University of California-Davis

NSF Awards · FY 2024 · 2024-09

The Center for Insights into the Pre-emergence Phase of Pandemics (CI3P) will pioneer transdisciplinary initiatives to fundamentally advance the ability to investigate and understand the pre-emergence phase of pandemics. The scale and impact of epidemics and pandemics are expected to increase substantially in future years given global trends in environmental change. Viruses that pose pandemic risk often circulate silently in wildlife, and the events that precipitate outbreaks and spillover pose formidable challenges for early detection. Innovative scientific and technological advances are urgently needed to detect the key events in the pre-emergence phase to inform mitigation of zoonotic disease outbreaks and prevent pandemics at their source. Insights into this pre-emergence phase of pandemics hold the greatest promise for reducing the frequency of epidemics and pandemics. By integrating cutting-edge technology with transdisciplinary research, CI3P will establish sensor systems that detect signals of pathogens circulating in natural reservoirs and changes in health of wildlife, and population and ecosystem measures that can provide insights into how pandemic threats emerge. Scientific and technical innovations achieved by CI3P will have broad applicability beyond animal health, public health, and wildlife conservation. By involving communities in participatory research and engaging the public in One Health solutions, CI3P aims to benefit society by enhancing early detection of pandemic threats, protecting human health, preserving biodiversity, and promoting resilient ecosystems. This Center will catalyze the expertise, creative strategies, and partnerships needed to tackle major challenges in pandemic research, to advance new models for predictive insights into the pre-emergence phase of pandemics. The Center for Insights into the Pre-emergence Phase of Pandemics will facilitate a new paradigm for surveillance of pandemic threats, enabling characterization of key ecological, behavioral and biological events that precede pandemics. This will be achieved through transdisciplinary research and innovative technology needed to 1) inform evolutionary models of virus emergence, characterize community composition and host ecology at high-risk animal-human interfaces, and develop mechanistic models of spillover in complex environments to characterize tipping points in the pre-emergence phase of pandemics, 2) develop multimodal sensor networks for detection of pathogens, disease outbreaks, and key events, that will be deployed alongside participatory community-based research to identify social and practical barriers to large-scale use, and 3) forecast pandemic risk using a suite of machine learning algorithms developed to accommodate rare events and incorporate model-based inferences with real-time data processing for prediction. This Center brings together diverse institutions and partners with the expertise needed to achieve and sustain this bold vision. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Feeding a growing global population and engaging in the bioeconomy to sustainably produce proteins, fats, and other useful biomolecules requires high levels of integration between life sciences and engineering disciplines as well as a deep understanding of the current capacities and limitations of the contributing fields. Developing U.S. advanced biomanufacturing capabilities in this area is a national priority that depends on a workforce with interdisciplinary fluency. This National Science Foundation Research Traineeship (NRT) award to the University of California at Davis will explore new bioprocessing technologies for cellular agriculture and industrial biomanufacturing important for the development of novel, environmentally friendly consumer products, processes, and services. The comprehensive expertise developed through participation in this program will prepare emerging researchers to meet complex challenges here on Earth and ultimately pave the way for long term human space exploration. The project anticipates training a total of 43 graduate students, including 10 stipend-funded master’s trainees, 13 stipend-funded doctoral trainees, and 23 additional trainees from a strategically assembled group of life science and engineering disciplines (Agricultural and Resource Economics, Animal Biology, Biochemistry, Molecular, Cellular, & Developmental Biology, Biological Systems Engineering, Chemical Engineering, Food Science, Nutritional Biology, and Plant Biology). Building on strong relationships with industry partners and faculty researchers who have expertise in precision fermentation, cellular agriculture, plant and algal based bioprocessing, and characterization of novel proteins and biomolecules, trainees will conduct research in three broadly defined research areas: 1) Cultivated Meat (growing animal muscle, fat, and connective tissue cells in large-scale bioreactors to produce a meat product); 2) Alternative (Alt) Proteins for Human and Animal Nutrition (developing alternative proteins such as meat, egg, or dairy products that are plant-based, cultivated, and/or derived via microbial fermentation); and, 3) Natural or Recombinant Plant/Algal Proteins and Small Molecules for Industrial Applications (growing plants, plant cells, or algae under controlled conditions for a broad range of industrial applications). Trainees will cultivate technical and professional skills necessary for designing and implementing novel bioprocessing strategies across 6 categories: Bioprocessing Technical Skills, Technoeconomic and Life Cycle Analyses, Project Management, Team Science, Science Communication, and Bioethics and Professionalism. Experiential learning via an industry-mentored Capstone Team Design Project, opportunities to conduct industry internships, and networking with professionals who are actively working to scale novel bioprocesses will provide program trainees with valuable “real world” insights on potential innovations and emerging career paths in this research space. This NSF Research Traineeship (NRT) project will bring together graduate students from multiple disciplines interested in design and engineering for sustainability to solve real-world problems and increase climate resilience. This project will offer students core technical education and training opportunities and will contribute to the development of soft skills necessary for participation in the future workforce. It will foster collaborations and support immersive experiences for trainees and build a robust community in STEAM fields. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, and potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.   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

