University Of California Riverside

NSF Awards · FY 2025 · 2025-07

Many problems in industries such as manufacturing, scheduling, and chip design require solving discrete optimization problems over a finite but large set of feasible solutions. This field of optimization relies on mathematical disciplines such as graph theory, algebra, and topology to design efficient algorithms. However, for many of these optimization problems, finding optimal (best) solutions is widely believed to be computationally infeasible. Approximation algorithms offer a practical approach by providing solutions that are guaranteed to be close to the optimum, measured by a predefined approximation ratio between the cost of the generated solution and the cost of the optimal solution. This project addresses key challenges in designing such algorithms, focusing on a fundamental type of problems in computer science called constraint satisfaction problems. As a part of the education plan, this project emphasizes educational outreach by fostering enthusiasm for mathematics among high school and undergraduate students, highlighting its connection to cutting-edge research and real-world applications. Additionally, by connecting research to education, the project prepares undergraduate and graduate students for careers in computational fields. The proposed research includes three main components: (1) developing a mathematical framework to characterize the approximation thresholds of satisfiable finite-domain Constraint Satisfaction Problems (CSPs), building on the foundational dichotomy theorem for CSPs; (2) advancing the understanding of Ordering Constraint Satisfaction Problems, a variant of CSPs, by leveraging their structural properties to design efficient approximation algorithms; and (3) applying the developed mathematical tools to address important problems in additive combinatorics and complexity theory. These efforts aim to enrich the theoretical understanding of optimization and computation by building on deep mathematical principles and exploring their connections to algorithm design. 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 · 2025-07

The mathematics in this research project centers around questions in geometry and topology, which are broadly concerned with understanding various notions of shape. This project focuses on 2-dimensional spaces called surfaces, which are fundamental in many areas of mathematics. Surfaces can be flat, like a piece of paper, or curved, like the outside of a ball, a donut, or a saddle, and the various shapes they take often strongly constrain the shapes of the higher dimensional spaces in which they live. The educational portion of this project involves a variety of activities aimed at recruiting and supporting students into mathematics. The first part continues a series of workshops featuring mini courses by early career speakers on their cutting-edge research aimed at graduate students. The second part establishes a series of undergraduate research and recruitment events connecting undergraduate mathematics researchers with graduate recruiters from programs in the region. The third part is a Topical Pedagogy Seminar which will provide graduate students and postdocs training in incorporating topical material into foundational mathematics courses. A remarkable and ubiquitous example of this mathematical phenomenon is a surface bundle, which just like a donut, can be sliced so that the cross-sections are surfaces. Unlike a donut, however, as one moves through most surface bundles, the surface cross-sections can twist and deform in complicated ways. This twisting–which essentially determines the bundle–is encoded in the mapping class group, which, among other things, is the collection of all symmetries of the space of shapes that a surface can take, also known as Teichmüller space. The first part of the research program aims to develop a powerful fleet of combinatorial techniques for studying the geometry of mapping class groups and Teichmüller spaces, as well as their structure at infinity. The second part focuses on the geometry of surface bundles arising naturally from dynamics. In more detail, this project will investigate the coarse geometry of the mapping class group, Teichmüller space, and surface bundles using the tools of geometric group theory. The first part aims to address a family of results showing that mapping class groups can be coherently locally modeled by CAT(0) cube complexes, allowing for the construction of new metrics with a variety of applications, including about their geometry and topology at infinity. The second part studies the geometry of surface bundles of Veech surfaces and their combinations, as well as developing a Sullivan-like notion of structural stability for the subgroups associated to a variety of surface bundles with nice curvature properties. 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 · 2025-07

This award will support a conference on “A Celebration of the Science Legacy of Joanne Chory,” to be held July 25-26, 2025 at the Baird Convention Center, Milwaukee, WI. This meeting will be attended by graduate students, postdocs, and junior and senior faculty researchers from all areas of plant biology to discuss and advance our understanding of the physiology, structure, and interactions of plant cells, especially the responses to light, responses to hormones, and formation growth, and function of chloroplasts. The conference will also discuss advances in plant cell biotechnology applications. The interactions of plant physiologists, geneticists, and cell biologists will increase our understanding of plants. The speakers presenting the talks are all former students or collaborators with Dr. Chory, and will engage with junior scientists to help them develop successful scientific research careers. This meeting will be attended by graduate students, postdoctoral fellows, and junior and senior researchers to discuss our understanding of all aspects of plant cell biology and response. Sessions will focus on recent advances in such topics as “Light and Temperature Signaling”, “Organelle Signaling and Dynamics”, “Auxin and Shade Avoidance” and “Brassinosteroids” In addition to formal talks, engagement will be facilitated by discussions stimulate interactions between researchers. NSF support will be used solely to defray registration costs to allow attendance of participants from early stages of their careers. This award is funded by the Cellular Dynamics and Function Program of the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Consistent high yields of commodity crops in the U.S. fosters economic prosperity within the agriculture sector that benefits consumers. A significant challenge in crop production is maintenance of the narrow range of soil moisture and oxygen that maximize the capture of water and nutrients necessary for plant growth and fitness. The oversaturation of soils is caused by varied factors including uneven field contours, poor drainage, and periods of intense rain. In animals and plants, oxygen is essential for production of cellular energy. Soil saturation or compaction limits the level of oxygen available to root cells for their normal function. Previous studies have shown how plants cells respond to sudden or prolonged periods of insufficient oxygen (hypoxia). These responses cause changes in cell functions and plant growth that can lead to better oxygen retention or protect tissues until oxygen levels are restored. This project leverages the current understanding of hypoxia sensing, response mechanisms, naturally occurring variation and gene engineering of an oxygen sensor protein to alleviate negative impacts of hypoxia on nutrient uptake and growth. Experimentation will design and test slight changes in the key oxygen-sensing protein of critical commodity crops, which will be tested for their effect on hypoxia. This strategy is a promising path towards establishing more consistent crop yields. This project will enhance the scientific workforce by training a postdoc and undergraduate students in cutting-edge techniques. Plants sense and respond to low cellular oxygen (hypoxia), which ensue when soils are waterlogged, tissues undergo partial to submergence, and in some organs during development. Hypoxia responsive genes are controlled by Group VII ethylene response transcription factors (ERF-VIIs) that are constitutively synthesized but only stabilize when endogenous oxygen levels drop below a threshold. Plant Cysteine Oxidases (PCOs) are conserved cellular oxygen sensors that catalyze the conversion of ERF-VIIs to an N-degron destined for proteasome mediated degradation. Collaborators in the U.K. have identified a “tunnel” within Arabidopsis PCOs that controls the rate at which oxygen is delivered to the enzyme’s active site. Engineering this “tunnel” alters the oxygen binding threshold of PCOs, providing a route to modulate ERF-VII stabilization. Fine-tuning of ERF-VII levels influences the hypoxia response and flooding resilience. This project will leverage this knowledge and the conserved structure of PCOs to test the outcomes of PCO modification on ERF-VII stabilization in the cereals rice and barley. Specifically, this project will exploit pangenome variation and structure-function modeling to predict PCO modifications that tune oxygen binding and enzyme kinetics. Structure-guided modification of the PCO tunnel will then be tested in vitro, protoplasts, and in transgenic or edited plants. Outcomes include (i) genotypes of rice and barley with altered low oxygen sensitivity ready for field testing; (ii) dissemination of data and technology; and (iii) team-based authentic research experience module in engineering for crop improvement, valuable in industry and education. 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 · 2025-07

