University Of Illinois At Urbana-Champaign

NIH Research Projects · FY 2024 · 2024-08

R13 Application, PI: BLANKE, Steven R. SUMMARY Protein toxins and effectors are important virulence factors for many pathogenic bacteria, contributing to disease progression by modulating or disrupting essential functions in target cells. Historically, research has focused on several toxins associated with important human diseases, which in some cases provided the framework for eradicating the disease associated with the toxin. However, recent advances in microbial genomics and discovery-based screening techniques have dramatically increased the number of toxins and effector systems under investigation. Advances in high-resolution microscopy, structural biology, cryo-electron microscopy, proteomics, and CRISPR have provided new tools to study the structure and function of toxins and effectors, as well as the mechanisms by which they contribute to interactions and modulation of target cells. This R13 application derives, in part, from the realization that, although the field of protein toxins and effectors is rapidly expanding, important activities, such as the dissemination of research findings and establishment of interdisciplinary collaborations, have been challenging because of a relative dearth of scientific conferences/meetings in North America specifically centered on protein toxin/effector structure/function and mechanism. To address this gap, we conceived, designed and launched several years ago an "in-person-virtual" hybrid conference, the "Lakeside Conference on Protein Toxins and Effectors" (#LakesidePTE), in October 2020, and which was followed up with a hybrid in person-online meeting in October 2022. Based on the excitement and positive feedback resulting from these two meetings, we now propose to follow up with the 2024 Lakeside Conference on Protein Toxins and Effectors October 6-9, at the Abbey Conference Center located on Lake Geneva WI. The 2024 hybrid conference will concentrate on the cellular, structural, and biochemical mechanisms of protein toxin and effector action, along with exploring the translational potential of these proteins for treating human diseases. The conference will be organized by Chair Dr. Steven Blanke at University of Illinois at Urbana-Champaign and Vice Chair Dr. Min Dong from Harvard Medical School. A six-member scientific advisory board will assist in selection of speakers and abstract review. There will be 5 sessions and a keynote address Ten additional invited speakers will anchor five scientific sessions that will be filled out with talks selected from abstracts. Here we are seeking NIAID funds 1) to support registration fees and travel for keynote and invited speakers, (2) to offer registration scholarship awards for underrepresented minority post-docs and graduate students and (3) for audiovisual support for a hybrid meeting that will be simultaneously live streamed. This support from NIAID will bring together microbiologists, biochemists, structural biologists, and biophysicists to encourage new collaborations across disease and phylogenetic boundaries and test an innovative model for the future of this conference as a hybrid conference that will reach a global audience.

NIH Research Projects · FY 2026 · 2024-07

Sleep and physical activity/sedentary behavior are physiological and behavioral processes that are intricately intertwined. Their Interconnectedness is particularly pronounced during early infancy when these systems are rapidly developing in concert with neurobiological changes. Yet, sleep and physical activity/sedentary behavior are often studied in isolation and with little attention to the home environment in which they occur. Further, current state-of-the-art methods, including wearables and mobile sensing devices that automate assessment of sleep, physical activity, and sedentary behavior, have been developed and validated predominantly with adults, adolescents, and school-age children. These adult-based methods, however, do not translate to infant populations and the unique challenges posed by this development period. With these issues in mind, our overarching aim is to advance methodological tools that provide valid, automated, objective, and fine-grained assessments of infant health behaviors in real-world environments. In doing so, we will use LittleBeats, an infant multimodal wearable device engineered by our team, that integrates a microphone to collect audio data, a 3- lead electrocardiogram (ECG) to assess infant cardiac physiology, and an inertial measurement unit (IMU) sensor to assess infant motion and posture. LittleBeats can be worn by infants for extended periods of time (8- 10 hours) in their natural environments without researchers present. We will leverage high-density data from this infant wearable to address three specific aims. First, we will develop and validate multimodal deep learning (DL) algorithms that use audio, ECG, and motion data as input to detect infant sleep/wake states, including quiet sleep, active sleep, drowsy, quiet alert, active alert, and crying states. Second, we will develop and validate DL algorithms that use ECG and motion data as input to detect infant physical activity (i.e., tummy time) and sedentary time (e.g., time restrained in a car seat). Third, because environmental noise, including loudness, variability, and number of sound sources have been associated with negative physiological, behavioral, and cognitive outcomes during the first year of life, we will leverage audio data from the LittleBeats device worn by the infant to detect noises in the home environment. Our development and validation will occur across two samples of infants under six months of age. DL algorithms will be validated against (a) annotations by trained and certified human coders, (b) ecological momentary assessments provided by infants’ primary caregivers, and (c) polysomnography, the gold-standard for sleep. By bringing together assessments of infant sleep, physical activity, sedentary behavior, and household noise under a single platform, we aim to advance research capacity to investigate the interdependencies and transactions among these core infant health behaviors and the environments in which they occur. Ultimately, such tools may aid in early detection, monitoring, and intervention among infants at risk for sleep disturbances, obesity, and other poor health outcomes.

