University Of California Berkeley

NIH Research Projects · FY 2024 · 2024-08

Project Summary/Abstract The correct implementation of developmental programs depends on information encoded in an organism’s DNA. Despite decades of work in dissecting the spatial control of gene expression in embryonic development, we know relatively little about the temporal control of these programs—largely due to reliance on dead, fixed tissues. Recently, our lab established new technologies for real-time measurements of input transcription factor concentration dynamics and output transcriptional activity in single cells of the early embryo of the fruit fly Drosophila melanogaster. These measurements have revealed that the transcriptional activity of individual genes is not constant in time; rather, bursts of gene expression arise when promoters transition between ON and OFF states. Transcriptional bursting in eukaryotes, particularly in developmental genes, is ubiquitous; most transcription factors control mRNA levels by modulating bursting frequency, duration or amplitude, or some combination thereof. Critically, while we have uncovered which bursting parameters transcription factors modulate to dictate transcriptional dynamics, we remain ignorant about how this control is implemented at the molecular level. Here, we propose to make progress toward uncovering the molecular mechanisms by which the Dorsal activator controls transcriptional bursting in the early embryo of the fruit fly Drosophila melanogaster. We will combine our cutting-edge imaging and computational technologies for simultaneously measuring Dorsal concentration dynamics and the instantaneous state of its target promoters (ON or OFF) in individual nuclei of living embryos. Specifically, we will (1) use our novel compound-state Hidden Markov model to determine whether Dorsal controls burst size, frequency, amplitude, or some combination thereof, in order to generate hypotheses about the mechanisms of action of this activator, (2) determine whether stable clusters of high Dorsal concentration that we recently discovered play an active role in regulating transcriptional dynamics, and (3) use cutting-edge lattice light-sheet and adaptive optics microscopy to detect functional Dorsal binding events at snail and determine how this binding triggers the cascade of biochemical events that leads to the initiation and maintenance of transcription. Overall, our proposed work will establish a clear workflow for the in vivo dissection of the molecular mechanisms of transcriptional control in development. This approach is amenable to implementation in other genes in the fruit fly as well as in other workhorses of developmental biology. We envision that our mechanistic insights will make it possible to derive theoretical models of developmental decision-making that will empower future synthetic applications as well as reengineering of multicellular organisms, for example to fix developmental defects or to halt states of unchecked cellular proliferation in cancer.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Over 250 million children under age 5 (43%) in low- and middle-income countries (LMIC) experience delays in development which have lasting effects on academic attainment, literacy, and economic opportunities, contributing to adverse health. Early interventions focused on responsive caregiving, early learning, and nutrition in this context have improved short-term health and development outcomes, however, whether these effects persist beyond 6 years of age is largely unknown. Further, few interventions have simultaneously targeted infectious disease prevention, another known risk factor for poor childhood development that disproportionately impacts children in LMICs. The objective of this K99/R00 proposal is to identify mechanisms through which early WASH and nutrition interventions impact health and development, assess whether these effects persist into adolescence, and estimate the potential impact of novel multicomponent interventions that target multiple prevalent risk factors in early life simultaneously. The work proposes to (1) leverage data from a large cluster randomized controlled trial in rural Bangladesh to uncover the mechanism of impact of an effective early water, sanitation, hygiene (WASH), and nutrition intervention on middle-childhood development, (2) use population intervention effects to identify early intervention targets that take into account baseline prevalence of risk and the confluence of risk factors that children experience in early life, and (3) follow up children who received the early WASH and nutrition intervention to evaluate impacts on health and development in adolescence. This work fills critical gaps in the literature regarding how early WASH and nutrition interventions work to improve outcomes, which combinations of interventions could lead to the largest improvements in child development outcomes, and the impact of early WASH and nutrition interventions on later adolescent health and development outcomes. Conducted at the University of California Berkeley, the proposed research will be guided by an exceptional mentor team with expertise spanning epidemiologic and biostatistical methods and adolescent health. The proposed plan builds on the applicant’s background in the design and evaluation of interventions in early childhood by providing subject matter training in (1) late childhood and adolescent health and development; as well as methodological training in (2) causal mediation analysis; (3) population intervention effects and target trial emulation with observational data; and (4) best practices in reproducible and transparent research. Combined, the training and research plan prepare the applicant to pursue their long-term goal of conducting research to improve health and development over the life course in low-resource global settings. Aligned with NICHD’s strategic direction to improve child and adolescent health and the transition to adulthood, this work will inform the design of future interventions that optimize the health and development through childhood and adolescence in the context of global poverty.

NSF Awards · FY 2024 · 2024-08

Engineering life beyond nature’s constraints would benefit greatly from an expanded set of amino acids beyond the 20 that are used to synthesize proteins in living organisms. This project will enable economical, large-scale production of proteins with non-standard amino acids for a variety of applications, including enzymes with improved stabilities or novel catalytic activities, biomaterials with enhanced properties, and pharmaceutical proteins. The resources for producing these non-standard amino acids will be sugars derived from waste or from U.S. agriculture, which may lead to new markets for U.S. agricultural products. Postdoctoral fellows, undergraduate students and high school students will participate in this project, and will help construct educational and interactive public exhibits on sustainable manufacturing. Engineering life beyond Nature’s constraints would benefit greatly from an expanded set of amino acids beyond the 20 that are used to synthesize proteins in living organisms. The goal of the proposed work is to develop a polyketide-based platform to produce many non-canonical amino acids (ncAAs) and demonstrate their incorporation into proteins expressed by Bacillus subtilis. The investigators have chosen B. subtilis as the host for their system because of its GRAS (generally regarded as safe) status, its ability to express PKSs, its precedence and available tools for genetic code expansion, and its industrial use to produce recombinant proteins and enzymes. In the first aim, the investigators will develop an engineered polyketide synthase (PKS) system to produce threonine and complement the growth of an auxotrophic strain; they will use auxotrophy to evolve the PKS to be more productive. In the second aim, multiple PKS loading modules, in conjunction with the previously employed PKS extension module from Aim 1, will be utilized to create a range of ncAAs. To broaden the spectrum of producible ncAAs, a full reduction loop will be introduced into the last extension module of the PKSs; the fully reducing PKSs will also enable production of two more natural amino acids, and their auxotrophies will be used to evolve the fully reducing PKS. In the third aim, the investigators will introduce an orthogonal translation system into multiple ncAA producing B. subtilis strains and demonstrate the application of the platform to screen an amber codon mutant library of lipases for enhanced activity to depolymerize plastics. In the final aim, they will develop a containment system for the ncAA-producing B. subtilis and educate all members of the research team as well as the public about the safety, ethical, and security concerns around engineered biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award funds a research project that studies economic and environmental consequences of policies that apply stricter environmental regulation to larger firms; a type of environmental regulation commonly used in most industrialized countries to control air, water, and other types of pollution. The researchers will use the US Clean Air Act Amendments, which apply stricter regulation to firms that have predicted pollution emissions exceeding statutory thresholds for their analysis. The researchers will collect detailed data on plant design from construction permits and link them to measures of regulatory enforcement, firm revenues and costs data, and observed pollution emissions and use this data set to analyze how the Clean Air Act’s tighter regulations for larger firms affect patterns of pollution, regulatory enforcement, and output. The researchers will then develop an economic model of how this type of environmental regulation affects the environment and economy. The economic model will then be used to assess how alternative environmental policies, such as policies that apply the same regulatory rules to large and small firms, would affect the environment and the economy. The results of this research would provide inputs into the formulation of more efficient environmental regulation policies. This award funds a research project that studies the Clean Air Act’s “major source” designations, which specify that firms with maximum possible pollution emissions exceeding a threshold must comply with stricter regulations. The PIs will build a model that combines canonical approaches from the macro-labor and environmental economics literatures to analyze bunching of firm density around major source thresholds, and assess how enforcement and firm revenues, costs, and productivity vary around these thresholds. Using the model, the PIs derive closed-form analytical results describing the costs of being a major source and calibrate the model using firm permit data merged with several administrative data sets. The PIs then use the estimates to quantify the regulatory cost of being a major source and the environmental and economic impacts of counterfactual policies, such as a Pigouvian tax. 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