Chemistry in space - including the interstellar medium and circumstellar envelopes - is an important source of organic compounds across the universe and may have contributed to the beginnings of life on Earth. The great majority of organic molecules observed in space have been in cold molecular clouds that are often hundreds of light-years away, and the only means of directly observing the compounds is via radio astronomy. Direct observation of the reactions is not possible. Therefore, terrestrial experiments or computer simulations are needed to understand the chemical reactions that create or destroy compounds in these environments. This research team will study chemical reactions in the interstellar medium using a computational method called the retrosynthetic nanoreactor (RNR). The reaction mechanism is the first step toward obtaining accurate reaction rates from laboratory kinetic studies or theoretical kinetic calculations. A graduate student will carry out the proposed calculations that comprise the main portion of the research project. The team will also develop a computationally inexpensive version of the nanoreactor that will be incorporated into the Scientific Programming for Chemistry course to enable undergraduate and high school students to carry out reaction discovery simulations on their personal computers. Mechanistic studies on astronomical molecules of interest will be carried out using the RNR. Starting from a target compound of interest, the proposed method uses first-principles molecular dynamics simulations to predict the possible precursors and gas-phase reactions that lead to its formation, complete with potential energy surface data that indicate the kinetic feasibility of each mechanism under the low density, low temperature conditions typical to astronomical environments. The team propose several methodological advancements that will extend, accelerate, and automate the RNR in a transformative way, providing the scientific community with a powerful, intuitive, and easy-to-use tool for astronomical reaction discovery. The team will create an AI-based recommendation system to rank and prioritize the relevance of molecules in astrochemical setting. 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

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This conference is the third annual program-wide meeting in the National AI Research Institutes Program. The Summit for AI Institutes Leadership (SAIL 2024) is an NSF-sponsored conference organized and executed by the program’s hub activity, the AI Institutes Virtual Organization (AIVO). The conference gathers the leaders and other key personnel from all AI Institutes to foster community building of those Institutes and other related activities into a network of collaborating organizations conducting knowledge exchange, growing their own competencies, and engaging with the broader public. The conference will take place 6-9 October 2024 in Pittsburgh, Pennsylvania. This conference aims to maximize the value of the AI Institutes as a flagship national AI investment. The conference delivers on the intent of NSF and its funding partners to continue to nurture the AI Institutes into a fully cohered national program, resulting in synergy across the constituent institutes that is greater than the sum of its parts. This gathering builds upon the successes and lessons from the previous SAIL events (2022 and 2023) and continues a successful record of establishing SAIL as the flagship event for the National AI Research Institutes program. The conference program addresses the needs of AI Institutes in various stages of their lifecycle, from those in their fourth year to newly-established AI Institutes. This greatly enhances knowledge transfer among all. The program includes knowledge exchange about education and outreach, project management, computing and research infrastructure, communications, workforce development, and ethics. The conference is comprised of a balance of community-moderated panels with plenary sessions and other program-wide community building. A workshop day prior to the main conference allows the program’s special interest groups to hold smaller community workshops around topics of interest withing a specialized area, and across institute boundaries. Following the SAIL conference events, an AI Institutes Expo Day co-located in Pittsburgh will build upon this gathering to create a program-organized public engagement event. Expo day will combine Institute exhibits, talks, panels, and networking venues to allow the public to directly and efficiently engage with the AI Institutes toward greater understanding of AI and the potential initiation of new collaborations. 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

The IGNITE CAREER project will shed light on a fundamental question in biology: How does microbial warfare lead to ecological success of pathogens? Importantly, this project develops foundational knowledge in economically important plant pathogens. These plant pathogens cause devastating epidemics of wilt disease on crops that are major components of the American diet, including potato, tomato, banana, and peanut. These wilt pathogens are more than just threats to food security; they are complex organisms with extensive warfare strategies. To pioneer understanding their intricate warfare mechanisms, this project leverages innovative computational analyses and synthetic biology methodologies to study their ‘weapons’—toxins—at every level, from the smallest molecule to their impact on entire ecosystems. The IGNITE project has significant societal benefits—it establishes a cutting-edge STEM educational program, the IGNITE CURE, that provides hands-on research experience to undergraduate students, with an emphasis on transfer students. This initiative will prepare the next generation of scientists, equipping them with the technical and essential soft skills that will empower them to tackle emerging plant health and public health challenges. Students will gain critical thinking skills and gain confidence in analyzing big-data (population genomic data relevant to plant epidemics). Moreover, the data from the CURE will be openly and rapidly shared on the national genomic database, NCBI, benefitting national and international surveillance programs to predict and mitigate emerging plant pathogens. Overall, the IGNITE CAREER project promotes two major domestic interests: food security and the education of emerging scientists. The central hypothesis of this project is that the molecular mechanisms and evolution of type 6 secretion system (T6SS) toxins and immunity proteins drives the ability of phytopathogenic Ralstonia to antagonistically compete against other plant-colonizing bacteria. To understand T6SS toxins across biological scales, the project has three aims: (1) explore the evolution of Ralstonia T6SS genes, (2) characterize the molecular and cellular impacts of Ralstonia toxins and immunity proteins, and (3) test the ecological role of the Ralstonia T6SS in inter-bacterial competition. The project will generate and leverage new public sequence data to shed light into the evolutionary processes that shape phytopathogen diversity at the population and molecular scales. Through cytological profiling and forward genetic screens (random-barcoded transposon mutant sequencing, RB-TnSeq), the researchers will connect the evolutionary diversity of toxin/immunity pairs to their phenotypic impacts on bacterial cells. With a cutting-edge barcoded bacterial mutagenesis approaches, they will quantify the in planta antagonism between bacteria with diverse repertoires of toxin/immunity pairs. By combining experimental and discovery-driven data from single cells to landscapes, they have developed a set of aims that will not only allow them to understand how pathogen genotypes lead to success in an environment but also how competition and successional dynamics modulate core evolutionary concepts like “survival of the fittest.” 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

Climate change is redistributing species globally, leading to range shifts, population declines, local extinctions, and cascading effects on ecosystem function and provisioning of ecosystem services. In the ocean, increasing sea surface temperature exerts natural selection, shifting the genetic composition of populations. While temporal baselines and long-term datasets are beginning to emerge, these approaches may take decades to yield critical results. On the other hand, extreme events such as marine heatwaves can provide a means to study evolutionary responses to environmental change in the short term. Mass mortality during extreme climate events can result in dramatic shifts in genetic composition due to natural selection, affecting future adaptive capacity. Perhaps the most widespread example of climate-driven decline is the global bleaching and mortality experienced by reef-building corals over the last decade. This project aims to investigate rapid evolution in reef-building corals during a single marine heatwave event; how does the mortality induced by warm water events change the genetic composition of coral populations? A combination of field experiments and genomic sequencing will yield new information on which individuals are most likely to survive marine heatwave events, which genes confer tolerance, and how predictable the evolutionary response is. These research goals are intertwined with the educational goals of this proposal, which focus on increasing student persistence in STEM by reducing barriers to research participation through a progression of courses and mentored research experiences. Global change has invigorated interest in understanding the dynamics of contemporary evolution. However, due to the challenges of establishing and maintaining longitudinal datasets most studies of contemporary evolution rely on either i) contrasting populations inhabiting spatial environmental gradients or ii) laboratory experiments. In the first, we do not directly observe evolution, rather inferring adaptive processes based on current patterns of genetic diversity. In the second, while we may directly observe evolution, we are limited by the conditions and species that can be established in laboratories. This proposal leverages extreme climate events to observe evolution in real time and in natural populations of the reef-building coral Acropora hyacinthus. Phenotypic and genomic variation will be measured prior to selection and the clonal nature of corals allows for replicated estimates of fitness (survival). We can therefore test fundamental questions in evolutionary biology and global change, including: 1) How does selection shape plasticity and tolerance? 2) What is the genetic architecture of adaptation? and 3) How predictable are evolutionary responses to environmental change? Together this will advance our understanding of evolutionary responses to rapid environmental change and enhance our ability to predict responses to future climate events. 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

Language allows people to produce and understand sentences that have never been said before, making human language unique among the communication systems found in nature. Humans do this by recombining pieces of sentences that have been heard before in new ways to express new meanings. But among the multitude of ways that humans can make new sentences, there are still some constraints. For example, speakers need to know what is allowed in their language and what is not in addition to knowing detailed statistics of how words and sentence structures are typically used. Deciding how to say a new sentence therefore requires searching through many ways to recombine known words and phrases to express a message in a way that is both clear and natural sounding. Understanding how language users sort through these possibilities to express new ideas has been a challenge for a scientific understanding of the human mind. This project combines tools from linguistics, psychology, and computational approaches to advance an understanding of language processing in the mind. Advances in this area contribute to improved diagnosis and treatment of language disorders, improvements in second language instruction, and improvements in teaching other language-related skills such as programming. This project supports education by training students in quantitative skills including experimental methods, programming, and statistics. Teaching these quantitative skills in the context of linguistics provides an opportunity to reach more diverse audiences, such as women and underrepresented minorities, who are better represented in linguistics compared to some other disciplines. The project also creates publicly available corpora of naturally occurring instances of verb usages that are manually annotated. These corpora enable future research on a wide variety of questions in linguistics by researchers with fewer resources for data annotation. This project focuses on how speakers use different types of knowledge when deciding what to say. For example, many verbs in English (such as “give” or “send”) can be used in two structures: (1) I gave the man a book, and (2) I gave a book to the man. Whether a speaker prefers one structure