The interconnected, interdependent nature of wide-area modern power systems necessitates effective integration of increasingly larger-scale, heterogeneous, and spatially distributed physical assets with a multitude of ubiquitous cyber devices. Conventional admittance-matrix-based power grid topological models have been shown to lack analytical capabilities to effectively model ubiquitous flexibility, capacity, and prosumer profiles and enable granular and accurate controls of the ultra-high-dimensional distributed energy resources. This project aims to transcend the state-of-the-art, and will develop a data adaptive graph generation module, topological data analytical techniques with multiple filtrations, higher-order network models, and input layers for deep neural networks that take topological signatures and higher-order interactions for (dynamic) networks. The project will integrate ubiquitous, high-dimensional information structures on transmission nodes by taking properties of power systems as special directions and designing new perspectives at the intersection of algebraic topology, commutative algebra, and deep learning. Fundamentally, this project will directly benefit local and national interests as well as guide the modeling, operation, and control of wide-area power transmission networks with ultra-high-dimensional penetration of distributed energy resources and energy storage systems. The project framework will serve as a tool to enhance the reliability and resiliency of power grids, improve power systems quality. Developments from this project have the potential to significantly lower energy costs, directly benefit national economic growth, and substantially reduce the frequency and severity of power outages caused by extreme weather events. In addition, students working on this project research will acquire an excellent orientation of cross-disciplinary work and experience at the interface of computer science, mathematics, data science, and electrical engineering. This project is a first attempt to bring the emerging machinery of adaptive graph structure learning, topological data analysis, simplicial complex-level representation learning, and (geometric) deep learning to power grids. Thus, this project will fundamentally redesign existing power grid topology and uncover the fundamental mechanisms of embedding ubiquitous, local topological information, and high-dimensional structures on transmission nodes via higher-order topological graph models and learning methods. Furthermore, the project techniques will include generalizable capability and so can be applied to many different settings involving dynamic networks. These approaches and newly developed topological and higher-order interactions representations learning modules will pave the way for new research directions in computational topology, machine learning and data science. Moreover, the project will contribute an open-source software library to obtain topological summaries learned from the multifiltration of power system data based on techniques developed within the project. These tools in return will enhance new applications of machine learning and deep learning tools for large-scale power system analysis that are deemed infeasible today due to high computational costs. 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 · 2025-07

The activity and interconnectivity of neurons, a key type of brain cell, are crucial to the brain's ability to compute and process information. However, recent studies suggest that astrocytes, a different type of brain cell, may also play an important role. This project combines experiments and computational modeling to study how astrocytes contribute to brain function. Astrocytes affect many aspects of neuronal activity and communication, providing a potential mechanism by which they can alter signaling in the brain. The computational modeling and mathematical analysis within the project will enable a deeper biological understanding of these astrocyte-neuron interactions, generate new ideas for why they may be important for information processing in the brain, and suggest ways to integrate these principles into artificial intelligence systems. In conjunction with the modeling will be experiments to observe and manipulate astrocytes in living brains. In so doing, the project will validate new ideas about astrocytes' roles in the brain, providing an enhanced understanding of neural circuits and brain function. The scientific premise of this project is the "contextual guidance" hypothesis, which postulates that astrocytes act as switchboards that transmit information about the environment and the physiological state of the organism to neurons and networks thereof. As such, astrocytes may act as a force multiplier that can expand the repertoire of dynamics that neurons can realize, thus enabling computation. The project will explore two ideas in this regard: (i) that astrocytes actively modulate neuronal dynamics in response to signals sensed from the environment, and (ii) this modulation enables neuronal networks to tailor their dynamics in response to context-specific circumstances. To substantiate these ideas, the project will investigate the role of astrocytes in neuromodulatory systems and subsequent effects on neuronal activity and synaptic plasticity. Furthermore, the project will examine network-level interaction between neurons and astrocytes, exploring features like "tiling," where astrocytes overlay neuron clusters to influence signal routing. In addition to scientific insights, the research will examine how brain-inspired computing may be enhanced by new artificial neural network designs that incorporate astrocytes, with a focus on context-dependent computational paradigms. Additionally, the project includes initiatives to engage trainees in interdisciplinary neuroscience research and exchange, including new mini-courses that bridge neuroscience, engineering and artificial intelligence. A companion project is being funded by the French National Research Agency (ANR). 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 · 2025-06