In a network, such as a road network or a social network, there is tension between the number of links it has and how “well-connected” it is --- in particular, it seems having very few links should make it hard to get from one place to another. However, there is a special type of network called an expander graph in which this intuition is misleading: there are very few (say, 3) links per node, but nonetheless, when taking a random walk on such a graph, the walker gets completely lost in very few steps, a process known as mixing. This surprising phenomenon has been very useful in computer science and is an essential ingredient in many fields, including error-correcting codes, cryptography, fault-tolerant computation, statistical mechanics, and more. This project addresses two kinds of mysteries that remain regarding expansion. First, we still don’t know the optimal tradeoff between the sparsity of a network and its rate of mixing, articulated in various ways. This tradeoff is important because it directly affects the performance of algorithms based on expanders. Second, our understanding of quantum generalizations of expansion is still in its infancy --- roughly speaking, these are notions of mixing that would be useful in simulating quantum statistical mechanical systems rather than classical ones, and we don’t understand mathematically when such mixing happens. This project will attempt to answer these questions, which will potentially lead to faster algorithms in coding theory and quantum computation. At a technical level, the project will pursue the following questions: (A) The existence of optimally expanding (nonbipartite) “Ramanujan” graphs--- which are expander graphs with second eigenvalue as small as possible --- employing insights from the geometry of polynomials and free probability theory. (B) The construction of a certain kind of optimal error-correcting code called an “epsilon-biased code” via a finer study of random walks on Ramanujan and related graphs, employing insights from mathematical physics on graphs. (C) Proofs of rapid mixing for “Quantum Markov Chains" converging to thermal states of quantum systems, generalizing what is known in the classical case. This work offers an opportunity to discover new mathematical connections between theoretical computer science and the aforementioned areas of mathematics. The investigator will organize an interdisciplinary program to cultivate these connections and bring together the people working in these areas. 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

Even with the extraordinary success of the the Standard Model of Particle Physics (SM), certain phenomena observed in the Universe that have profound consequences to life as we know it, such as the matter-antimatter asymmetry, the hierarchy problem, and the existence of dark matter and dark energy, remain unexplained. Therefore any physical description of such phenomena requires a theory that goes beyond the Standard Model (BSM), while at the same time encompassing the Standard Model and its predictions related to ordinary matter. A physical quantity potentially sensitive to the source responsible of the matter-antimatter asymmetry is the electric dipole moment (EDM) of particles such as the neutron and proton. The PI will study the impact of theories beyond the Standard Model to the matter-antimatter asymmetry in the universe by calculating the electric dipole moments of protons and neutrons induced by such theories. In addition to investigating the role played by theories beyond the Standard Model to the observed matter-antimatter asymmetry in the universe, which is one of the biggest unanswered questions in particle and nuclear physics, the PI will mentor a student engaged in this research. This project uses a new method, based on the so-called gradient flow, for the determination of the Quantum Chromodynamics (QCD) component of key BSM matrix elements related to quark and strong theta-CP violations. This set of matrix elements impacts the understanding of electric dipole moments (EDMs) within nucleons and nuclei (a key signature of BSM physics), and their determination will lay the foundation for extraction of BSM observables from future low-energy, high-intensity experimental measurements. The use of the gradient flow will circumvent some of the big challenges posed by the determination of the above-mentioned matrix elements by introducing a new scale, the flow time, that will mitigate divergences present in the calculations. Additionally the gradient flow is perfectly suited to be adopted to QCD calculations on the lattice. Lattice QCD is, as today, the only robust and theoretically sound approach to non-perturbative QCD calculations. The new method the PI has developed is ideally suited for calculating all the CP-violating contributions to the EDM of nucleons and light nuclei. Most of the tools and technique developed in this project are alternative to traditional methods and can be easily applied to other matrix element calculations contributing to the study of dark matter candidates. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

