and concerns about the environmental impact due to extensive computations continue to persist. Among these models, score-based diffusion models are currently replacing the generative adversarial neural nets and at the forefront in terms of popularity and efficacy. However, the score training process can be exceedingly slow and energy intensive. To address this, the investigator will study the more computationally and energetically efficient rectified flow algorithm and its variants which turn the high-dimensional density estimation to an iterative regression problem, and this iterative regression leads to an optimal transport. The research will advance the understanding of the success of models in generative AI. The intrinsic connections to be explored among those models will help convert statistical guarantees from one generative model to another, and lead to novel and improved algorithms, which would eventually advance the state of art of generative AI. The investigator aims to study the statistical and computational assurances of rectified flow and diffusion models and explore two connections among those models: score matching and solving ordinary/stochastic differential equation, with an intriguing linkage to nonparametric empirical Bayes. The following questions will be addressed: 1) can we show that the iterative rectified flow obtains density and transport estimation optimally in just one step of regression? 2) how fast does the iterative regression of rectified flow converge to the optimal transport? 3) can we propose an improved algorithm over the rectified flow for a better statistical and computational guarantee? 4) what are the statistical and computational guarantees of diffusion models? 5) can we improve the denoising diffusion probabilistic models by an iterative algorithm to obtain the optimal transport? In addition, the project will explore applications of generative models and optimal transport to neuroscience and autism spectrum disorder. Research results from this proposal will be disseminated through articles, workshops, and interdisciplinary seminar series. It will integrate research and education by teaching monograph courses and organizing workshops and seminars to enhance the career development of the next generations of statisticians and data scientists, including a particular focus on the underrepresented groups in 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 · 2024-08

Alkalinity is a measure of the buffering capacity of water. Compounds such as bicarbonate and carbonate contribute to alkalinity and prevent waters from becoming more acidic. This study focuses on understanding the processes that produce alkalinity in salt marshes. In addition, the project will quantify how much of the alkalinity produced in marshes is transported to the ocean. It is widely known that alkalinity is produced in salt marshes and wetlands. However, it is not yet known how this alkalinity contributes to regional carbon budgets, ocean acidification, and blue carbon. This study will develop a mathematical model that combines estuarine physics, hydrological drivers such as tidal and groundwater flow, and biogeochemistry. The scientists will use this model to estimate how much alkalinity is delivered to the ocean. As part of the project, the lead investigator will participate in outreach activities that promote awareness and understanding across a range of audiences. These activities include: (a) mentoring graduate and undergraduate students, (b) developing blogs and videos and providing reports to stakeholders, and (c) ensuring transparent and open-source data sharing. This study aims to provide a better understanding of the factors influencing alkalinity fluxes from tidal wetlands and the carbon cycle in the coastal ocean under the effect of climate change. This project will address the following research questions: 1) How do tidal dynamics and saltwater intrusion influence the export of alkalinity during tidal, spring-neap, seasonal, and inter-annual cycles? 2) What are the impacts of sea-level rise (SLR) on the export of alkalinity from tidal marshes and what is the influence of marsh evolution on the carbon cycle in the coastal ocean? To answer these questions, numerical modeling will be primarily utilized, complemented by data analysis and remote sensing techniques. The project will develop and utilize the state-of-the-art SCHISM-ICM-CC-SF-Marsh model along the US East Coast, with Plum Island Estuary serving as a primary study site due to its extensive tidal marsh coverage and rich dataset. Furthermore, future scenarios of SLR and marsh evolution will be simulated to estimate potential changes in alkalinity exports. By integrating interdisciplinary elements, this project will improve understanding of coastal carbon cycling, and advance methodological approaches within this field. Additionally, this project will contribute to the broader exploration of ocean alkalinity enhancement (OAE) as a potential method for carbon dioxide removal (CDR) to mitigate the challenges posed by climate change. 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