Machine learning (ML) models are driving an AI revolution that is transforming all areas of human life, with applications including healthcare, self-driving cars, and robotics. Anticipating the security vulnerabilities of ML is essential to improve the safety and trustworthiness of systems that depend on them. The project studies a new threat to ML models that exploits how these models are built and trained. Specifically, when ML models are trained, they configure a number of parameters as they learn patterns in data sets; large models can contain billions of parameters that help them as they learn complex tasks efficiently. However, once the model is trained, it is well known that only a subset of these parameters contribute to the model’s functionality. The remaining, unused, parameters have little effect on the performance of the model, and there are reasons to believe that malicious actors might be able to exploit unused parameters to harm the security and privacy of models. The goal of this research is to understand the security implications of the unused parameters of machine learning models. Since unused parameters do not affect the baseline model, their state can be manipulated by the attacker during training to install potentially malicious additional functionality, without being detected when the model is tested. The project characterizes this threat both experimentally and theoretically for different model types, and develops mitigation approaches against it, helping to secure ML models. The project will explore a number of coordinated research directions around exploiting unused parameters. First, the research empirically studies the capacity of ML models to store data covertly within the unused parameters without affecting the baseline model accuracy. Viewing the problem from an information theoretic perspective allows the project team to use tools from communication to reason about the capacity of the unused parameters to hold unintended functionality (in this case, covertly stored data). This in turn allows the use of ideas from game theory to analyze tradeoffs between optimizing malicious goals and preserving baseline functionality. The project also studies generalizations to emerging ML models including Large Language Models, and to additional attacks including a novel model hijacking attack. Having established the threats and characterized their properties, the project team will explore defenses and mitigations to improve the robustness of machine learning models against this new attack vector. These new attacks, optimizations, theoretical models, and mitigations represent the intellectual merits of the project. The broader impacts include improving the safety and trustworthiness of ML models, training graduate and undergraduate students, and developing new pedagogical material on ML safety. 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 · 2025-06

Scientists and engineers build quantitative models of our natural and constructed world. Using the process of statistical inference, the models are tested against experimental data in order to constrain their underlying parameters, which leads to an improved understanding of the models and more accurate predictions for future data. Markov Chain Monte Carlo (MCMC) is the most popular algorithm for statistical inference because of its power and simplicity. The goal of this project is to develop a new MCMC inference method, Shrek, which exploits the fact that many models in science and engineering can be calculated using different time-accuracy trade-offs. By performing inference with a combination of low accuracy but computationally cheap models, with high accuracy but computationally expensive models, Shrek can achieve better performance than traditional MCMC algorithms. The project team aims to apply Shrek to the field of cosmology to answer questions about dark matter, galaxy formation, and the expansion rate of the Universe. Furthermore, the team is developing an open-source software package so that scientists across many fields may use the Shrek algorithm. This project exploits the multi-fidelity nature of many scientific and engineering models to help guide MCMC. Many simulations, including the Boltzmann codes ubiquitously used to model the cosmic microwave background, have tunable fidelities. This can be, for instance, a spatial or temporal resolution of the model, or an accuracy parameter for a differential equation solver. Using lower-fidelity models to guide the MCMC algorithm at the desired highest-fidelity model results in an MCMC method that converges to the true highest-fidelity posterior in fewer samples and less total computation time. The project team is developing a new recursive MCMC sampler (Shrek MCMC) that can exploit multiple fidelities for computational acceleration. Specifically, the plan is to (1) couple Shrek MCMC with neural network emulators, (2) prove convergence bounds and use these to guide automatic tuning of Shrek MCMC, and (3) develop a layered Multi-fidelity Hamiltonian Monte Carlo (Shrek HMC) sampler. The resulting algorithms will be made publicly available through open-source licenses and will be incorporated into popular existing sampling packages. In cosmology, this research allows for larger, more flexible, inference pipelines. The team will use the new Shrek algorithms to test proposed solutions to the cosmological H0 and S8 tensions simultaneously and to model the local dark matter density in order to answer questions about the formation and content of the Local Group of galaxies. 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 · 2025-06

The recent wildfires in Los Angeles County in January 2025, which affected thousands of lives and caused substantial property damage, have underlined the urgent need for new, advanced, data-informed strategies for efficient and proactive management of such devastating events. However, the development of such data-informed solutions is still largely hampered by limited access to multisource, multi-resolution remote sensing imagery, leaving many in the machine learning and computational sciences communities unable to contribute robustly. This project uses large language models (LLMs) to extract properties and relationships from relevant LA fire data sources, storing them in a comprehensive knowledge database. By integrating complementary wildfire-related information, the framework facilitates monitoring of key physical parameters, such as real-time evacuation orders, meteorological variables, and air quality indicators. The project aims to develop an LA Fire Knowledge Graph-Agent (LAFireKG-Agent) platform--an autonomous and end-to-end LLM-based framework designed to meet the diverse data needs of end users, and enhance situational awareness for both safety and timeliness in wildfire risk management. The LAFireKG-Agent framework focuses on three key objectives: rapid decision-making, predictive modeling, and complex reasoning. Beyond these core capabilities, end-users, including computational scientists, environmental scientists, and risk managers, will be able to explore wildfire-specific questions, generate tailored insights, and receive data-driven recommendations. By integrating advanced machine learning and knowledge graph methodologies, this project will not only lead to more effective disaster preparedness and response strategies, but also promote open science and reproducible research in AI-driven environmental studies. The resulting tools and best practices will be shared through publicly accessible platforms, expanding research synergy among scientists, practitioners, and community organizations. 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 · 2025-06

Use of psychostimulant drugs, such as methamphetamine (METH), is a frequent comorbidity in people living with human immunodeficiency virus (HIV)-1 (PWH). The interaction of virus and addictive substance, in particular with regard to viral persistence, is poorly understood and will be studied here using in vitro and in vivo approaches in combination with single cell (sc) and single nuclei (sn) RNA-sequencing. We observed that METH in combination with a single-stranded (ss) RNA mimic of the HIV long terminal repeat (LTR) that acts as TLR7/8 ligand upregulates components of the arachidonic acid (AA) pathway, including cysteinyl leukotriene synthase (LTC4S), which produces cysteinyl leukotrienes (CysLTs). We also found that i) CysLT receptor 1 (CYSLTR1) expression in HIV+ brain correlates with HIV DNA and RNA, ii) CYSLTR1 is upregulated in cerebral cortex of cART-receiving PWH with brain pathology, iii) CysLTs released by HIV-1 infected macrophages (MΦ) and CYSLTR1 play a critical role in neurotoxicity, and iv) Pharmacological blockade of CYSLTR1 abrogates neurotoxicity of HIV- infected MΦ. Therefore, we propose here to investigate, if the CysLT-CYSLTR1 axis plays a causal role in the promotion of HIV-1 persistence and brain injury by METH in the presence of cART. Three Specific Aims are proposed: 1) To determine how methamphetamine engages the CysLT-CYSLTR1 axis and increases HIV-1 infection of peripheral blood CD4+T-cells and macrophages in the presence of cART. This aim will test the hypothesis that METH engages the CysLT-CYSLTR1 axis to escalate HIV-induced inflammation, viral production and reservoirs despite cART through upregulation of proviral host factors, incl. lncRNAs. 2) To investigate whether inhibition of CYSLTR1 combined with cART ameliorates brain damage by METH and HIV in humanized CD34+ HSC-engrafted NSG-SGM3 mice. This aim will test the premise that METH upregulates the CysLT- CYSLTR1 axis to drive HIV-induced inflammation, infiltration of infected MΦ and CD4+T-cells into the brain and neuronal damage despite cART. This aim will also assess if the clinically approved, CNS-penetrating CYSLTR1 inhibitor Montelukast (MTLK) abrogates those pathological processes. 3) To assess in vitro how the blockade of CYSLTR1 enables neuronal survival in the presence of METH, cART and HIV-induced macrophage toxins. This aim will investigate the hypothesis that blockade of CYSLTR1 enables neurons to suppress cellular signaling pathways promoting inflammation, cellular stress, injury and apoptosis, such as those mediated by p38 MAPK and JNK, in favor of pro-survival and cytoprotective pathways, such as activation of PKB/Akt and ERK1/2. The experiments will be performed in vitro with isolated human peripheral blood cells and iPSC-derived neurons and in vivo in humanized mouse model that provides HIV permissive human peripheral CD4+T-cells and MΦ. All three Specific Aims will define RNA signatures of HIV-1 infected CD4+ T-cells and MΦ in the presence and absence of METH, cART, and MTLK using sc and sn RNA-sequencing, and will test the proposed mechanisms, including the interference of METH with TLR7/8 function, leading to increased inflammatory CysLT production.