NSF Awards · FY 2024 · 2024-07

This project aims to establish millimeter wave (mmWave) radar as a first-class perception and control tool for small-sized robots and drones. Small-sized robots and drones are key enablers for many emerging applications – precision agriculture, inventory management in smart warehouses, drone-based delivery of goods, and search & rescue operations. Perception and control of such robots and drones is fundamentally challenged by their over-reliance on optical sensors, low power budgets, and limited computational capabilities. Optical sensors such as cameras and lidars cannot see through smoke, dust, and fog and miss transparent surfaces like glass. In contrast, mmWave radar sensors can see through smoke, dust, and dark conditions, and can detect glass and low-texture surfaces. Due to their small wavelengths, these sensors can be packaged in small form factor devices and are being increasingly commoditized, with each sensor costing less than a hundred dollars. Despite these advantages, mmWave radars have seen limited use in robotic perception and control. This project aims to build new perception and control pipelines for radar sensors that are performant on the small onboard compute of robots and accessible to the robotics community. The project will demonstrate these pipelines on robots in real-world scenarios such as agriculture and create new educational and outreach materials including open-source software, a research workshop, and student-training modules. The project outlines a new approach that bridges signal processing techniques with modern machine learning methods and tightly integrates perception and control. Specifically, the project will utilize this approach to enable four new capabilities: a new radar-based localization and mapping pipeline that uses neural networks and antenna array processing to create high-fidelity maps of the environment; a new passive mmWave markers that can seamlessly interact with off-the-shelf radars and use these markers to identify unseen objects in environments to obtain semantic scene understanding; neural fields to generate a realistic 3D model of the environment and enable realistic simulations of radar signals in this environment; and, build on these techniques for active perception strategies wherein the robot or drone seeks optimal viewpoints or modifies hardware parameters for enhanced perception. These components form a new approach for small robots and drones to sense and react to the environment, even in visually degraded conditions, improving robustness of robot operation. 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

Automatic Differentiation has become an important enabling technology for scientific computing and machine learning. Simply put, machine learning is a parameter fitting problem, and the computation of derivatives enables the fitting of parameters. Historically, programmers were required to spend significant time and effort developing these derivative codes by hand, making the use of machine learning and simulation on existing applications a tedious and difficult task. In recent years tools have been developed to generate code to compute these properties. However, they have been limited to specialized domains and specific programming languages. In contrast, the Enzyme project aims to generate derivatives of arbitrary programs in any LLVM-based language (e.g. C/C++, Fortran, Julia, Rust, Swift, Python, MLIR, etc), without restriction on scientific domain. The project's impacts are that scientists and engineers in all fields will be able to apply modern algorithms like neural networks to their domains without extensive rewriting of their entire application. This project will both develop the existing research prototype Enzyme implementation into a production-quality ecosystem, and establish an open-source community that will allow Enzyme to be maintained by the open-source community. This involves extensive user documentation and examples, documentation for new and existing Enzyme developers, integration into vendor compilers, organizing an annual Enzyme conference and weekly developer meetings, providing tutorials for Enzyme at relevant meetings, creating an Enzyme advisory board, and coordinating community satisfactions as well as development priorities. 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