Project Summary/Abstract Vaccines composed solely of antigens are often poorly immunogenic; increasing intensity and length of the induced immune response is required to achieve desirable vaccine efficacy. Adjuvants (adjuvare, to help) are biomolecules used for >100 years to enhance human immune responses to vaccine antigens. Vaccines containing adjuvants are spectacularly successful, with billions of doses administered to save millions of lives each year. Despite this success, there is only a limited set of FDA-approved adjuvants (e.g., aluminum salts, oil- in-water emulsions, CpG oligonucleotides, and an extract [aka QS-21] from the Chilean soapbark tree [Quillaja saponaria]) owing to the intrinsic toxicity of new adjuvant candidates, difficulty to source and produce them, and their poor ability to induce long-term immunogenicity. Ongoing efforts to tailor adjuvant bioactivity are limited because their structure-activity relationships and mechanisms of action are not fully understood. Herein, we propose to address this challenge by engineering adjuvants through iterative diversification of their molecular structures (synthetic biology) and deduction of their molecular-level immunogenic mechanisms (systems-level immunology), an approach that will enable the rapid discovery of improved adjuvants. Recently, we engineered yeast (Saccharomyces cerevisiae) to produce QS-21 from simple sugars by upregulating native yeast pathways and heterologously expressing 38 proteins from six other organisms. We also have extensive experience in profiling (in vitro and in vivo) immunogenicity (innate and adaptive) of small molecules (e.g., adjuvants) associated with both bacterial and viral antigens. Together, we are uniquely positioned to redesign the microbial biosynthetic pathway for QS-21 to access its scalable production as well as that of its natural (i.e., QS-7) and new-to-nature (i.e., a “core pharmacophore”) analogs (Specific Aim 1). QS-21, QS-7, and a “core pharmacophore” will be starting points for a rapid pipeline that iteratively studies their mechanisms of action through systems- and molecular-level immunology studies (Specific Aim 2) and diversifies their molecular structures using combinatorial synthetic biology approaches (Specific Aim 3). Ultimately, this interdisciplinary and innovative approach will establish a framework to discover the structure-activity relationships that govern adjuvant immunogenicity and apply this knowledge to design and deploy best-in-class adjuvants that transform the prevention and treatment of disease.

NSF Awards · FY 2024 · 2024-07

The resurgence of deep neural networks has led to revolutionary success across almost all areas of engineering and science. Despite recent endeavors, current theoretical understandings of deep networks remain fragmented and only pertain to idealized and over-simplified network models. There is a significant lack of a systemic and unified approach for designing and explaining deep networks. Therefore, the underlying principles behind the success of deep learning still largely remain a mystery, which hinders its further development and adoption to broader applications. Nevertheless, the blessings of dimensionality imply that real-world data often reside in low-dimensional structures, and ample empirical evidence implies that there is a strong connection between deep learning and low-dimensional modeling. This connection implicitly appears in many different forms, in terms of learned representations, network architectures, and optimization strategies. However, these connections are far from being elucidated nor are they fully exploited. Based on the theory of data compression and optimal coding for learning from low-dimensional structures, this project aims to bridge the gap between the theory and practice of deep learning by developing a principled and unified mathematical framework. To develop this framework requires two steps. First, this project will design white-box deep networks by unrolled optimization schemes for maximizing the information gain of the resulting representation, which can be measured precisely by the coding rates of the representation. Second, the project will guarantee correctness through rigorous mathematical analysis of the optimization objective for learned representations. Third, this project will ensure consistency of the learned representations through a self-correcting closed-loop transcription framework that integrates encoding and decoding into a complete autonomous learning system. This new framework naturally unifies representation learning for all purposes: discriminative, generative, and auto-encoding, and is generalizable to all settings: supervised, unsupervised, self-supervised, and continuous learning. 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

Studying how the ocean and atmosphere have changed over Earth’s history helps us understand past climate and environment, as well as the development of life. Ancient pieces of ocean floor provide one way to study ancient ocean chemistry as the rocks in them have interacted with seawater. This project will investigate three pieces of ocean floor from a time between 750 million and 2 billion years ago, which is not well understood. Iron, strontium, and oxygen in these rocks will be analyzed to quantify past oxygen levels and the composition of seawater. Additionally, a detailed database of chemical data from ophiolites of all ages will be created for future research. This project supports a graduate student and offers internships to both international undergraduates and high school students from the Pasadena Unified School District. An interdisciplinary workshop will be held to share and synthesize state-of-the-art research and encourage future collaborations. In summary, this research will advance our knowledge of Earth’s early environment, with potential implications for predicting future environmental changes, and support the training of the future scientific workforce. Altered oceanic crust provides an archive to reconstruct the chemical evolution of the ocean and atmosphere over Earth’s history. During formation and cooling, oceanic crust is hydrothermally altered via low- and high-temperature interactions with seawater. These interactions modify the original composition of magmatic crust and provide a record of deep ocean seawater chemistry. Although these alteration processes are well-documented in the modern (through drilled oceanic crust) and Phanerozoic (through ophiolites, or preserved fragments of oceanic crust), the Precambrian record of oceanic chemistry is debated and poorly studied from the perspective of altered oceanic crust. This research will investigate hydrothermal alteration processes in three near-complete Proterozoic ophiolites via systematic sampling and analysis of samples from all stratigraphic levels. Specifically, bulk-rock Fe3+/ΣFe, 87Sr/86Sr, and 18O/16O will be measured to reconstruct past marine O2 concentrations, radiogenic Sr isotopic composition, and O isotopic composition. The sample suites will be archived and made available to future researchers to understand other aspects of Proterozoic marine chemistry. Further, a compilation of all previously published geochemical data on ophiolites (of all known ages) will be produced and made available to the scientific community. This research represents a critical step forward from the current state of limited data for Proterozoic ophiolites and a fragmentary data archive of ophiolite chemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT Most daily tasks demand cognitive control, but people vary in their motivation to meet the control demands required of those tasks. Motivational impairments are a common and transdiagnostic feature of a wide range of psychiatric and neurological disorders—including major depression, schizophrenia, and Alzheimer’s—severely compromising the daily functioning and overall wellbeing of individuals with these disorders. Unfortunately, little is known about the neurocomputational mechanisms that drive these impairments. We recently developed a computational model of how people make decisions about control allocation based on an evaluation of the costs and benefits (the Expected Value of Control [EVC] model). Our model points to several potential sources of motivational impairments and their putative neural substrates. These include deficits in learning about incentives, signaling those incentives when expected, and/or properly utilizing those incentives when making decisions about control allocation. The model suggests that dorsal anterior cingulate (dACC) is responsible for integrating incentive information in order to motivate the level of cognitive control that is most worthwhile. Our model further points to two dissociable components of the incentives for control: (1) the expected efficacy of control (the extent to which control is necessary to reach a particular goal) and (2) the expected reward for reaching that goal. Previous research has primarily focused on the latter component. It is therefore largely unknown how efficacy is learned and anticipated; how it is integrated with reward to guide control allocation; and to what extent motivational impairments are caused by deficits in the processing of efficacy. We have developed and validated a set of tasks that tease apart the independent influences of reward and efficacy on effort allocation. We will have adult participants perform these tasks while undergoing EEG or fMRI, to characterize the neurocomputational mechanisms by which expected reward and efficacy are (1) signaled, (2) utilized to determine effort allocation, (3) updated based on feedback, and (4) generalized to novel stimuli. We predict that dACC will integrate reward and efficacy information from separate frontoparietal inputs, to determine the amount and type of control that is most worthwhile. This control allocation will be enacted through dACC’s interactions with goal-specific prefrontal and subcortical regions. We also predict that reward- and efficacy-selective regions of frontostriatal and frontoparietal circuits will interact to guide learning and generalization of task incentives. We will test these predictions with model-based analyses of behavior and neural activity, using our EVC model to generate participant-specific estimates of incentive processing and control allocation across trials. This research will offer critical new insight into the computations and circuits underlying the motivation of cognitive control. It therefore has the potential to inform our understanding of the mechanisms of evaluation and motivation more generally, and to provide a path towards improving diagnosis and treatment for impairments that are both prevalent and transdiagnostic.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT: Metabolic dysfunction Associated Fatty Liver Disease (MAFLD) is the most common chronic liver disease, yet treatment options are limited. Within hepatocytes, the endoplasmic reticulum (ER) serves as a crucial hub for protein and lipid metabolism. Substantial evidence shows that dysfunction of hepatic ER, characterized by loss of its adaptive capacity, is a key mechanism involved in metabolic deterioration in MAFLD. However, the exact mechanisms underlying ER dysfunction in this condition are unknown. Recently, work from our lab has revealed a new angle: obesity leads to a marked loss of hepatic ER architectural organization, which significantly impacts its function. Moreover, recovering ER structure is sufficient to improve ER function and metabolic health in obese mice. Our long-term goal is to understand how alterations in ER architecture affect its function and the functionality of ER-interacting organelles; and how dysregulation of ER architecture leads to metabolic dysfunction in the liver. Our preliminary data show that fasting induces remodeling of hepatic rough ER sheets, which forms a curved membrane around the mitochondria, a structure regulated by the protein Ribosome Receptor Binding Protein (RRBP1). Obesity leads to loss of this response mainly due to downregulation of RRBP1. Moreover, RRBP1 gain of function reduces hepatic steatosis in obese mice. Based on these data, our central hypothesis is that RRBP1- driven rough ER sheet-mitochondria interaction in fasting allows the mitochondria to regulate key adaptive processes such as fatty acid oxidation. Lack of this response in obesity leads to metabolic dysfunction. We will test our hypothesis by: 1) Delineating the requirement of RRBP1-driven rough ER sheet-mitochondria interaction for hepatocyte adaptation to nutritional challenges; 2) Unraveling the mechanisms through which rough ER sheet-mitochondria interactions regulate organelle function and 3) Determining the importance of ER architectural remodeling for the development of diet-induced fatty liver disease. Under the first aim, we will thoroughly characterize the impact of liver specific RRBP1 loss and gain of function on liver metabolism in mice. In the second aim, we will test the hypothesis that rough ER-mitochondria proximity allows mitochondria to adapt to fasting. In the third aim, we will use FIB-SEM imaging and machine learning-based organelle segmentation to determine the impact of diet-induced fatty liver disease on ER structural remodeling in mice and in humans. We will also test if targeting ER structure by overexpression of RRBP1 can improve metabolic stress in MAFLD. This work opens a novel dimension in the field by unraveling how organelles reprogram their metabolic output and optimize their function by reorganizing their subcellular architecture in response to nutritional challenges. It also has the potential to unravel new therapeutic targets to treat fatty liver disease.