versus the other depends in part on the verb. For example, speakers prefer to use “give” in structure (1) but “send” in structure (2). Preference also depends in part on other properties of the sentence. For example, speakers prefer to use structure (1) when the receiver is short (e.g., “I gave him a book”) and structure (2) when the receiver is long (e.g., “I gave a book to the old man who I met at the grocery store”). These other properties are more generalizable in that they apply to all verbs, unlike the idiosyncratic verb-specific preferences. This project examines how speakers make use of both the generalizable constraints and the verb-specific preferences when deciding in real time which structure to use for a sentence. This project first builds computational models of how the mind integrates these two different types of knowledge. Then, these models are tested using controlled experiments. Finally, further computational models of how language changes over time are built. Each generation of speakers must learn the existing language, but each generation also subtly re-shapes the language, creating a situation in which speaker preferences and the language itself are simultaneously co-evolving. Computational models are used to investigate how human capacity for language and the structure of the languages become jointly optimized. 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

The emergence of cyanobacteria approximately 2 billion years ago was a pivotal moment in Earth's history. Cyanobacteria increased the amount of oxygen in the atmosphere from nearly none to over 10% of modern levels through the process of oxygenic photosynthesis. This oxygen-rich atmosphere allowed for the evolution of aerobic respiration and complex life forms. However, tracing the early evolution of cyanobacteria has been challenging because cyanobacteria rarely ever form fossils. In addition, most modern cyanobacteria are Phycobacteria, and their sister lineage, Gloeobacteria, are very rare – until 2020 we only knew of two species. The poor representation of Gloeobacteria has made it difficult to determine if their unique features were ancestral or results of specific evolutionary paths. Recent discoveries, however, have revealed a much greater diversity within Gloeobacteria than previously recognized, especially in high-latitude and high-altitude habitats. This project aims to comprehensively characterize the hidden diversity of Gloeobacteria, which will shed new light on the early evolution of oxygenic photosynthesis, a process that fundamentally shifted the trajectories of life on Earth. The first aim involves isolating new Gloeobacteria cultures through targeted fieldwork and real-time nanopore sequencing. These cultures will be characterized in the second aim based on their genomic, phylogenetic, morphological, and physiological traits, leading to detailed taxonomic descriptions. The third aim focuses on fostering interdisciplinary research by organizing a workshop to unite experts from various fields. This study is crucial for comprehending the evolution of cyanobacteria, as Gloeobacteria hold key insights into the origins of oxygenic photosynthesis. The project plans to increase the number of known Gloeobacteria strains tenfold and significantly expand the number of described species, bridging hundreds of millions of years of evolutionary history. Further, given that polar regions are disproportionately threatened by climate change, this work will help document and conserve key microbial diversity before these habitats disappear. Student training opportunities will be provided at various academic levels, and by partnering with Let’s Botanize, the findings of this research will be broadly communicated to the public. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This research project will deploy deep learning to three mainstream research areas in statistics: survival analysis, causal inference, and conformal inference. Survival analysis studies event-time data, such as time to death, disease recovery, new employment, or equipment failure. Causal inference aims at assessing the causal effect of one variable (the treatment) on another variable (the outcome), while controlling for potential confounding factors. Conformal inference is a powerful tool to construct prediction intervals without relying on specific assumptions. These three areas have not yet fully benefited from the advantages that deep learning can bring, and the goal of this project is to fill this gap. This research falls squarely in the realm of data-centric AI (artificial intelligence) by tackling some of the key issues where Statistics can make major contributions in the age of AI. A major emphasis is the development of new methodology and theory that will be widely disseminated. The proposed approaches will be applied to various data, including data for breast cancer, hospital care, AIDS studies, and air quality. Codes for the algorithms developed in the project will be posted on CRAN for R or on github for Python. Student researchers will receive training in research, computing and communication skills. The research findings will be incorporated in graduate curricula, undergraduate research projects and short courses at workshops and will be presented at professional meetings. Project 1 (Deep Learning for Survival Data) will fill a void in deep survival analysis, referring to approaches that employ deep learning for the analysis of incomplete event-time data, by developing hypothesis testing procedures on two fronts: testing the significance of some specific covariates; and goodness of fit tests for survival models. A key feature of survival data is that they routinely involve incomplete observations, such as random right censoring, and therefore regression methods must be adjusted to account for such censorship. Deploying existing methods for inference and testing for deep learning approaches is challenging because of the ability of deep learning to detect the null model structure even while performing the optimal search in the full model. Consequently, conventional test statistics will vanish to zero asymptotically even under the null hypothesis. A new framework of hypothesis testing is thus needed to prevent the test statistic from approaching zero under the null hypothesis. To our knowledge, this is the first attempt to perform significance tests for censored survival data when deep learning is employed to model the risk function nonparametrically. Project 2 (Advances in Causal inference) addresses two problems of high relevance in causal inference: testing for continuous treatments and causal inference for censored survival data. Existing tests for continuous