Social media platforms see a surge of user-created polls, known as social polls, which gauge social media users’ opinions for various societal issues. These polls are not scientific and often exhibit biases favoring particular poll responses. Such polls can mislead the public into believing these biased outcomes reflect true public opinion. Every month, well over a million social polls are created on social media. However, these biased polls can give a misleading impression about what the public believes. Given the rising popularity of social media polls, it is crucial to address their potential to distort people’s perception of public opinion. This project aims to investigate and mitigate the harmful effects of biased social polls by identifying the biases, studying their prevalence and dissemination, examining potential harms, and developing corrective measures. These efforts will help maintain the integrity of public opinion perception. This project is pursuing three key goals. First, the project is identifying publicly visible social polls that misrepresent public opinion and evaluating the level of bias reflected in those polls by analyzing the demographic characteristics of social media users engaging with them. Second, the project is examining the prevalence and uses of such polls. Third, the project is developing a novel algorithmic method for correcting demographic biases in social polls via regression and post-stratification based on inferred attributes of users interacting with the polls. Finally, the project is experimentally assessing the effects of exposure to biased and bias-corrected poll outcomes on public opinion perception. To achieve these goals, the project is analyzing data from polls published publicly on social media, comparing the results of this analysis with the results of traditional polls, and conducting survey experiments to assess the impact of social polls on individuals. The project will significantly contribute to understanding and mitigating the impact of biased social polls on 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

Due to increasing numbers of satellites, particularly in non-Geostationary (nGSO) orbit, impacts from optical reflections to key astronomical systems such as the Legacy Survey of Space and Time (LSST) are anticipated to be profound without mitigation. Such mitigation may take several forms, but predictions of satellite locations and brightness will likely play a key role in avoidance of optical interference. This work includes development of software designed to produce these predictions based upon observed tracking data for satellites, and produce expected errors introduced in ground-based astronomical observatories. This will make better avoidance of such errors possible. This work brings together a consortium of experts to quantify and ultimately mitigate impacts on two key LSST science goals as well as develop software for use by ground-based astronomers. The PIs intend to leverage a collaboration with Aerospace Corporation to produce predictive data on satellite brightness and location. Brightness predictions will make use of a novel tool, Lumos-Sat, that enables Bidirectional Reflectance Distribution Function (BRDF) fitting to both laboratory and on-orbit observational data. Further, data collected through a network of engaged observers at the IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS) SatHub, as well as early LSST data, will be analyzed to validate the accuracy of the software (“SatChecker”) predictions. This information will be used to develop an analysis of errors introduced by satellite streaks in LSST observing 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