NIH Research Projects · FY 2026 · 2025-06

Project Summary To better adapt to their environment and align key metabolic and physiological activities, the circadian clock helps organisms partition specific responses to the most optimal times of the day. In humans, many diseases are characterized by dampened circadian rhythms and symptoms that cluster at specific times of day. In plants, gene expression dynamics in response to environmental stimuli is highly dependent on the time of day, a process referred to as circadian gating. Plants have been an ideal model for investigating circadian gating of stress responses. However, the key regulators involved, the regulatory mechanisms and the functional relevance of circadian gating remain poorly understood. Insights from our research in the model Arabidopsis suggest that the current knowledge of the existing clock proteins is not sufficient to explain the functional mechanisms of gating. Our central hypothesis is that the clock facilitates dynamic protein/DNA interactions, mRNA fate (storage and/or degradation), and dynamic protein complex composition depending on the time of the day to regulate stress tolerance mechanisms. In this proposal, we will continue to leverage the plant model Arabidopsis and a combination of genetics, genomics, biochemical, and molecular biology approaches to identify key regulators involved in gating and investigate how the clock manages stress signals to elicit a response that is then communicated to allow for optimal physiological responses in a changing environment. Plants are an ideal system to engineer or reprogram temporal control of gene expression through manipulating clock-controlled genes. Our long-term goal is to leverage innovative approaches to precisely enhance or engineer time of day specific gene expression responses for positive physiological outcomes. Our research discoveries will bridge the existing knowledge gaps on how signals are partitioned and modulated by eukaryotic clocks and provide new insights that can aid in optimizing protocols for stress resilience, disease treatments, and chronotherapy.

NIH Research Projects · FY 2026 · 2025-06

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. SUMMARY Significance, timeliness and rationale. NIBIB’s mission is to transform through engineering the understanding of disease and its prevention, detection, diagnosis, and treatment. Life would not be possible without liquid crystalline (LC) order. The most celebrated example is the unique combination of flexibility and confining ability of cell membranes which is possible because lipids cooperatively aggregate into a lamellar LC phase in water. Many viruses, e.g., SARS-CoV-2, are enveloped in a lipid membrane and they may employ LC order for packing their nucleic acid cargo. Non-biological LCs have turned out to be sensitive in detecting and reporting on the presence of pathogens and toxins, making them highly interesting for autonomous bio-/chemosensors. LC formation is thus an integral component of what keeps us healthy, and they are emerging as a powerful tool for detecting disease agents. Although this connection is long known, it has until now been researched only marginally, constituting an under-explored opportunity in addressing societal needs. Today, the international LC research community is undergoing a rebirth, fully embracing these aspects. Interdisciplinary crossovers where LC science meets biological, life and pharmaceutical sciences, and engineering and materials science, play a groundbreaking role in leveraging the opportunities. Additional impact on the core fields of the NIH-NIBIB mission comes from, e.g., the exploration of LC elastomers in soft actuators or implants in the human body. Objective. Gordon Research Conference (GRC) and Gordon Research Seminar (GRS) in Liquid Crystals, 2025: “Uniting disciplines for global challenges: Liquid crystals as active and learning materials” to take place July 6-11th 2025 (GRS July 5-6, 2025) at the Southern New Hampshire University, Manchester, NH, US. The joint meetings will bring together US and international researchers at the forefront of today’s vibrant LC science, spanning its full breadth and connecting academia, industry and military labs, early career and established researchers, and harnessing every opportunity to promote scientific discussions and encourage collaboration between speakers, discussing leaders and participants. Approach. 23 prominent speakers have confirmed our invitations to present unpublished cutting-edge research. Our goal is to promote the participation of students, postdocs, and scholars at an early stage of their careers. We seek to promote scientific discussion among all participants who will fuel the future of Liquid Crystal Science in biomedical and biotechnology applications. Academic speakers are complemented by 2 industry and 2 military research lab speakers. Additionally, 5 speaker slots will be given to outstanding scientists selected from submitted poster abstracts. To further strengthen the voice of young researchers, the GRC is preceded by the GRS, catering only to students and post-docs and providing mentoring and career advice. During the GRC, young scientists will be given priority in all scientific discussions.