treatment effects fail to attain correct Type-I errors and therefore are not suitable when deploying deep learning. A new test procedure will be designed to resolve this problem with supporting theory. For survival outcomes, the conventional average treatment effect is shown to be ill suited for causal inference, motivating a new paradigm based on median or other quantiles to quantify treatment effects. Project 3 (A New and Improved Approach for Conformal Inference) explores a better conformal score that leads to improved conditional coverage probabilities compared to existing state-of-art score functions. The project will include the first theory for deep learning in conformal inference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Plant growth is brought about by continuous cell divisions that take place in a regulated manner throughout a plant’s life. To produce cells for new tissues, plants must precisely orient the division plane when cells undergo cell division, simply because plant cells cannot move. In order to regulate division, plant cells establish a division site at very early stages of cell division by building large arrays of proteins called microtubules that form an array at the cell surface, which then is taken apart after cells enter later stages of division. The site at the cell surface is where new cell wall is synthesized to divide the mother cell into two daughter cells that are aligned properly for further growth. This project is aimed at understanding the molecular basis of how the cell division plane is determined. Earlier accomplishments in the collaborating US and Israeli laboratories led to the identification of several key proteins affecting formation of microtubule arrays. Because plant and animal cells exhibit great differences in how cell division occurs, the outcome of this study will also shed light on understanding the different underlying mechanisms possible in cell division among all species. The project will also provide multidisciplinary training to participating undergraduate and graduate students, The PIs also will create an historical perspective on the development of research on plants suitable for a wider audience of students interested in plant science. To build a new cell wall during cytokinesis, a mitotic plant cell forms the microtubular preprophase band (PPB). The PPB marks the future cortical division site and disappears upon nuclear envelope breakdown. Earlier studies indicated that intact actin filaments are required for the cytokinetic apparatus of the phragmoplast to recognize the division site. This work builds on the discovery of large molecular assemblies containing both Kinesin-12 motors and Myosin XI motors, as well as other novel proteins. The project employs approaches of molecular genetics, cell biology including live-cell imaging, and protein biochemistry including mass spectrometry to answer a long-standing question of how the PPB at prophase and the phragmoplast at telophase/cytokinesis communicate at the molecular level. The project will first dissect the function of the cytoskeletal motor assemblies at the cortical division site by revealing how the Kinesin-12 and Myosin XI motors are integrated together with other associated proteins in the cortical assemblies. The research will also test whether hypothesized cytoskeletal motor assemblies formed in the phragmoplast midzone are connected to the molecules at the cortical division site. To uncover how the cell cycle-dependent functions of specific Myosin XI motors are expressed, the research will reveal dynamic subcellular locations of different motor paralogs and determine whether their functions are regulated via phosphorylation. The outcome of this project will advance knowledge of how two major cytoskeletal elements and their associated motors work in concert to orient the cell division plane in plants. This collaborative US/Israel project is supported by the US National Science Foundation and the Israeli Binational Science Foundation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The current educational framework of K-12 STEM programs needs a major overhaul to introduce students to the extensive range of crucial STEM fields, particularly those facing workforce shortages as identified by the CHIPS and Science Act. Limited exposure and lack of engagement often diminish interest among younger students and contribute to a serious shortage of students viewing STEM as a career choice in their early school years. To help maintain our nation’s lead in technological development, the UC Davis team proposes the "Broad Engagement for Students and Teachers to Advance and Reinforce Science” (BE-STARSE) initiative, a robust community engagement model emphasizing close engagement of university faculty, research scientists, industry STEM professionals, national lab experts, and retired educators with K-12 students, K-12 teachers, and undergraduate students. The principal aim of BE-STARSE is to invigorate workforce innovation and foster inclusion in the expansive fields identified by the CHIPS and Science Act. Its goal is to stimulate system- level thinking and foster an interest in semiconductor microelectronics among K-12 students and early-stage college students. BE-STARSE engages young students before they fully grasp foundational concepts taught in later years. This program will “pull back the curtain” to help students develop a physical intuition and big-picture understanding of semiconductor-relevant topics through intense intellectual interactions between the researchers, host mentors, faculty, and domain experts. Through BE-STARSE, the UC Davis team plans to immerse K-12 students in the practical aspects of emerging technologies via workshops in an active microfabrication lab (a cleanroom), design labs, maker spaces, and hands-on activities at the UC Davis campus. Furthermore, BE- STARSE will provide internship opportunities to undergraduate students in the realms of semiconductors and microelectronics and facilitate close interactions with interns, K12 students, and teachers. A special emphasis will be placed on engaging students from communities historically marginalized within STEM fields, and on fostering leadership skills, career planning, and bolstering diversity, equity, and inclusion. The UC Davis team endeavors to expand the career horizons for students from the Central Valley of California, a traditionally underserved and economically disadvantaged area with a significant percentage of under-represented minority students. The project will aggressively recruit ethnic and racial minorities, women and girls, Pell Grant-eligible students, recent immigrants, and first-generation college students. The