Networks, also referred to as graphs, consist of nodes (vertices) connected by edges (links). Many types of information can be represented as networks. For example, in social networks, vertices can represent people and edges can represent friendships, while in biological networks the vertices can represent proteins and the edges can represent interactions between proteins. A basic problem in network analysis is to partition the vertices of a graph into non-overlapping sets so that each set represents a cohesive group. This problem, referred to as “community detection” or “graph clustering”, has widespread application in many domains, including biology, engineering, and the social sciences. In this project, new community detection methods will be developed that can run on large networks in the order of millions and billions of vertices and will be implemented in highly efficient software that can be used in high performance computing platforms. An educational component is included with advanced training for both undergraduate and graduate students. Community detection, otherwise known as graph clustering, is the problem of partitioning the vertices of a graph into disjoint sets, so that each set has desirable properties, such as being well-connected (i.e., not having a small edge cut), having high internal density, and being relatively separated from other clusters. Common approaches for graph clustering include optimizing under the modularity criterion or the Constant Potts Model. Because these are NP-hard optimization problems, heuristic searches are used that can be very computationally intensive on large datasets. Furthermore, recent research has revealed limitations to currently popular methods, including the tendency to produce very poorly connected clusters, i.e., clusters with small edge cuts. The Connectivity Modifier software was developed to address this problem: it modifies a given clustering by iteratively finding and removing small edge cuts from clusters and then reclustering, until all clusters are well-connected. This project will develop new performant implementations of the Connectivity Modifier, and expand the set of clustering methods that can be used within the framework. The project will also develop a modular suite of clustering tools that address other problems in community detection, such as finding center-periphery clusters and overlapping clusters, that will enable developers to explore algorithmic approaches to clustering on large networks and also enable exploratory data analysis for applications researchers. The current implementations of these codes have not been implemented for very large networks nor for high performance computing (HPC) platforms. This project will develop parallel codes for these methods that can be deployed on a wide range of compute platforms. The research in this project will be integrated into graduate level courses and undergraduate and graduate students will participate in advanced research under the mentorship of the project investigators. The expected benefits include new software capable of analyzing networks with billions of vertices and that can be deployed in a wide range of hardware, enabling researchers to make discovery in a wide range of application areas, including systems biology, scientometrics, and social network analysis. 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

Computational notebooks have become a cornerstone of the scientific computing enterprise, providing an interactive means to acquire and communicate insights, share discoveries, and visualize experimental outcomes. However, computational notebooks today are most suited for small-scale explorations carried out on a single computer, and are quite difficult to use for large-scale computations on high performance computing clusters or commercial clouds. This project will develop NBFlow, a software toolkit for converting notebook computations into workflows that are feasible to execute efficiently on clusters or clouds. This will make it possible to use notebook technologies in conjunction with high performance clusters to enable new discoveries in scientific fields such as high energy physics and geosciences. These technologies will be used to develop new educational curricula, outreach activities for K-12 students, and research experiences for college students. NBFlow will support and advance the use of computational notebooks in scientific research and data analysis by bridging the gap between interactive computation and distributed cyberinfrastructure developed for data-intensive sciences. Today's notebook environments provide easy access to standard artificial intelligence and machine learning toolkits for processing vast datasets with greater efficiency and accuracy compared to conventional methods. However, migrating a notebook from a scratchpad-like analysis to a robust pipeline, which must be distributed across a cluster or cloud infrastructure, currently requires significant efforts by developers and scientists. NBFlow will build upon existing NSF investments in the areas of containerization and workflows that will record notebook executions and schedule tasks for concurrent execution. By experimenting with an integrated notebook-workflow system, this cutting-edge research will advance understanding in data management and distributed computing sub-fields. At the same time, the project will produce novel techniques to robustly capture provenance from notebook-based workflows, a rich source of data in itself, as well as put techniques developed for incremental computation to practice. These technologies will be deployed with active user communities in high energy physics at multiple facilities and in geospatial and sustainability sciences through the I-GUIDE cyberinfrastructure. 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 My research group combines traditional molecular biology approaches with microfluidic technology to examine how host-relevant shear flow impacts stress responses and surface adhesion of the human pathogen Pseudomonas aeruginosa. While reductionist experimental systems provide great mechanistic insight, they commonly lack key aspects of natural systems, such as fluid flow. Thus, there is a great opportunity to solve outstanding problems in microbiology by implementing experimental systems that more precisely model natural conditions. Two major recent discoveries from my lab highlight the scientific opportunities of studying bacteria in flow. First, we discovered that flow sensitizes P. aeruginosa to host-relevant doses of hydrogen peroxide (H2O2). Second, we discovered that host- relevant shear forces counter-intuitively enhance P. aeruginosa adhesion by counteracting pilus-driven surface departure. Over the next five-year funding period, we will use our microfluidic platform to build on these discoveries and investigate three key research gaps: how spatial H2O2 gradients impact bacterial communities (Project 1), how temporal dynamics of H2O2 stress responses are regulated in P. aeruginosa (Project 2), and how type IV pilus retraction promotes surface departure of P. aeruginosa in flow (Project 3). These projects will provide mechanistic answers to questions related to the basic biology of P. aeruginosa and will lay a clear foundation for innovative advances in the treatment of bacterial infections.