NSF Awards · FY 2025 · 2025-05

Rivers occasionally experience a process called avulsion when they jump out of their banks and carve a new path across the landscape. The resulting floods are more extreme than typical floods caused by rainfall; avulsions can devastate entire communities. River avulsions occur infrequently, so we have very few observations of them. As a result, the scientific understanding of this important natural hazard lags behind other comparable hazards like earthquakes. For example, the number of people in the United States living in the potential path of river avulsions is not known because we do not even know for sure what conditions prime rivers to avulse. In this project, the team will develop a new unified theory for the processes that prime river avulsion, and test the theory using experiments and observations from satellite images. Two main conditions are thought to destabilize rivers and set them up to avulse. The first is called superelevation (β), where sediment accumulates on the levees and the riverbed, lifting the river above the surrounding floodplain. The second is called gradient advantage (γ), where a steeper alternative path is available to the river. These two conditions have long been considered either mutually exclusive or unrelated. Recent observations have revealed that avulsions occur when the combined values of superelevation and gradient advantage reach a joint threshold; specifically, where β • γ ≈ 2. Based on this, the team will test three hypotheses: 1) the joint threshold for river avulsion arises because alluvial ridge superelevation grows faster on fans and gradient advantage grows faster on deltas; 2) avulsions occur in isolated river reaches where β • γ is locally elevated, and do not occur elsewhere; and 3) the threshold of β • γ for avulsion will decrease with increasing trigger size. To build on this discovery, the project team will develop a new theory for river avulsion setup into a physics-based modeling framework that seeks to model how β and γ evolve over time on a given river. That model will be tested against new remote-sensing observations of how β and γ vary along river reaches, and new lab experiments aimed at testing the controls on the threshold value of β • γ. 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 · 2025-05

Nontechnical description This project aims to revolutionize computer memory by developing new materials and devices for high-density magnetic random-access memory. The project focuses on enabling these devices using thin layers of special materials, called van der Waals heterostructures, on a wafer scale. This wafer-scale approach is crucial for practical applications in the semiconductor industry, unlike current lab methods that rely on delicate, small flakes of two-dimensional materials prepared by exfoliation. Achieving highly efficient switching in these magnetic random-access memory devices is essential for future “memory-in-computing” technologies, which promise faster and more energy-efficient computing. This research will address critical challenges in material development and device fabrication, ultimately paving the way for transformative advancements in computing. Additionally, the project will provide invaluable training for students, equipping them with essential skills for the semiconductor industry and contributing to the development of a highly skilled workforce. Technical description This research will address the limitations of conventional spin-orbit torque switching in magnetic random access memory devices by developing wafer-scale epitaxial van der Waals spin heterostructures. Current spin-orbit torque efficiency in three-dimensional materials is restricted by intrinsic and extrinsic factors, such as low spin Hall angles and imperfect interface spin transparency. Recent studies on exfoliated two-dimensional materials suggest the potential to surpass these three-dimensional limitations. This project seeks to achieve material breakthroughs by growing epitaxial van der Waals heterostructures with exceptional structural, electronic, and magnetic properties, including atomically sharp interfaces, on a wafer scale. These heterostructures will consist of a two-dimensional ferromagnetic material with room-temperature Curie temperature and tunable perpendicular magnetic anisotropy, epitaxially grown via molecular beam epitaxy onto a topological quantum material with strong spin-orbit coupling. Topological insulators (e.g., Bi2Te3) and semimetals (e.g., WTe2) will provide the spin-momentum locking necessary for high intrinsic spin-orbit torque efficiency. The ferromagnetic component, Fe3GaTe2, a recently discovered two-dimensional ferromagnet, exhibits a Curie temperature of approximately 380 K, among the highest reported for two-dimensional magnets. Furthermore, like Fe3GeTe2, Fe3GaTe2 possesses strong perpendicular magnetic anisotropy, a crucial property for high-density memory devices. Successfully integrating these components into a wafer-scale heterostructure via molecular beam epitaxy represents a significant scientific advancement compared to devices made with exfoliated flakes. The combined expertise of the PI and Co-PI in molecular beam epitaxy, nanofabrication, and spintronics will ensure the project’s success. The resulting epitaxial heterostructures will have a profound impact on nanoscale magnetism, spintronics, and related fields. First-principles calculations will be employed to provide theoretical insights into the fundamental properties and behavior of these novel materials, guiding the optimization of material growth and device design. 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 · 2025-05

This NSF-funded Research Experience for Undergraduates (REU) Site to the University of California, Riverside, located in Riverside, CA, will support the training of 10 students for 10 weeks during the summers of 2025-2027. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in the program and contribute to development of the US STEM workforce. The program is designed to provide opportunities to students interested in the cellular and molecular biology of plants through next generation technologies. The scientific focus addresses an urgent national need for training in both translational and foundational plant biology and associated environmental influences, including pathogens and other microbes. Students will learn how research is conducted, and many will present the results of their work at scientific conferences. The effectiveness of the REU site will be assessed using student feedback and tracking the career path and publication record of program participants. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). This ten-week summer research program is focused on understanding plants and plant pathogens. Students from all colleges are welcome to apply. These will include students from two- and four-year colleges with limited research infrastructure and no prior research experience. A goal of the program is to engage all participants in exciting research and inform them about career options within plant biology and related fields. The program is hosted by the UC Riverside Center for Plant Cell Biology which, in association with the Institute for Integrative Genome Biology and other college departments, includes many faculty who study plants, plant pathogens (fungi, bacteria, viruses, nematodes), other microbes, and allied fields. Applicants are selected by a holistic review conducted by a faculty-led committee. The program begins with a one-week series of workshops, in which students are introduced to bioethics, experimental design, techniques and approaches used to study plants and plant pathogens, including genetics, molecular biology, genomic and bioinformatic analyses, and confocal microscopy. In the first week, faculty mentors present their research so that students can select a host lab based on the scientific focus. During the remaining nine weeks, students conduct laboratory research and participate in workshops to enhance learning skills and professional development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