project efforts are expected to significantly impact the career choices of these young students by providing exposure, mentorship, first-hand experiences, and training opportunities. By expanding the talent pipeline, these efforts will strongly impact the ability of universities and technology industries to recruit more highly motivated, well-educated, and aspiring young talent. This project is co-funded by the Advancing Informal STEM Learning (AISL) and Discovery Research PreK-12 (DRK-12) programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With the support of the Chemical Catalysis Program in the Division of Chemistry, Professor Annaliese Franz of the University of California, Davis will study the development of new silicon-based molecules and their formation of structurally-defined complexes with metals. These complexes will be studied as catalysts for applications to the efficient synthesis of organic materials and renewable polymers that are used in technology, medicine, and daily life. The use of catalysts is important for increasing industrial efficiency and reducing waste production, and silicon is the second most abundant element in the earth's crust. As such this research will address issues of sustainability and provide opportunities to prepare new chemical structures for discovery in chemistry and catalysis. This project will also train undergraduate and graduate students to develop technical and critical thinking skills for chemical synthesis and catalysis. Professor Franz will also actively engage in STEM outreach activities including demonstrations and workshops for elementary and middle school girls to participate in hands-on activities and learn about catalysts, energy, and polymers. This project aims to develop innovative multi-functional silanol ligands, including access to Si-stereogenic compounds, with unique properties that can be exploited to design novel homogeneous chiral catalysts with the potential for high catalytic activity and stereoselectivity. The design and synthesis of new functionalized chiral silanol will be prepared and their metal-coordinating abilities, structural studies, stability, and catalytic activity will be examined in several bond activation reactions and photocatalytic applications. The structure, binding interactions, and mechanism of the new ligands and catalysts will be studied using various spectroscopy methods, kinetics, X-ray crystallography, and mass spectrometry, and also computational studies. This project will also contribute to training the next generation of scientists and broadening participation. Professor Franz will be engaged in mentoring and outreach activities to broaden participation in STEM, including demonstrations and workshops for elementary and middle school girls to participate in hands-on activities and learn about catalysts, energy, and polymers. Professor Franz will also design and implement new research-based courses for undergraduate students that will increase research opportunities for diverse first-year college students and transfer students to build critical skills that will help launch them into careers in science and technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The project aims to conduct comprehensive statistical and computational analyses, with the overarching objective of advancing innovative nonparametric data analysis techniques. The methodologies and theories developed are anticipated to push the boundaries of modern nonparametric statistical inference and find applicability in other statistical domains such as nonparametric latent variable models, time series analysis, and sequential nonparametric multiple testing. This project will enhance the interconnections among statistics, machine learning, and computation and provide training opportunities for postdoctoral fellows, graduate students, and undergraduates. More specifically, the project covers key problems in nonparametric hypothesis testing, intending to establish a robust framework for goodness-of-fit testing for distributions on non-Euclidean domains with unknown normalization constants. The research also delves into nonparametric variational inference, aiming to create a particle-based algorithmic framework with discrete-time guarantees. Furthermore, the project focuses on nonparametric functional regression, with an emphasis on designing minimax optimal estimators using infinite-dimensional Stein's identities. The study also examines the trade-offs between statistics and computation in all the aforementioned methods. The common thread weaving through these endeavors is the synergy between various versions of Stein's identities and reproducing kernels, contributing substantially to the advancement of models, methods, and theories in contemporary nonparametric statistics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This project aims to build a workbench for scientists and engineers to address numerical issues in real-world applications. Numerical issues are issues caused by the gap between mathematical (real) numbers and the number representations used on computers, like floating point. Ultimately, this gap makes it difficult for scientists and engineers to develop software that does numerical computation accurately and runs reliably and efficiently on a variety of hardware and software platforms. Over the years, the research community has studied these issues and developed a number of tools that make developing numerical software easier, but these tools have become difficult to use together. In this project, the investigators will develop a set of standards, benchmarks, and user interfaces to make these existing tools interoperable and thus easier to use in concert. The project’s novelties are a set of standards where floating-point computations can be connected to the hardware and software platforms they run on, along with observed bad inputs or bugs. The project’s impacts are its potential improvements to real-world software packages, making them faster and more reliable across a wide range of hardware and software. Additionally, the investigators plan a variety of community-building initiatives including a community meeting, workshops, and REUs to build further ties within the numerical research community and between that community and practitioners in industry, national laboratories, and academia. The project builds on the existing FPBench standardization and interoperability effort. That standardization effort largely focuses on unambiguously describing floating-point computations, but real-world numerical workflows must track much more information: representative inputs; platform characteristics; pointers into codebases; and error bounds, observed or proven. This project will extend the FPBench standard with new formats to record and transmit this additional information, and update a variety of existing, widely-used numerical tools to use the new format. The investigators will then collect more benchmarks from real-world applications, recording rich metadata descriptions using the new formats, and distribute the extended tools and new benchmarks. To tie these standards, tools, and benchmarks together, the investigators will develop a novel, task-oriented user interface for scientists and engineers dealing with numerical issues. This interface will dispatch individual tools and collect the information they generate (in the new standard formats) in a single database, transparently passing the necessary information to every tool and informing the user when running additional tools would be useful. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Effective climate policy design requires reliable methods to measure the economic impacts of climate change. An interest in climate and environmental economics is the effect of additional exposure to different temperature levels on economic outcomes. Because damage from increased exposure tends to be most severe at extreme temperatures, correctly capturing these effects is important to inform climate and environmental policy. Existing methods use year-to-year changes in temperature and other climatic variables to measure the impact of these climatic variables on various outcomes, thus are not able to account for these extreme temperatures. This award funds a research agenda that will develop flexible regression methods that account for the extremes in exposure and thus offer new methods to accurately measure the effects of climate change on economic outcomes. The research will also study other flexible regression methods that builds on the new flexible methods developed in this study. The researchers will develop statistical software that will be used to implement the new methods thus making them widely useful to applied researchers. The new methods will improve climate research quality as well as enhance research in other social sciences. The research results will also improve climate and environmental policy making and implementation as well as help to establish the US and a global leader in climate economics. Binned regression models used to estimate and draw causal inference in mixed frequency panel data in climate economics have many drawbacks that have not been addressed despite their ubiquitous use in climate economics research. This award will fund research to develop a comprehensive toolkit for understanding the flexible binned regressions methods with mixed-frequency panel data method, and generalizations thereof. The unique technical and methodological challenges posed in this approach will be addressed as part of this grant. In particular, the research will demonstrate the empirical and theoretical limitations with current approaches that can potentially lead to incorrect conclusions. It will also develop more robust, data-driven estimation and inference methods. The theoretical work will also contribute to the literature on non-parametric estimation and inference for mixed-frequency panel data. While the contributions of the research under this award are expected to include several novel mathematical statistics results. Finally, the research will develop general purpose statistical software, train graduate students, and produce general interest review articles. The new methods will improve climate research quality and policy as well as enhance research in other social sciences. The research results will also help to establish the US and a global leader in climate economics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The proposal seeks support to cover the travel expense for invited participants from US institutes to participate a research workshop in Taipei, on June 26-27 of 2024. The requested travel support enables important technology exchanges among research stakeholders. The workshop enables in-person exchanges and helps facilitate the creation of research collaboration in cyber security research. It allows participants to enrich their international collaboration experiences and build joint research plans that are mutually beneficial. The planned workshop will explore the research and collaboration opportunities in the research topic areas of Trustworthy AI, AI for Security, Resilient Networks, Zero Trust system, Post quantum security, and possibly others. The workshop will hold lightening talks and round table discussions in three thrust areas: AI and Security, System and Network Security, and cryptography and applications. The workshop will offer a unique experience for cyber security researchers to engage with each other under a two-day extensive program that allow for research ideas sharing, emerging topic discussion, and research roadmap definition. 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 · 2024-03

Animals use multiple pieces of information from their environments to make decisions such as where to search for food, or which option is the best choice. How animals learn about available options has traditionally been studied in relation to a single reward (such as a particular food item). However, animals including humans often have to make decisions that vary in multiple types of reward. The research from this grant will determine how animals make these more complex types of decisions. To do this, bumblebees will be used: these bees visit a wide variety of flower types and in doing so learn to visit the most highly rewarding ones. The majority of research has focused on how bees learn and make decisions based on a single reward from flowers (nectar), despite the fact that bees collect multiple types of reward (the most common being nectar and pollen). This research will increase our knowledge of bumblebee foraging, which is particularly timely given recent bee declines. It will also more broadly inform our understanding of how animals, including humans, make complex decisions. The grant will support a postdoctoral researcher, a graduate student and undergraduate researchers. The research will be presented at national and international conferences, as well as to the public through a number of outreach events. Finally, 1-2 high school students per year will work alongside graduate students on research projects, with the aim of encouraging students from diverse backgrounds to pursue careers in science. This project will address how multiple rewards affect cognition (learning, memory and decision-making), in three objectives. 1) It will be determined how learning is affected by multiple rewards through establishing how single vs. multiple rewards (nectar and pollen) affect how well a stimulus is learned, and the timing of reinforcement. 2) It will be determined how animals make decisions when choosing between options with multiple rewards, through addressing how bees make decisions between flowers that vary in nectar and pollen quality. For this objective it will also be determined whether absolute or comparative judgement is used. 3) It will be determined how bees’ motivational state affects their memory of flowers with multiple rewards, by addressing whether motivational state when learning and remembering affects memory of reward or stimulus quality. All objectives will be carried out using lab- and greenhouse-based experiments with artificial flowers and commercial bumblebee colonies. 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 · 2024-03