NIH Research Projects · FY 2026 · 2024-07

Project Summary Over the past two decades, single-molecule assays (SIMOA) have transformed the detection landscape for low- abundance protein biomarkers, such as cytokines and neurological markers, across a vast dynamic range. Sourced from a variety of samples, these assays provide exceptional capabilities that enhance our knowledge of diseases and fine-tune diagnostic methodologies. While various platforms have been developed and commercialized, they invariably involve intricate workflows and depend on highly specialized, bulky equipment. Such complexities confine the use of this technology to centralized labs, necessitating controlled environments and skilled operators. To meet these challenges, we introduce a compact SIMOA system that seamlessly integrates electronics and sub-µm sized microfluidics within a single integrated circuit (IC) chip, produced by semiconductor foundries. The creation of the microfluidics leverages a unique single-step wet-etching process, which facilitates the efficient integration of thousands of fluidic and electronic channels onto a compact, millimeter-sized CMOS chip. Drawing from the principles of flow-cytometry-based and nanopore-centric SIMOA platforms, our system electronically counts individual sandwiched immunocomplexes as they pass through the on-chip 500-nm pores. Notably, the IC itself stands as the main instrument, enabling results to be directly viewed on a personal device, eliminating the need for a reader. The project objective will be achieved by four specific aims: (1) Aim I will focus on implementing the CMOS- embedded microfluidics, the sensing on-chip pores, integrated on-chip electrodes, and the magnetic separation module. (2) Aim II will focus on developing resistive-pulse-sensing low-noise readout circuits and signal processing units for digitization and identification of the binding events. The integrated microfluidics/electronics system will be packaged, tested, and validated. (3) Aim III will focus on the functionalization of nanoparticles using target-specific antibodies. We will target three biomarkers, ɑ-synuclein, NfL, and GFAP, that are related to neurodegenerative diseases. (4) Aim IV will optimize the workflow and validate the assay by benchmarking with the established commercial SIMOAs. This project holds significant promise; its successful completion could transform future diagnostics for many diseases that require detecting low-abundant biomarkers, particularly benefiting settings with limited resources.