NIH Research Projects · FY 2024 · 2024-07

ABSTRACT KRAS is the most frequently mutated oncogene with mutation rates of 95% in pancreatic ductal adenocarcinoma (PDAC), 45% in colorectal cancer, and 30% in lung adenocarcinomas. The most common K-RAS mutations occur at codon 12, namely G12D, G12V, G12C, and G12R. K-RAS was considered undruggable due to lack of well-defined drug-binding pockets. But a recent breakthrough was achieved with covalent inhibitors that form a bond with K-RAS G12C cysteine. Several of these compounds are in clinical trials, and one was given FastTrack status by the FDA. Unfortunately, only a small fraction of K-RAS oncogene mutants harbor the G12C mutation, and G12D, G12V and other mutations do not provide an accessible cysteine nucleophile. In recent work with the RAS GTPase Ral we showed through high-resolution structures and extensive biochemical studies that covalent bond formation with Tyr-82 created a well-defined novel binding site located between the Switch II and the Switch I/II pockets. Additional fragment screening carried out more recently identified a fragment that forms a covalent bond at K-RAS Switch II Tyr-64 to inhibit activation of the GTPase by the Son-of-Sevenless (SOS) guanine exchange factor. Here, we hypothesize that covalent bond formation with tyrosine or other amino acids on K- RAS will inhibit activation or effector binding and block K-RAS signaling in cancer cells. Our preliminary data and extensive experience in the field puts us in a strong position to accomplish our objectives. In Specific Aim 1, we employ ligand- and structure-based methods to generate fragment electrophile libraries from large commercial collections, and we follow a structure-based method to grow hit fragments into neighboring pockets to enhance their binding affinity and reaction rates. In Specific Aim 2, we will carry out well-established intact protein mass spectrometry, nucleotide exchange, and effector binding studies to screen fragment libraries for hit compounds, and to characterize small molecules that emerge from fragment growing strategies. In Specific Aim 3, we will use X-ray crystallography to solve the structure of hit fragments and derivatives that emerge from fragment growing efforts. We also carry out cell biological studies to confirm direct engagement of K-RAS, inhibition of K- RAS signaling, and inhibition of cancer cell proliferation. We expect to identify high quality fragments and small molecules that form a covalent bond at wild-type and mutant K-RAS oncogenes, inhibit K-RAS wild-type or oncogene mutant activity in cancer cell lines, and inhibit PDAC and lung adenocarcinoma cancer cell viability. These compounds will serve as starting points to pursue in lead optimization efforts towards the development of therapeutic agents for the treatment of K-RAS-driven tumors.