Pollen production and regulation of plant fertility are essential to agriculture. Hybrid corn and other grains have significantly boosted world food production. This project will investigate the functions of RNAs that support pollen development in anthers (the organs that make pollen in flowering plants). The project will utilize plant genomics and developmental biology to determine the biological roles of small RNAs and the proteins that utilize them for anther functions. These RNAs are required for robust male fertility: in the absence of the small RNAs, at normal temperatures, development fails, yielding male sterility. Fertility can be rescued under lower temperatures, linking environmental conditions to the role of these small RNAs. The project focuses on maize because it is an optimal system for the proposed experiments; plus, male fertility is key to the production of hybrid corn seed. Outcomes of this project could include improved control of male fertility in grass crops. Broader impacts of the project include training of students in plant and RNA biology. The project will focus specifically on the class of 24-nt phased, secondary siRNAs, known as “24-nt phasiRNAs” that are highly enriched in meiotic-stage anthers. The project will characterize phenotypes and molecular genetic analysis of loss-of-function knock-outs including the Dicer-like5 (Dcl5) gene, for which the preliminary data demonstrate male sterility. The questions to be addressed by this project include which basic Helix-Loop-Helix transcription factors function to activate transcription of the 24-nt phasiRNAs? How does the core biogenesis protein DCL5 function, and what is the exact nature of the phenotype of sterility in its absence? Are there genetic modifiers of the dcl5 conditional phenotype in other backgrounds of maize? What are the key catalytic Argonaute proteins that load the 24-nt phasiRNAs for their function? And what are the target RNAs with which the phasiRNA-loaded Argonaute proteins interact? This award was co-funded by the Genetic Mechanisms Cluster in the Division of Molecular and Cellular Biosciences and the Plant Genome Research Program in the Division of Integrative Organismal Systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-03

This project is a collaboration among seven laboratories with diverse and complementary expertise. The overarching goal of the project is to understand the role of extracellular RNA (exRNA) in communication between cells and in shaping the community of microbes, especially bacteria, that live on and inside plants, insects, and humans. These collections of microbes are often referred to as microbiomes and are critical for the health of plants and animals, including humans. A healthy microbiome promotes a healthy immune system, but how healthy microbiomes are maintained is poorly understood. This project will test the hypothesis that RNA secreted by host cells plays a central role in maintaining health both through communication among cells and by modifying the microbiome. RNA is best known for its key role in protein production inside cells, such as in RNA-based COVID vaccines. However, not all RNA encodes proteins, and cells produce more non-coding RNA than coding RNA, some of which is actively pushed into the environment by cells. This secreted RNA appears to be taken up by other cells, including bacteria and fungi, where it could potentially impact their growth. Understanding how exRNAs shape communication between cells and organisms will enable manipulation of exRNA communication in both agriculture and medicine, which will lead to development of environmentally friendly pesticides, as well as treatments that promote formation of healthy microbiomes in both plants and animals. This knowledge will also enable development of diagnostic and therapeutic tools for early detection and/or treatment of disease. All cellular organisms secrete RNAs. The functions of these extracellular RNAs (exRNAs), however, are poorly understood. Two likely functions are intercellular and interkingdom communication. Open questions abound in exRNA biology: how are exRNAs selected for secretion, how are they targeted to recipient cells, and what are their roles in normal health and organismal fitness? Arabidopsis leaf exRNA isolates are highly enriched in the post-transcriptional modification N6-methyladenosine (m6A) (as compared to total cellular RNA) suggesting that post-transcriptional modifications may tag specific RNAs for export. Consistent with this, human exosomal microRNAs are enriched with m6A (relative to cytosolic microRNAs). Interestingly, a large number of mammalian small non-coding RNAs (ncRNAs) that localize to the external cell surface were recently found to harbor specific sialylated glycan modifications. These observations suggest that specific RNA modifications tag RNAs for cellular export and direct entry into appropriate recipient cells. This project will 1) test the hypothesis that exRNAs have specific features marking them for secretion and uptake, both within and among species, 2) determine how exRNAs are transferred from signaler to receiver cells, and 3) assess the impact of exRNA on microbiome health and composition through examining human gut, insect gut, and leaf surface models. This project was co-funded by the Directorate for Biological Sciences, and the Plant Genome Research Program and the Plant Biotic Interactions Program in the Division of Integrative Organismal Systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Other NSERC · FY 2024

Ecology, Stoichiometry, Transient Dynamics, Dynamical systems, Mathematical Biology

Other NSERC · FY 2024

Emerging modes, Activity-travel behaviour, Behavioural modelling, Econometric modelling, Automated vehicles, Ride-sourcing, Stated preference, Travel demand modelling, Mobility-as-a-service