NIH Research Projects · FY 2024 · 2024-07

Summary To measure the detailed function of neural networks in vivo, 2-photon population calcium imaging is widely used, but has important limitations including low temporal resolution and poor single-spike detection. These limit its ability to measure physiologically relevant activity patterns, particularly in cerebral cortex. A powerful alternative is voltage imaging with genetically encoded voltage indicators (GEVIs). Modern GEVIs have high sensitivity and reduced bleaching, detect spikes with 1-2 ms time resolution, work under 2-photon (2p) conditions, and report both subthreshold and spike signals. In this project, we optimize in vivo 2p imaging methods for two modern GEVIs in somatosensory cortex (S1) of awake, behaving mice. We use conventional 2p resonance-galvo imaging in small fields, and free-space angular chirp-enhanced delay (FACED) 2p imaging in larger fields (>100 neurons), both of which detect spikes at 1-2 ms resolution. Aim 1 optimizes these methods for pyramidal cells and major interneuron types, and quantitatively calibrates optical spike detection. Aim 2 applies these methods to study the dynamics and interactions of sensory and cognitive signals in specific cell types in S1. Layer 2/3 pyramidal neurons mix rapid whisker touch signals (<10 ms resolution) with slower cognitive signals (e.g., for decision or expectation). We will measure rapid sensory-evoked spike and subthreshold dynamics in two functionally distinct, spatially intermixed pyramidal cell classes, in order to test whether these represent distinct networks. We also study a cognitive signal, the response to unexpected deviant (oddball) sensory stimuli, which is thought to be a long-latency, top-down signal. We study how this cognitive signal interacts with rapid sensory signals in pyramidal networks, and how it recruits inhibitory interneurons. This long-latency deviant response corresponds to the mismatch negativity (MMN) signal, which is a widely used EEG biomarker for schizophrenia, and our results may reveal novel circuit mechanisms for this signal. This project brings together the Feldman lab, with expertise in neural coding and circuit function in S1 cortex, and the Ji lab, with expertise in in vivo 2-photon imaging method development and GEVI imaging. This early-stage project is based on an ongoing collaboration in which we have developed reliable methods for 2-photon voltage imaging of whisker-evoked activity in S1 in vivo, using ASAP4.6-Kv, a high-sensitivity GEVI developed by Michael Lin. Overall, this project will establish optimized methods for 2p GEVI imaging in vivo, and use them to probe first-level questions about sensory and cognitive dynamics in S1 networks. In the long run, 2p GEVI imaging promises to reveal network activity at millisecond time scales, revolutionizing our understanding of cortical circuits in health and disease.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Early puberty is associated with adverse health outcomes over the life course, including psychopathology in adolescence, and reproductive cancers, diabetes, and cardiovascular disease in adulthood. The age of pubertal onset has declined dramatically over the past 40 years in the United States (US), with alarming racial/ethnic disparities. These disparities may amplify future health inequities in chronic conditions, yet remain poorly understood. During the COVID-19 pandemic, pediatric endocrinology centers across twelve countries reported large increases in the incidence of central precocious puberty (CPP), a rare condition characterized by developing secondary sexual characteristics before age eight in girls and nine in boys. However, existing studies are subject to limitations such as small samples from specialty care settings, insufficient power to examine trends in boys, and reliance on diagnostic criteria for CPP. In addition to CPP, it is important to examine normative timing of early pubertal milestones, including onset of pubic hair and breasts/testes development, because they represent the earliest observable markers of underlying hormones and may play differential roles in the etiology of health outcomes. Moreover, no studies have investigated whether the pandemic exacerbated pre-existing racial/ethnic or neighborhood-level disparities in pubertal timing. To fill gaps in current knowledge, this study will leverage electronic health records from Kaiser Permanente Northern California (KPNC) to conduct the first population-based study on the pandemic and pubertal timing in the US. KPNC comprises ~32% of the northern California population and has 4.4 million members. In 2010, KPNC began systematically documenting routine pubertal development assessments for all children aged 6 years and older, thereby facilitating the study of trends in both CPP incidence and normative pubertal timing in a population with considerable racial/ethnic, socioeconomic, and geographic diversity. First, we will estimate pre–post pandemic changes in incident CPP diagnoses at KPNC medical centers using an interrupted time series design, using data from 2017–2023 (Aim 1). Second, we will use survival analysis techniques to estimate pre–post pandemic changes in the timing of normative pubertal milestones (including onset of pubic hair development, breast/testes development, and menses) in a representative population cohort of approximately 103,000 boys and 72,000 girls (Aim 2). Finally, we will examine the differential impact of the pandemic on CPP and normative pubertal timing across diverse racial/ethnic groups and neighborhood conditions (Aim 3). Study strengths and innovations include the use of a robust quasi-experimental design, longitudinal assessment of a large and representative population of boys and girls, and investigation of several important hallmarks of puberty. Examination of racial/ethnic and place-based disparities will guide the design of upstream health equity interventions and inform both clinical practice and future pandemic response. This study also provides a foundation for future research to determine whether earlier puberty has lasting health consequences for today’s children as they transition to adulthood.

NSF Awards · FY 2024 · 2024-07

As the Earth orbits the Sun in our vast solar system, it is immersed in the solar wind plasma, a fast flow of charged particles continuously streaming away from the Sun. The solar wind compresses the Earth’s dipole magnetic field on the dayside and pulls it into a magnetic tail on the nightside, just like the tail of a comet. In this long and stretched magnetic tail, an explosive process called magnetic reconnection is prevalent. This process converts the magnetic energy in the stretched magnetic tail into particle jets and heating of the ambient plasma. Sometimes the heating is low, and sometimes it is very high. In this project, hundreds of reconnection jets in the Earth’s magnetotail will be studied using in-situ spacecraft observations to find out what controls the degree of the plasma heating. The results will contribute to understanding how particles are energized and the plasma is heated during magnetic reconnection in other astrophysical and laboratory environments, and will help to understand and predict the drivers of the space weather on Earth. Magnetic reconnection is a universal plasma process which converts stored magnetic energy into particle energies. One of the key unresolved questions in reconnection research is what controls the degree of ion heating in reconnection. Past observational studies of ion and electron heating scaling in reconnection have focused on the low heating regimes of the magnetopause and the solar wind. These studies have found that both ion and electron heating in reconnection are linearly proportional to the available magnetic energy per particle. However, independent observational and theoretical studies have suggested that the linear dependence of ion heating may not be universal. To test the universality of the linear scaling of ion heating, observations of reconnection in the high heating regime are required. The Earth’s magnetotail provides a natural laboratory in the high heating regime where in-situ spacecraft can observe reconnection in action. This project will conduct a comprehensive study using spacecraft observations of reconnection in the Earth’s magnetotail to establish the characteristics and controlling factors of ion heating. 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