NSF Awards · FY 2024 · 2024-07

Compound multiphase bubbles (gas-liquid bubbles coated by a distinct third fluid) are ubiquitous in various environmental and industrial configurations, since flow mechanics and physical chemistry allow bubbles to scavenge and concentrate dispersal compounds from the surrounding fluid. These buoyant compound bubbles can then transport the coating compound by rising, and ultimately eject them as small droplets into the atmosphere by bursting. The rising and bursting dynamics of these compound bubbles not only play an important role in ocean-to-atmosphere mass transfer of biogenic organics and anthropogenic pollutants, but also are widely applied in many bubble-mediated industrial processing, such as flotation and direct-contact heat exchange. Nevertheless, most previous studies only consider bare bubbles. How the rising and bursting dynamics of the compound bubbles are mediated by physical parameters of the multiphase system remains elusive. Therefore, the goal of this project is to develop a comprehensive and fundamental framework to understand how such a coating compound alters the bubble rising and bursting mechanics. The project will also provide outreach to a wide audience from K-12 to graduate levels, through undergraduate course reformulation, hands-on educational module, Engineering Open House, public web videos, mentorship and research training, to disseminate the research outcome and enhance public understanding of the project. This proposed research will leverage state-of-the-art experiments, scaling modeling and numerical simulations to accomplish two specific objectives. The first objective is to elucidate the rising dynamics of a compound multiphase bubble by characterizing the shape, trajectory, drag, path instability and wake structure with particle tracking and image velocimetry. Governing dimensionless parameters and the regime map for compound bubble rising dynamics will be obtained. The second objective is to elucidate the bursting dynamics and the resulting jets of a compound multiphase bubble by characterizing the cavity collapse and capillary wave damping with high-speed imaging and direct numerical simulation. New scaling laws for the jet size and velocity will be obtained considering effect of the coating compound on the capillary waves that control the jet formation and droplet ejection. Successful completion of this project will advance the fundamental description of multiphase flow physics with structurally compound interfaces, provide the physics-based model constraints to evaluate bubble-mediated scavenging and aerosolization of contaminants, and help optimize the productivity of multiphase contactors and reactors that utilize bubble dynamics in industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Plant breeding is pivotal in addressing the three priorities for U.S. agriculture: plant health, production, and products; food safety, nutrition, and health; and bioenergy, natural resources, and the environment. Developing improved plant breeding practices is crucial for overcoming future agricultural challenges, including climate variability and the need for secure, sustainable, and nutritious food supplies. Each year, the National Association of Plant Breeders (NAPB) holds an annual conference that highlights cutting edge interdisciplinary research and the methodologies and technologies applied by today's plant breeders. In addition, this meeting seeks to educate young scientists on the latest methods for developing sustainable plants and provide a platform for networking and collaborations, ensuring that discoveries translate into commercially relevant products with real-world impact. NSF funds will be used to provide travel awards to students and early career investigators, especially those from underrepresented groups in STEM, to participate in the annual NAPB conference to be held July 21-25, 2024, in St. Louis (MO). The 2024 NAPB Annual Conference will serve as a dynamic platform for scientists and professionals from diverse backgrounds to present research findings, network, discover cutting- edge research, and foster collaborations. By championing diversity and inclusion, the conference aims to enrich the plant breeding community with a broader range of perspectives, fostering more innovative research and improved agricultural practices. The event is poised to make significant strides in advancing the field of plant breeding, addressing key agricultural challenges, and cultivating a collaborative and inclusive community, thereby contributing to global food security and sustainable agricultural practices. 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

Non-Technical Description This project is developing methods to understand how metal nanoparticles, 1000 times smaller than the width of a hair, capture and convert light into usable energy when contacting metal oxide semiconductors. Although metal nanoparticles efficiently absorb light, most of the absorbed energy is converted into heat. On the other hand, metal oxide semiconductors can store light energy for much longer times than metals making them useful for applications such as photodetection. However, metal oxide semiconductors do not absorb as strongly or often only at specific wavelengths, while metal nanoparticle can be designed to strongly interact with light of any color. This project overcomes these limitations by combining the high absorption of metal nanoparticles with the longer lifetimes of the absorbed light energy in metal oxide semiconductors. The principal investigator uses techniques that allow him to study how the light energy absorbed by a metal nanoparticle is transferred to an adjacent metal oxide semiconductor layer. These experiments are carried out for one nanoparticle at a time to resolve heterogeneities that arise from materials synthesis. In addition, the PI is continuing his longstanding participation in Rice University’s Civic Scientist Program and Research Experience for Teachers, allowing him to educate K-12 students about nanotechnology and inspire them to pursue scientific careers as well as to provide teachers with experience to in turn help students in those pursuits. Technical Description The goal of this project is to understand and maximize plasmon decay into charge separated states between a metal nanoparticle and an adjacent metal oxide semiconductor via direct charge transfer following plasmon excitation. The principal investigator will accomplish this goal by addressing the following objectives: 1) Design and fabricate plasmonic metal–semiconductor heterostructures and establish a correlation with interface induced plasmon decay via changes to the homogeneous plasmon linewidth; 2) Quantitatively determine charge injection into semiconductors surrounding plasmonic nanostructures using single particle ultrafast spectroscopy and correlate with efficiencies obtained from plasmon damping; 3) Apply Stokes and anti-Stokes emission spectroscopy to independently follow interfacial charge transfer through emission quenching under both one- and multi-photon excitation conditions. These proposed studies will elucidate the mechanism of interfacial charge transfer in plasmonic heterostructures and the underlying material parameters that determine efficiencies with a focus on excess energy as determined by the plasmon resonance and the relative band alignment including Schottky barrier height. Such detailed mechanistic information would be impossible to obtain without single-particle techniques due to the heterogeneity of plasmonic nanoparticle sizes and local environments. The proposed studies will potentially have a transformative impact on developing efficient photovoltaic devices based on plasmonic metal-semiconductor heterostructures taking advantage of a wide wavelength sensitivity, large absorption cross section, and long hot carrier lifetime. 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