The widespread use of fluorochemicals in modern society has led to worldwide accumulation of per- and polyfluoroalkyl substances (PFAS) in the environment. Wastewater treatments that use membrane technology to separate pollutants generate concentrated waste streams that are rich in PFAS compounds and salts. This project will use short-wavelength UV light-driven water photolysis to destroy PFAS in the waste stream. Short-wavelength UV light is among the most efficient water ionization photon sources, because it uses water molecules as a catalyst. It can be readily generated and is safe to control and operate. This project will investigate pathways to produce an extremely reactive system to destroy PFAS at reduced energy cost. This GOALI project is a partnership between the University of California - Riverside and the Orange County Water District. The results will have the potential to advance the science and technology in water treatment for PFAS removal. This project will investigate the short-wavelength ultraviolet light-driven water photolysis (SHARP) process to destroy per- and polyfluoroalkyl substances (PFAS) in the concentrated brine reject from membrane treatment of wastewater. Short-wavelength UV light takes advantage of water molecules as the catalyst. Elevated salinity in the brine can further catalyze the photolytic process and PFAS destruction. The research will test the hypothesis that short-wavelength UV water photolysis in the brine can produce an extremely high yield of hydrated electrons, and the brine solution chemical conditions (e.g., pH, chloride, sulfate and bromide) and light sources (wavelength and intensity) can be optimized to maximize PFAS degradation and minimize the energy footprint of UV-based treatment. The project will be carried out through a partnership between University of California, Riverside and the Orange County Water District (OCWD) that operates the world’s largest advanced water treatment system for potable reuse. The outcome of this project has the potential to help the water industry with smart infrastructure for sustainable PFAS water treatment technologies. By joint research and knowledge sharing, this GOALI project will: (1) strengthen academia-industry linkage with an interdisciplinary team of faculty, students and industrial engineers; (2) enhance US technological competitiveness in the water sector by accelerating new knowledge transfer between university and industry; and (3) contribute to STEM education and the STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Matthew P. Conley of the University of California, Riverside is studying new catalysts for challenging chemical bond cleavage and formation reactions. Catalysts are important in a variety of reactions that activate inert bonds. The catalysts studied will be metal oxide materials containing anions, with a focus on zirconium oxide containing sulfate and pyrosulfate anions. This catalyst is used industrially in hydrocarbon refining and has a rich and complex history. Most literature assigns the unique reactivity of this material to strong acid sites present from sulfate anions, while other reports suggest that pyrosulfate anions are the reactive centers that promote catalysis. This work shows that the pyrosulfate anion is responsible for activation of inert carbon-hydrogen and carbon-carbon bonds in paraffins. This property is extended to activation of the inert bonds in polymer waste, a stream containing vast amounts of polyethylene and polypropylene that are difficult to recycle. The products of these reactions are processible oils that can be further functionalized using know catalytic technologies. Through these, and related studies, the chemistry of oxides containing pyrosulfate anions will help define this interesting class of catalytic materials. As part of this effort. Professor Matthew P. Conley also hosts high school students from a nearby high school to engage them in an academic research environment and to inspire them to pursue studies in STEM fields. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Matthew P. Conley of the University of California, Riverside is studying the surface chemistry of sulfated zirconium oxide (SZO) materials. When considering the chemistry of SZO towards alkanes, the dominant view in the available literature is that Brønsted sites mediate all reactivity. This proposal provides a new mechanistic model to describe how alkanes interact with SZO; pyrosulfates behave as adsorbed SO3, a very strong Lewis acid capable of activating C–H bonds. The insights from this mechanistic proposal, supported by preliminary data, resulted in the formulation of a simple anion doped oxide as a catalyst for chain cleavage in polypropylene waste. Thus, a simple oxide can facilitate a reaction that is usually performed by a highly reactive organometallic or supported metal nanoparticle catalysts. The results from this mechanistic model also extends to other H-atom transfer reactions that, to the best of our knowledge, are unprecedented in the chemistry of SZO. Professor Matthew P. Conley’s group will also host students from a nearby high school to involve them in this project to help retain them in STEM fields. 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 · 2025-04

Retirement from the workforce is an important life transition that impacts the well-being of older adults. For many people, retirement eliminates work-related learning opportunities, such as learning to use new software. At the same time, retirement may also provide the time to pursue other learning opportunities. We do not know how changes in learning opportunities related to retirement affect cognitive aging and brain aging. This project addresses these questions using behavioral and neuroimaging methods. The project includes research training for graduate and undergraduate students and dissemination activities aimed at improving public understanding of science related to learning, cognition, and aging. This project first examines how new learning activities as well as activity challenge level and variety relate to the ability to learn new information using behavioral tasks in a sample of older adults who are several years pre- or post-retirement. The project then uses neuroimaging methods to investigate whether the amount of new learning opportunities, activity challenge, and activity variety predict brain structure and brain volume in older adults. The findings of this project help test the “adult cognitive plasticity theory,” which suggests that changes in demands induce cognitive and brain changes. This project advances understanding of how active engagement in cognitively stimulating activities after retirement can help maintain cognitive and brain functions. 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 · 2025-03

This project explores an important question in evolutionary biology: How do new species form? It looks at whether the location of genes in a genome makes them more likely to help keep species separate. This is important because biologists can better predict how biodiversity is generated and preserved if the same parts of the genome are used in different speciation events. At the same time, this project combines research, education, and outreach to support biology education in the U.S., build student confidence in science, and contribute to the development of a skilled STEM workforce. It will train graduate and undergraduate students in research and mentorship, and the entire research team will visit middle school classrooms to improve public understanding of science and the natural world. Finally, this project will find useful traits in wild sunflowers, which could help make sunflower farming more sustainable and improve global food security. By pursuing these objectives, this project will also create a unique opportunity to integrate multiple levels of mentees into the research process. To achieve this, the project will include the development of a hands-on, inquiry-based module for first-year undergraduate students to explore assortative mating in sunflowers through genotyping offspring that will be incorporated into an existing course-based research experience (CURE). In addition, the research team will initiate a multifaceted operation that links existing research, professional development, and outreach experiences for small cohorts of undergraduate students. Briefly, this initiative will include recruiting students from the CURE class, transitioning them into a summer research program, pairing the students with graduate student mentees to continue their research, including the student-mentee pairs middle school outreach, and finally facilitating professional development opportunities. This project explores the interaction of parallel evolution, structural variants (SVs), and the evolution of reproductive barriers using dune sunflowers as a model system to examine speciation. Previous work has shown that SVs facilitate parallel adaptation to sand dunes in sunflowers and that dune adaptation results in reproductive barriers, suggesting that SVs facilitate parallel speciation. To test this hypothesis, three primary research questions will be addressed: (1) How and why does assortative mating evolve between sunflower ecotypes? (2) Does parallel evolution lead to parallel speciation? (3) How broadly can SVs facilitate parallel evolution? These questions will be answered by phenotyping 200 individuals from 26 sunflower populations and species that span a range of environments from the dune core, to the dune edge, to non-dune populations. It will connect genotype to phenotype to fitness using GWAS, and measure components of reproductive isolation using crosses from populations with and among the same and different habitat types. 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 · 2025-03