Imaging the Earth’s deep interior at a greater resolution than currently achievable will improve our understanding of the nature of deep-Earth structures, including those that lead to the formation of volcanic islands such as Iceland and Hawaii. A powerful way to probe the interior is to measure the energy from earthquake waves as they travel through the complex Earth. Available seismic methods do not have sufficient resolution to describe these features to understand their origin and nature. Improving resolution requires the development of efficient and accurate methods for the computation of how seismic energy travels through these features, to be compared to what is observed at seismograms across the globe. The project team proposes a new algorithm, “Distributional Finite Difference Method” (DFDM) which presents advantages relative to the most efficient codes presently available by allowing for flexible model geometry without degrading the accuracy of the computation. This approach also shows potential for increased computational speed, as this type of work is computationally heavy and requires access to high performance computing facilities. This project will develop a computer code based on DFDM, compare results with those from available methods, and illustrate its use for modeling regions of extreme physical properties at the base of the mantle. The code will be made available to the community through the Computational Infrastructure for Geodynamics (CIG) portal as a contribution to geophysical infrastructure while supporting the education and professional development of graduate students, postdoctoral scholars, and early career scientists. As higher spatial resolution is sought in global seismic imaging to investigate the nature of small scale objects, the complexity of meshing, accuracy of the wavefield, and soaring computational time at high frequencies represent significant challenges, justifying continued efforts to design the next generation of numerical wave propagation codes. Flexible methods may take advantage of variable resolution requirements for different locations within a 3D model. This approach keeps the wavefield computation accurate and maintains competitive computational costs in comparison to codes such as the SPECFEM suite, which is based on the spectral element method (SEM). The newly developed versatile Distributional Finite Difference Method (DFDM) shows promise for application in global seismology, with some potential advantages compared to the SEM. DFDM combines the simple structure of the pseudo-spectral/finite-difference methods (FD) with accurate treatment of boundary conditions including free surfaces, as in spectral element-based techniques. This method also accurately accounts for material discontinuities and non-conformal interfaces, via an element-wise domain decomposition, where elements can be arbitrary large, depending on the medium’s geometry. In DFDM, the accuracy is independent of the size of the elements, such that elements can be large in regions where the model is smooth. In addition to its flexibility and accuracy, preliminary benchmarks of the algorithm prior to parallelization and optimization show that the DFDM is computationally faster for the same accuracy when compared to SEM in complex 3D models. DFDM therefore represents an attractive alternative to SEM-based approaches once parallelized and optimized. This team will implement, parallelize, and optimize a novel seismic wave propagation solver, based on this novel algorithm to combine advantages of the spectral element method widely used in global seismology, and of the Finite Difference method. The DFDM shows promise for application in global seismology and for modeling of fine scale structure in the deep Earth. The project will benchmark the resulting code against the highly optimized SPECFEM3D_GLOBE and provide an illustration of application to a complex ultra-low velocity zone (ULVZ) model at the core-mantle boundary. The final product will be a platform-independent, efficient package (“app”) for distribution to the seismology community through the Computational Infrastructure for Geodynamics (CIG) portal. 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

Memory is a critical aspect of many of our behaviors. We use memory to find our way around, to detect a familiar face in a crowd, and to keep track of our ideas as we speak and write. One powerful way that memory can affect so many of our behaviors is by helping to guide what we pay attention to. For example, in a messy kitchen, you can use your memory for where mugs are typically stored in order to find one. Therefore, although memory is often studied for its own sake - for example, to understand how we are able to reminisce about the past, or how that process can go wrong - it is also critically important to understand how we can use memory in the service of guiding our attention and actions. The goal of this project is to understand how the functioning of the human brain enables us to use memories of the past to direct our attention, and the consequences that has for how quickly and accurately we can accomplish tasks. In doing so, this work will highlight the critical importance of memory for moment-to-moment attention. This will fill an important gap in scientific research, which often studies attention and memory in isolation. It will highlight the fundamentally interactive nature of our past and current experiences, with implications for how learning and remembering in educational settings can affect attention and future learning in a feedback loop. This project therefore seeks to determine the neural mechanisms by which memories guide attention, focusing on the memories stored in a key brain region that is critical for building new memories and retrieving old ones: the hippocampus. This will be accomplished in two Aims, which use multiple methods: functional magnetic resonance imaging, studies of patients with brain lesions, eye tracking, and measures of behavioral accuracy and response times. In Aim 1, the project will determine the neural circuits for memory-guided attention and their relationship to behavior. The main hypothesis is that a brain network including the hippocampus and prefrontal and visual cortices allows us to use memory to update attentional goals and anticipate task-relevant information before it appears. This hypothesis will be tested using a novel approach of characterizing interactions between brain regions (representational connectivity), which enables investigation of synchrony in information content between regions. In Aim 2, this project will establish how memory and attention jointly guide visual exploration. The main hypothesis is that hippocampal memory retrieval of prior attentional goals will influence visual exploration, attention, and memory in novel situations. This work will have innovative implications for education, e.g., the use of eye tracking to identify if students are remembering and attending to relevant information, even if they cannot verbally describe it. Together, these two Aims will start to uncover the powerful way that memories can influence our in-the-moment attentional behaviors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT Diabetes is a leading cause of mortality and morbidity in the United States. Gestational diabetes mellitus (GDM), which is diabetes that is first diagnosed and recognized during pregnancy, is a strong risk factor for future development of type 2 diabetes. In the US, a two-step diagnostic process is used to diagnose GDM: a screening test (step 1), followed by a diagnostic test (step 2), which is offered to the subset of women that fail the screening test. Only those that fail the diagnostic test are diagnosed with GDM. According to recent evidence, women that fail the screening test and have normal diagnostic test results have an intermediate level of gestational glucose intolerance (GGI), which is also a risk factor for diabetes. There are vast racial/ethnic disparities in diabetes, GDM, and GGI. It is unknown if future diabetes risk after GDM and GGI differs according to racial/ethnic groups and disaggregated subgroups (among Asian and Hispanic subgroups). Social determinants of health, which are environments where people are born, live, learn, work, play, and age, are key to addressing these disparities. Neighborhoods are considered a key social determinant. Economic disadvantages tend to cluster at the neighborhood level to cause inequitable opportunities for health improvement, adversely affecting communities of color. It is unknown if neighborhood deprivation affects risk of future diabetes after GDM or GGI. There are interventions that exist to lower future diabetes risk after GDM including screening, lifestyle, nutritional, and pharmaceutical interventions. However, there are barriers to women accessing these interventions including lack of social support and lack of frequent interactions with clinicians. Community midwifery, with its unique comprehensive perinatal care model, including up to six post- partum visits, presents an opportunity to address these barriers among individuals with GDM. Currently, there is no research assessing how community midwives support women with GDM to prevent future diabetes. Thus, the main goal of my study is to identify opportunities to address racial/ethnic disparities in diabetes after GDM and GGI. The specific aims are to: Aim 1) Ascertain risk of future diabetes after GDM and GGI overall, and by racial/ethnic groups and disaggregated Asian and Hispanic subgroups; Aim 2) Investigate if risk of future diabetes after GDM and GGI is modified by levels of neighborhood level deprivation, overall, and by racial/ethnic groups and disaggregated subgroups; and Aim 3) Assess how community midwifery care supports individuals with GDM during the perinatal period to lower their future diabetes risk, especially among historically minoritized racial/ethnic subgroups. Findings from this research may identify racial/ethnic groups and subgroups at high risk for developing diabetes after GDM and GGI and inform culturally tailored, targeted, neighborhood and clinical perinatal interventions to lower future diabetes risk.