Random objects in general metric spaces have become increasingly common in many fields. For example, the intraday return path of a financial asset, the age-at-death distributions, the annual composition of energy sources, social networks, phylogenetic trees, and EEG scans or MRI fiber tracts of patients can all be viewed as random objects in certain metric spaces. For many endeavors in this area, the data being analyzed is collected with a natural ordering, i.e., the data can be viewed as an object-valued time series. Despite its prevalence in many applied problems, statistical analysis for such time series is still in its early development. A fundamental difficulty of developing statistical techniques is that the spaces where these objects live are nonlinear and commonly used algebraic operations are not applicable. This research project aims to develop new models, methodology and theory for the analysis of object-valued time series. Research results from the project will be disseminated to the relevant scientific communities via publications, conference and seminar presentations. The investigators will jointly mentor a Ph.D. student and involve undergraduate students in the research, as well as offering advanced topic courses to introduce the state-of-the-art techniques in object-valued time series analysis. The project will develop a systematic body of methods and theory on modeling and inference for object-valued time series. Specifically, the investigators propose to (1) develop a new autoregressive model for distributional time series in Wasserstein geometry and a suite of tools for model estimation, selection and diagnostic checking; (2) develop new specification testing procedures for distributional time series in the one-dimensional Euclidean space; and (3) develop new change-point detection methods to detect distribution shifts in a sequence of object-valued time series. The above three projects tackle several important modeling and inference issues in the analysis of object-valued time series, the investigation of which will lead to innovative methodological and theoretical developments, and lay groundwork for this emerging field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT Type 2 diabetes mellitus (T2DM) poses a serious global health issue, affecting millions globally, with a concerning rise in younger age groups. T2DM patients often suffer from hypoglycemia, typically medication- induced, leading to severe consequences and cognitive impairment. Frequent hypoglycemic episodes can give rise to 'hypoglycemia unawareness,' escalating the risk of severe hypoglycemia and cognitive decline, including dementia. Investigating cognitive alterations induced by hypoglycemia in T2DM patients is complex due to the interplay of vascular responses, brain activity, and cognitive functions. Current functional imaging techniques can visualize brain vascular parameters but lack compatibility with behavioral assessments, creating a challenge to correlate brain activity with cognitive functions. Our research project aims to unravel the intricate connections between vascular responses and cognitive dysfunction influenced by recurrent hypoglycemia. To achieve this, we will employ a novel head-mounted dual- modality photoacoustic/ultrasound localization (PAUL) imaging technique. With its exceptional resolution, PAUL imaging allows us to visualize detailed patterns of brain blood flow and blood oxygenation, providing data on par with fMRI. We anticipate that this advanced imaging method will enable the visualization of dynamic changes in brain vascular signatures during cognitive assessments. Through this innovative approach, we expect to gain valuable insights into the association between hypoglycemia and cognitive impairment. Our approach involves three distinct aims. First, we plan to investigate the effects of various hypoglycemia conditions on brain vascular responses in anesthetized rats with T2DM. By inducing diabetes in middle-aged rats and employing PAUL imaging, we aim to record vascular responses under varying insulin-induced hypoglycemia levels, frequencies, and recurrent lengths. Second, we will design a lightweight head-mounted probe for high-quality PAUL imaging of brain vascular responses in freely moving rats, reducing stress during cognitive assessments. Lastly, we aim to scrutinize the brain vascular signatures and cognitive functions in rats with recurrent hypoglycemia. This will involve assessing cognitive behaviors while simultaneously imaging brain blood flow and oxygenation, alongside monitoring glucose levels.