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Ji and Fang of Oregon State University in collaboration with Professor Greaney of the University of California, Riverside will explore the roles of local solvation structures in expanding the stability of water-based electrolytes for energy storage technologies. While previous efforts to improve the water stability against electrolysis have focused on changes to the O–H bond strength, the team will pursue a new approach by considering the impact of changing the hydroxide and proton solvation energies. The team will use a combination of femtosecond stimulated Raman spectroscopy (FSRS) and ab initio molecular dynamics (AIMD) simulations to study electrolytes of varying salt concentrations, cations and anions, and pH values to support their hypothesis that the electrochemical window of aqueous solutions can be expanded by discouraging their solvation of hydroxides and protons, the byproducts of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively. Their studies will elucidate key factors in chemical environment that dictate HER and OER onset potentials in aqueous electrolytes. Besides three graduate students who will directly perform the project tasks, the three PI’s labs will engage in outreach activities via online videos and in-person chemistry summer camps. This collaborative research project by Ji and Fang Labs at Oregon State University and Greaney Lab at UC Riverside will systematically investigate how solvation environments in aqueous electrolytes affect water’s HER and OER as well as the electrolytes’ resulting electrochemical stability window (ESW). The team will reveal how the local chemical environment with various cations and anions at low temperatures affects the electrolytic properties of aqueous solutions, and identify a richer set of Raman spectrum descriptors and predictors for chemical environments in concentrated aqueous electrolytes via PIs’ complementary expertise in electrochemistry, FSRS, and atomistic modeling. The direct observation of water’s H–O–H bending mode and use of a photoacid will provide deep insights into water’s local environment, bridging kinetics on ultrafast timescales to thermodynamics at equilibrium. The broader impacts of this work involve the development of a powerful experimental and theoretical platform to rationally design aqueous electrolytes with a significantly expanded or suppressed ESW for safe grid-scale storage batteries and green hydrogen production. The project, with cross-disciplinary knowledge and in the context of battery technology, will effectively engage STEM learners with combined science and engineering mindsets and natural curiosity about water in a myriad of applications. 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 · 2025-02

The impacts of uncontrolled wildland fires range from the destruction of native vegetation to property damages to long-term health effects and losses of human lives. Increasing accuracy in projections of wildland fire activity, fire behavior, and wildland fire weather is the key toward developing more efficient fire control strategies and reducing the risks of wildfires. Recent studies have demonstrated that the tools of artificial intelligence (AI) can help in planning for upcoming prescribed burns by providing higher spatial and temporal fire weather forecasts and can also assist in developing more efficient strategies for wildfire risk mitigation. However, the modeling tools that are currently used to predict fire activity are largely subject to a number of temporal or spatial constraints. For instance, most deep learning (DL) approaches for wildfire risk analytics tend to be restricted in their capabilities to systematically capture the multidimensional information recorded at disparate spatio-temporal resolutions. Furthermore, such DL architectures are inherently static and do not explicitly account for complex dynamic phenomena, which is often the key behind the accurate assessment of wildfire driving factors. Finally, these models primarily rely on supervised learning approaches where a large number of task-specific labels (e.g., fire or no fire) are needed. To address these challenges in wildfire risk analytics, this project will leverage inherently interdisciplinary approaches at the interface of Earth system sciences, DL, computational topology, statistics, and actuarial sciences. The project aims to introduce the concepts of topological data analysis (TDA) to wildfire predictive modeling, coupling them with such emerging AI machinery as time-aware graph neural networks. The resulting new methods are expected to better capture the shape patterns in the wildland fire processes with respect both to time and space and to assist in a more reliable statistical assessment of wildfire risks. The new high-fidelity predictive approaches will have the potential to deliver forecasts of fire behavior, fire activity, and fire weather at multiple spatial and temporal scales under scenarios of limited, noisy, or nonexistent labeled information. To enhance the utility of the research solutions in wildfire analytics, the researchers in this project will work in close collaboration with stakeholders, particularly, focusing on the insurance sector. The project will provide multiple interdisciplinary training opportunities at the nexus of wildfire sciences, AI, and mathematical sciences at all educational levels, from undergraduate students to practicing actuaries. 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

Abstract Nearly half of the world’s population lives in countries where malaria is endemic. Plasmodium falciparum, the causative agent of the most severe form of human malaria, is responsible for 95% of malaria deaths worldwide. The project’s main goals are to identify RNA-binding proteins (RBPs) and RNA-dependent proteins (RDPs) in the pathogen; elucidate their importance in parasite development; and finally identify novel pathways that can be targeted to kill the parasite. Ribonucleoprotein complexes are composed of RNA, RBPs, and RDPs and have been shown to play fundamental roles in RNA regulation in eukaryotic organisms. However, in the human malaria parasite, P. falciparum, identification and characterization of these proteins are particularly limited. Our lab has used several approaches including an unbiased proteome-wide approach, called R-DeeP, a novel method based on sucrose density gradient ultracentrifugation, to successfully identify not only RBPs but also RDPs in the asexual stages of the P. falciparum life cycle. Using quantitative analysis by mass spectrometry as well as a combination of computational and molecular approaches, we identified over 800 RDPs, including 500+ proteins not yet associated with RNA. We also reconstruct Plasmodium multiprotein complexes based on their co-segregation and their RNA-dependence. One RDP candidate was functionally characterized and validated as interacting in complex with various Plasmodium non-coding transcripts, including var genes and several ap2 transcription factors. Overall, our novel genome-wide proteomic and molecular approaches provided the first snapshot of the Plasmodium protein-protein interaction networks in the presence and absence of RNA in the asexual cycle of the parasite. No data were however acquired for parasite sexual differentiation, a stage essential for the transmission of the pathogen to the mosquito vector. We therefore now propose to use our novel R-DeeP methodology to not only identify and reconstruct Plasmodium multiprotein complexes based on their RNA-dependence during the parasite sexual differentiation but also validate their potential as new therapeutic targets. We have therefore designed the following two specific aims. In AIM 1, we will adapt and expend our unbiased proteome-wide approach to identify RDPs from early and late gametocytes. Our data will also be used to determine RNA dependent multiprotein complexes that are specific to these parasite sexual stages. In AIM 2, we will use a combination of CRISPR-Cas9 genome editing tools as well as functional genomics approaches to validate the role of RDPs candidates that are involved in sexual differentiation. The results of this work will transform our understanding of the role of RNA-dependent protein complexes (RDPCs) in the parasite biology and identify novel intervention strategies that may interrupt the development of malaria parasites.