NSF Awards · FY 2024 · 2024-07

Science advances because scientists collect data, develop methods, and generate theories that become part of a shared scientific record. To be part of this shared record, scientific works go through peer review by other scientists. Although peer review is intended to promote rigorous standards, it also has consequences for the scientific workforce - for who wants to stay and who is able to stay, in research-focused careers. Despite peer review’s place as a core scientific practice, learning how to engage with peer review is not explicitly taught. Few people receive training or oversight to ensure that reviewers provide feedback that is helpful, professional, and culturally sensitive (i.e., delivered in a way that does not marginalize underrepresented minority scholars). Graduate students’ experiences with peer review can influence whether they decide to stay in the STEM pathway. This project examines peer review with an eye to equity (are outcomes and processes equitable across groups), inclusion (does peer review offer experiences of fit and belonging across groups), and diversity (does peer review contribute to increasing the range of identities and experiences constituting the field). This NSF Innovations of Graduate Education (IGE) award to Indiana University, Columbia University, and California State San Bernardino seeks to foster diversity, equity, and inclusion within science by improving peer review culture and graduate students’ ability to navigate peer review. This project supports the innovative structure and goals of Reviewer Zero, a coalition of faculty and graduate students in psychology and neuroscience working to understand and intervene to increase equity in peer review processes. Reviewer Zero envisions a “reset” of peer review culture in which reviews serve a formative rather than gatekeeping function. This project will develop strategic programming with two audiences: the historically underrepresented graduate students most directly affected by inequitable systems of peer review, and the reviewers/editors who occupy positions of power in making peer review decisions. The project will design, deliver, and assess interventions that build awareness, knowledge, and support for each audience. Specifically, the project asks how targeting different aspects of the culture cycle can best shift peer review culture toward greater equity. By re-imagining ideas about what peer review is, the evidence-based training will engage individuals with new tools and supports, whether they are trainees or reviewers. A new paper development system (Formative And Interactive Review) will provide a novel institutional structure for fundamentally different interactions between reviewers and trainees. Outreach and partnerships with existing institutions (journals, societies) will lead to the dissemination of new views of the goals and processes of peer review. Towards these goals, this project will implement a comprehensive strategy to increase diversity, equity, and inclusion in the peer review process by (a) working with reviewers/editors to shift culture and (b) providing direct support and training to graduate students navigating peer review. By studying how engaging with program activities affects trainee or reviewer/editor knowledge, skills, and abilities, the project will contribute to understanding how shifts in culture cycles occur. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This CiviL Infrastructure research for climate change Mitigation and Adaptation (CLIMA) project supports research on housing infrastructure adaptation in coastal communities exposed to natural hazards in a changing climate. This project develops a novel mathematical framework to analyze interactions between collective human behavior, built environments, and natural hazards that can speed up retrofit and repair of homes or lead to more out-migration after disasters. It helps at-risk communities identify pathways to become more resilient. In addition, the project offers opportunities for student training in interdisciplinary methods in engineering and social science and aims for broad dissemination of results to engineers, scientists, and policymakers. This project puts forward convergent research that draws methods from civil engineering, urban and network science, and social science to hone an interpretable and scalable mathematical framework. The project uses a network perspective to study human-infrastructure interactions and enhance the understanding of collective human behavior in climate adaptation in three main thrusts. First, the project builds on fieldwork and migration theory to develop a probabilistic dynamic model for single-household actions, including structural (home retrofits and repairs) and non-structural interventions (people’s resettlement). Second, the project studies emergent collective behavior through statistically principled approaches, field research, and refined and extended disaster datasets. Third, the project extends the probabilistic formulation from single-household adaptability to community adaptability towards improved estimation of community resilience to coastal hazards. Overall, research completed in association with this project distills sociotechnical insights into using interpretable and scalable methods to illuminate pathways for community adaptation to climate change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