Diana Qiu of Yale University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop theoretical and computational tools to better understand how electrons in materials interact with circularly polarized light. Qiu’s research will focus on hybrid materials composed of chiral organic molecules, which exhibit different coupling to left and right-circularly polarized light, integrated in an inorganic crystal framework. This integration will allow for better control and detection of electron angular momentum through molecular chirality and the circular polarization of light, leading to the possibility of designing faster and smaller photonic and optoelectronic technologies, such as light emitting diodes and quantum sensors. Qiu’s research group will develop software codes that can calculate how electrons in materials interact with and absorb circularly polarized light. These codes will take into account complex electron-electron and electron-ion interactions inside the material, allowing for accurate predictions that can be directly compared with experiment. Additionally, Qiu will contribute to the development of a computational materials’ science curriculum at Yale and conduct an outreach program to New Haven high school students. Developing new pathways for the optical control of electron angular momentum and the concomitant electrical control of photon angular momentum is a key step towards realizing smaller, faster, and more efficient spin-optoelectronic and chiral photonic technologies. Hybrid organic inorganic perovskites containing organic chiral ligands are a promising class of materials that can couple structural chirality with electronic spin and the helicity of light. Qiu will develop an ab initio computational framework to understand the interplay of many-body exciton effects, circular dichroism, spin-orbit coupling, and structural distortions of the lattice in the excited state. This will be achieved through development of new software for calculating chiroptical spectra and exciton-phonon interactions and their systematic application to chiral hybrid perovskites. The proposed framework will build on the GW plus Bethe Salpeter equation approach (here, G stands for the single particle Green’s functions and W for the screened Coulomb interaction) and incorporates new tools for understanding electric circular dichroism and exciton-lattice coupling. This endeavor will not only establish a computational framework but also contributes to the broader scientific understanding of layered hybrid perovskites and other hybrid organic-inorganic materials and nanostructures. The research plan will be closely integrated with an education plan focused on the development of a materials science curriculum and outreach to underserved populations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY Fibrosis is a common final outcome of most human chronic inflammatory diseases and has been estimated to contribute to almost half of all deaths in the world1. It can result from autoimmune diseases such as scleroderma [systemic sclerosis (SSc)] and after stem cell transplant in graft-vs-host disease. SSc and the sclerodermoid subtype of graft-vs-host disease (sGvHD) most commonly affect the skin and the degree of skin involvement is associated with higher mortality and internal organ dysfunction2,3, suggesting common underlying mechanisms. T cells are fundamental regulators of fibrosis pathogenesis in both diseases, but the mechanisms by which T cells drive fibrosis remain unclear. Mismatch of minor histocompatibility (mHA) alleles is required for the induction of sGvHD4, but the importance of antigen-independent T cell functions is poorly understood. This proposal aims to understand how antigen non-related CD4+ T cells contribute to the pathogenesis of SSc in response to pro-inflammatory cytokines. In this project, we examine how innate-like functions of CD4+ T lymphocytes regulate skin fibrosis. CD4+ T cells respond to a specific antigen and differentiate into distinct subsets of helper T cells, including Th1, Th2, and Th175. However, differentiated CD4+ T cells can respond to IL-1 family cytokines to generate a diverse cytokine milieu, including IFNγ, IL-13 and IL-17A6. Such bystander activated CD4+ T cells have been shown to drive autoimmunity in diseases such as multiple sclerosis7. SSc and sGvHD skin are characterized by aberrant expression of cytokines spanning type 1, 2 and 17 immune responses8-10. We therefore propose that bystander activation of CD4+ T cells occurs in SSc and sGvHD and to investigate the mechanisms by which it is generated. To accomplish these goals, we will use sclerodermoid GvHD mouse models, which are the primary models of SSc skin fibrosis dependent on adaptive immune cells4,11. In our first aim, we will characterize bystander activation of CD4+ T cells in sGvHD mice by modulating the number of antigen unrelated CD4+ T cells and measuring their accumulation and cytokine expression profile in relation to fibrosis severity. This will allow us to establish to what degree bystander activated CD4+ T cells affects skin fibrosis in vivo. In the second aim, we will examine the cytokine signals that activate CD4+ T cells in this model. We will assess protein levels of inflammatory cytokines including IL-1 family members in the skin and blood and perform in vitro validation for their ability to drive expression of effector cytokines and extracellular matrix genes by fibroblasts. Together, these aims will provide mechanistic insight into how T cells drive the pathogenesis of SSc skin fibrosis, which may identify new targetable signaling pathways for development of treatments that impact patients’ lives.

NIH Research Projects · FY 2026 · 2024-08

Project Summary / Abstract This project will test a new model of speech motor learning, whose central hypothesis is that learning and retention are associated with plasticity not only in motor areas of the brain but in auditory and somatosensory regions as well. The associated experiments are motivated by the results of our preliminary work and each tests the new model in different ways. In Aim 1 we test the causal involvement in the retention of new learning of each of the main players in the model. If plasticity in any of auditory, somatosensory or motor cortex is central to retention, then suppression of its activity following learning using cTBS, should lead to an impairment. Aim 2 examines the temporal order in which neuronal changes are observed during learning. If auditory or somatosensory plasticity play a determining role in speech motor learning, changes in these areas should be detectable early and should also predict subsequent learning. Aim 3 examines circuit-level contributions to speech learning and retention. If plasticity in sensory as well as motor areas underlies learning or retention, it should be possible to identify sensory as well as motor areas whose baseline functional connectivity patterns predict subsequent learning and whose connectivity patterns following learning predict subsequent retention. Our preliminary work on each of the three aims supports the idea that there is plasticity in each of the primary systems which participate in speech motor learning (auditory, somatosensory, motor).

NIH Research Projects · FY 2025 · 2024-08

Heart failure (HF) is a pervasive, high-risk, and expensive condition that affects over 6.2 million Americans, many of whom endure an excessive burden of hospitalization and reduced life expectancy. This condition, although widely prevalent, disproportionately affects Black individuals who experience a 20-fold higher incidence rate and a 3-fold higher mortality rate in comparison to White individuals.The continuing disparity in HF outcomes among Black individuals, despite advances in HF care, represents a significant challenge that needs urgent attention. The primary concern remains the lack of validated methods to explore and address the underlying reasons for these disparities. Addressing the challenges, this grant proposal is dedicated to the development of robust models that enhance the assessment and utilization of care-quality process measures in the treatment of HF. We propose to develop and implement robust deep learning models to enhance the evaluation of care quality in HF management. The main objective is to improve the outcome of patients with cardiovascular disease by using deep learning to optimize care management and to identify and reduce systemic care differences in HF leading to disparate care quality in Black populations. Aim 1: Automate the assessment of HF phenotypes to evaluate the non-prescription of evidence-based therapies. The model will use deep learning-based natural language processing (NLP) methods applied to clinical documentation to determine individual HF subtypes and optimize treatment regimens. Aim 2: Automate the identification of social determinants of health and documentation patterns that vary across patient populations. This aim plans to train a deep learning NLP feature extraction model to identify social challenges and clinical language variations, assessing how these features impact care quality across different patient populations. The outcome of this work will provide an invaluable foundation for advancing data-driven innovations in cardiovascular medicine, promoting data-driven, individualized patient care. This project is anticipated to have a substantial impact on how HF care for across different populations is measured and conceptualized. The goal is to enhance the standardization of care and improvement in health outcomes, thus helping to shape the future of clinical care for one of the most common, high-risk, and high-cost conditions affecting the American population.