NSF Awards · FY 2025 · 2025-01

Artificial intelligence has achieved remarkable success in recent years, largely driven by advancements in foundation models, which leverage complex neural networks trained on vast amounts of data in order to perform a variety of tasks, such as question answering, text summarization, and image generation. This project seeks to extend the success of foundation models to sequential decision-making, where an agent--a programmable entity---interacts with an environment, seeking to accomplish a task by taking a series of actions over time, with each action influenced by the outcomes of previous actions. Sequential decision-making commonly arises in situations characterized by uncertainty, limited resources, or dynamic conditions, where each decision can have an impact on future actions. The objective is to select a sequence of actions that maximizes profits, rewards, utilities, or some other well-defined objective. Adapting foundation models for sequential decision-making is challenging, because high-quality data is often lacking and it requires recognizing task-specific structures and optimizing long-term objectives, where minor differences can drastically change optimal solutions. This project will develop novel methods for overcoming these challenges to significantly increase the applicability of foundation models for a wide range of sequential decision-making applications, such as smart manufacturing, multi-agent systems, and human-machine interaction. This project will develop novel techniques and methods to effectively adapt foundation models to multimodal sequential decision-making. The proposed research will be conducted and evaluated on three thrusts with progressively increasing problem complexity. Thrust 1 studies sequential decision-making problems in textual modalities where the decision-maker only needs to look one step into the future when evaluating the consequences of a proposed action, referred to as contextual bandits. The investigators will develop new techniques such as reward-aware text summarization and mixing foundation model-based and online-learned decision rules that leverage foundation models to warm-start the agent while avoiding being locked into pretrained parameters to improve the performance in the long run. Thrust 2 studies sequential decision-making problems that involve long decision horizons (the full reinforcement-learning problem) and are multimodal. The investigators will develop additional techniques that leverage foundation models for multimodal and hierarchical reinforcement learning. Thrust 3 extends the techniques to the cooperative multi-agent setting, where the foundation models are leveraged to facilitate both centralized and decentralized inter-agent communication, which is crucial for multi-agent coordination. In and outside the classroom, this project will conduct a series of educational and outreach activities, including development of course materials related to foundation models and sequential decision-making, undergraduate research mentoring, and public outreach in local communities. 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 · 2025-01

Obesity is a rising public health concern linked to multiple preventable comorbidities, such as worse infection outcomes, inflammatory disorders, and type 2 diabetes. Obesity affects the immune system, and the underlying immune response is a critical regulator of its outcome. A more common type 1 (Th1) immune response is associated with pro-inflammatory and chronic inflammation outcomes, while the less common T helper type 2 (Th2) immune response mediates protection. Th2 immune responses are also critical in immunity against soil- transmitted helminth infections, which are a significant public health concern, infecting two billion people worldwide. Identification of effector cells and molecules that promote protective Th2 immune responses therefore have broad implications for new treatments in both metabolic disorders and helminth infection. Significant sex differences exist in obesity and parasitic helminth infections, however, the underlying immune mechanisms driving this sexual dimorphism are understudied. In our recent paper we have shown that this mechanism involves macrophage-eosinophil interactions, which are in part regulated by the RELMα protein, released by macrophages in a sex-specific way. In this proposal we expand these results by studying these interactions in diet-induced obesity and type 2 immunity triggered by helminth infection. In Aim 1, we will investigate sex differences in macrophage and eosinophil function and test the hypothesis that these innate cells protect from diet-induced obesity and helminth infection in a sex-dependent manner. Aim 2 will determine whether myeloid cell-intrinsic RELM α promotes macrophage differentiation and eosinophil responses in the adipose tissue microenvironment. Aim 3 will combine single cell transcriptomics from lab-derived data of in vitro and in vivo- derived eosinophils with publicly available datasets to define eosinophil populations and identify specific subpopulations and cellular pathways involved. At the outcome of this proposal, we will have leveraged our expertise in metabolism, immunology and bioinformatics coupled with the strengths of in vivo models and mechanistic in vitro studies with new single cell transcriptomic technologies to determine protective type 2 immune mechanisms. Results from this study will have broad public health implications in tackling the obesity epidemic and gaining critical knowledge of beneficial type 2 immune mechanisms triggered by helminth infections.

NSF Awards · FY 2025 · 2025-01

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Prof. Francisco Zaera of the University of California, Riverside, will explore ways to improve the quality of thin metal films grown on solid surfaces by using atomic layer deposition (ALD). ALD has gained prominence in microelectronics fabrication, catalysis, and the design of energy-related devices such as batteries and supercapacitors due to the high film quality and conformality at a sub-nanometer scale, despite of surface roughness and complexity. However, one key challenge remains when growing metal films because metal atoms sinter into 3D nanoparticles (NPs). Prof. Zaera addresses this challenge via pre-conditioning of the surfaces to produce smoother and better-quality metal films. The knowledge gained shall benefit aforementioned applications, and serve educational purposes by illustrating basic principles in kinetics, catalysis, film deposition, and NP synthesis in undergraduate and graduate classes. Collaborations with Latin American research groups will be forged, and student participation from groups underrepresented in research, Hispanics in particular, will be strongly pursued. The main hypothesis underpinning this project is that control of the structure of the metals deposited on solids by ALD can be achieved via appropriate preconditioning of the surface of the underlying substrates. It should be possible to tune the size and surface density of ALD-grown metal NPs by adjusting the surface density and nature of the ALD nucleation sites: a high density should lead to the rapid coalescence of the developing metal NPs in the early stages of the ALD into 2D films and, conversely, a low density should allow for the NPs to grow in size before coalescing. Zaera's objective will be to study the molecular level chemistry that can afford such control, in particular the use of surface preconditioning as a way to define the characteristics of the metal NPs grown by ALD. Three approaches will be tested: (1) the increase of the density of silanol surface groups to act as nucleation sites; (2) the silylation of the substrate to partially block its nucleation sites; and (3) the derivatization of the nucleation sites to modify the surface chemistry of the metal ALD. Mechanistic studies will be carried out with model flat substrates and controlled ultrahigh vacuum (UHV) environments, relying on a combination of surface-sensitive techniques, including x-ray photoelectron spectroscopy (XPS), temperature programmed desorption (TPD), low-energy ion scattering (LEIS), secondary ion mass spectrometry (SIMS), and Fourier-transform infrared spectroscopy (FTIR). Studies of the preconditioning of the surface will be correlated with subsequent metal ALD tests. 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.