Project Summary/Abstract Almost every cell in a human being has the same DNA sequence yet different cells vary widely in which genes are expressed. In addition to the genetic code, human cells contain chemical modifications on DNA called epigenetics that can dictate cell type identity and cell fate. The most abundant epigenetic modification in the human genome is DNA methylation, which is critical for silencing transposable elements, genomic imprinting, and X chromosome inactivation. Aberrant DNA methylation patterns is a hallmark of aging and cancer cells, however the genetic pathways that contribute to DNA methylation remains unclear. My research laboratory aims to unlock how DNA methylation is established in the human genome and to understand how defects in DNA methylation pathways leads to disease. Using genome-wide genetic screens, my lab has recently discovered genes that previously have not been implicated in DNA methylation, including an RNA binding protein and the proteasomal complex that degrades proteins. In the next five years, we will dissect the roles of our newly discovered genetic factors of DNA methylation and how they regulate gene expression programs in cancer and embryonic stem cells using multidisciplinary approaches including biochemistry, genetics, and advances in DNA sequencing technologies. In addition to our goals to understand DNA methylation pathways, my laboratory is a leader in pioneering new CRISPR methods to write and erase DNA methylation at any site in the genome. We use lessons learned from our basic biology findings to engineer tools that allow us to turn off/on human genes solely by changing the epigenetics of a gene instead of inducing harmful DNA breaks. In the next five years, we will innovate next generation CRISPR epigenetic editing technologies that enables fine tuning of transcription at defined levels. To overcome the current challenge of delivering CRISPR technologies into primary human cells, we will establish the first protein-based delivery of CRISPR epigenetic editing technologies into primary immune cells for use in cancer immunotherapy applications. Our CRISPR epigenetic editing technologies and delivery platforms will have broad use in the biomedical sciences and as safe and robust tools for cell and gene therapy clinical applications purely by modifying epigenetics instead of genome editing.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT The Chemical Biology Training Program (CBTP) is a new interdisciplinary predoctoral training program at the University of California at Berkeley. The CBTP will be the only training program at UC Berkeley focusing on chemistry-driven approaches to biomedical research. The CBTP will recruit and appoint 12 trainees per year to train in the field of chemical biology, drawn from a pool of 1378 applicants and 117 matriculants per year in the Chemistry and Molecular and Cell Biology PhD programs. Trainees will be appointed for one-year terms and will continue to engage with the CBTP training activities and career-building events through to graduation. We expect that 100% of our students will graduate with a PhD within 6 years, with a target mean time to degree of 5.5 years. Our goal is that every student will publish a first or co-first author paper in a peer-reviewed journal recognized as excellent in the field of chemical biology, and will subsequently pursue a research-related career in academia, industry, or government. Professional development will be centered on the acquisition of six core competences designed to enable leadership in any of these workforce sectors. Training procedures will be informed by the latest scientific literature in the field of research mentorship and training, and will emphasize the acquisition of self-efficacy. Research training will be enhanced in response to the latest advances in chemical biology, including the rapidly growing importance of translational chemical biology to drug development. A series of training innovations have been incorporated into the CBTP, including the establishment of academic-industrial partnerships, the creation of a new academic unit focused on Molecular Therapeutics, and a structured mentoring environment with student peer mentoring groups and secondary faculty advocates. The effectiveness of the training program will be rigorously evaluated on an annual basis and advice sought from newly-constituted student and external advisory groups. Our 45 CBTP training faculty are drawn from 9 different departments at UC Berkeley, and our trainees will be drawn from the Departments of Chemistry and Molecular and Cell Biology PhD programs. Our faculty are world leaders in their fields and include 10 members of the National Academy of Sciences and recipients of the Nobel and Wolf Prizes in Chemistry. All CBTP faculty undergo formal mentorship training and evaluation of mentoring quality.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY DNA methylation is important for gene regulation, transcriptional silencing of repetitive DNA and establishing genomic imprinting. While DNA methylation is a dynamic modification, which is added and removed by writer and eraser enzymes, it is faithfully inherited over many millions of cell divisions, and even evolutionary timescales. How these writers and erasers combine to ensure such accurate epigenetic inheritance is a critical question, as failure to accurately maintain DNA methylation patterns is associated with aging as well as numerous diseases, including cancers. Despite the importance of DNA methylation writers and erasers, the mechanisms that regulate and coordinate their genes to maintain epigenetic homeostasis remain poorly understood. A major goal of the Williams lab is to eliminate this gap in knowledge. Using the epigenetic model system Arabidopsis, which can tolerate loss-of-function mutations to all methylation writers and erasers, my lab will perform a comprehensive study of the gene regulatory mechanisms that regulate the expression of writer and eraser genes to ensure epigenetic homeostasis. This will include performing a mechanistic dissection of the cell-cycle regulation of genes encoding writer and eraser enzymes and their targeting by anciently conserved cell cycle transcription factors. Additionally, my lab seeks to identify new trans-acting factors involved in the regulation of epigenetic homeostasis by studying a naturally occurring strain of Arabidopsis with drastically different regulation of epigenetic modifiers. Lastly, my lab will precisely define how epigenetic homeostasis is lost within some cells during aging, identifying mechanisms that contribute to age-dependent DNA methylation losses, and determining how “epigenetic age” is influenced by organ regeneration and the environment. Together, these approaches will generate multiple insights into how DNA methylation dynamics are established at a cellular scale and coordinated to achieve epigenetic homeostasis. We anticipate that these findings will provide new insights into the laws of epigenetic stability and inheritance, which are crucial for understanding many aspects of the health of human cells.

NIH Research Projects · FY 2024 · 2024-06

Project Summary This goal of this R21 project is to jumpstart mechanistic research on the role in neuropathic pain of SCN1B, an understudied druggable protein. Neuropathic pain is a debilitating, chronic condition that represents a major medical burden in the US. Current standards of care are ineffective both for preventing pain chronicity after nerve injury, and for providing long-term relief for neuropathic pain. Individuals show different susceptibilities to neuropathic pain and experience diverse sensory symptoms; however, little is known about the genetic factors that predispose individuals to chronic sensory dysfunction. Thus, there is a need to understand the mechanisms that that underlie neuropathic pain, because such molecules are potential therapeutic targets and biomarkers of susceptibility. In a screen for natural genetic variation among genetically distinct mouse strains, we identified Scn1b as a top candidate susceptibility gene for tactile hypersensitivity in a mouse model of paclitaxel-induced neuropathic pain (PIPN). The Scn1b gene encodes the auxiliary β1 subunit of voltage-gated Na+ channels, which are key regulators of neuronal excitability because they mediate the upstroke of the action potential. Scn1b expression is highly enriched in myelinated mechanosensory neurons that provide sensory drive for tactile allodynia in persistent pain. Based on these results, this project’s central hypothesis is that SCN1B plays a key role in the development of tactile allodynia in mouse models of neuropathic pain by governing the excitability of mechanosensory neurons. This hypothesis will be tested with a combination of sensory neuron-specific Scn1b knockout mice, electrophysiological studies and a battery of behavioral tests for somatosensory and motor behaviors. Aims are to: 1) determine whether Scn1b is a susceptibility factor for neuropathic pain in a mouse CIPN model; and 2) determine whether Scn1b is required for the development of neuropathic pain in a spared- nerve injury model. If successful, this research will generate essential pilot data that will rapidly catalyze future research to elucidate mechanisms through which Scn1b promote neuropathic pain, develop SCN1B as a new therapeutic target, and validate Scn1b as a biomarker for neuropathic-pain susceptibility.

NSF Awards · FY 2024 · 2024-06

Python and R are the predominant open platforms for computation in academia and industry, driving innovation in data science and AI. R is largely developed by statisticians, while scientific Python is mostly built by researchers from the applied sciences. As a consequence, Python's statistical capabilities lack cutting edge methods and techniques, and statisticians do not see their algorithmic innovations disseminated widely on this popular platform. This scoping project explores sustainable and effective pathways for establishing an open-source ecosystem which would catalyze the development of a robust set of statistical software for Python. The effort will also build a vibrant ecosystem of statisticians, domain practitioners, and software developers around the open platforms. The team aims to establish better software engineering practices in the statistical community and to provide onboarding pathways for young researchers, while documenting and implementing healthy and inclusive community practices that can be replicated in other communities. To anchor the effort, this effort focuses on two pilot projects (R and Python) with different scopes, target audiences, and levels of maturity, and determines how they should be modified to comply with modern software engineering and community governance best practices. YAGLM is an open-source Python package that makes modern generalized linear models (GLMs) easily accessible to data scientists. GLMs are flexible and powerful generalizations of ordinary linear regressions that cover many statistical models widely used in applications. The ISLP open-source Python package accompanies the new introductory text on statistical learning and Python ("An Introduction to Statistical Learning: with Applications in Python"). Through detailed code and governance audits of these pilot project, as well as feedback from the statistical community, the team will document the need for innovation within the current technological landscape; outline how to identify potential contributors and users; specify the necessary infrastructure, organization, and governance; and explore mechanisms for long-term sustainability. 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.