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT More than 42% of adults in the United States live with obesity, a condition characterized by excess body fat primarily within adipose tissue. Obesity is a risk factor for type 2 diabetes, cardiovascular diseases, cancer, and severe COVID-19 disease and has few widely effective non-surgical interventions. Obesity comorbidities are believed to be triggered by chronic inflammation in adipose tissue, in part caused by infiltration of macrophage cells (MΦs) with adipogenic phenotypes in the obese state. However, the pathogenic role of MΦs is complicated by their diverse and antagonistic roles in adipose metabolism and homeostasis as these cells have the capacity to promote both lipogenesis and lipolysis and promote both tissue hypertrophy and atrophy. Previously, we showed that polysaccharide-based nanocarriers could efficiently deliver drugs to adipose MΦs after intraperitoneal injection. We found that certain classes of these therapies potently modulate MΦ phenotype, induce rapid body weight loss, and reverse diabetic phenotype in obese rodents without changes in food intake. We now propose to develop an extended-release drug depot to deliver these MΦ-targeted nanocarrier drug conjugates to adipose tissue in a translational format designed for high patient adherence. Two primary technological products will be (1) biodegradable composite depots constructed from materials that are efficiently and safely eliminated from the body and (2) translational imaging techniques to longitudinally monitor drug depots in vivo. In Aim 1, we will tune the composition, size, and shape of the depots to generate implants with translational form factors, steady release over 6 months, and minimal foreign body response. We will also validate methods to image and monitor depots by magnetic resonance, ultrasound, and fluorescence modalities. In Aim 2, we will maximize drug loading and redesign the backbone of our targeted nanocarrier and drug linkers for efficient excretion and high solubility. In Aim 3, we will determine mechanisms by which regional depots elicit systemic physiological changes. In Aim 4, we will measure the long-term impact of lead candidate depots on preventing or reversing obesity and obesity-induced diabetes. We will study the dependence of efficacy on sex, hormonal status reflecting menopause, and genetic or dietary obesity origins. If successful, the drug delivery system may provide an urgently needed non-surgical high-adherence strategy to treat patients living with obesity to safely enhance weight loss and prevent or reverse the progression of comorbidities.

NIH Research Projects · FY 2025 · 2024-06

PROJECT SUMMARY Small Grant Program R03 proposal aims to generate CD43-floxed mice using the CRISPR-Cas9 technology and characterize the cell-specific antifungal functions of CD43 in hematopoietic and T cells using conditional CD43 KO mice. The preclinical model systems have been invaluable, providing insights into the human system, understanding the immune mechanisms, developing vaccines and immune therapeutics, and facilitating novel discoveries relevant to human medicine. Fungal infections, in the face of a lack of vaccines, cause significant mortality, rivaling TB and malaria, and instigated devastating complications during the COVID-19 pandemic. Further, the continuous rise in the immune-compromised or suppressed population by both nosocomial and non-nosocomial factors, as well as the expansion of fungal habitat due to climate change, has heightened the risk of fungal infections and posed a horde of public health challenges. A major limitation in the immune control of respiratory fungal infections is a lack of knowledge of protective host defense mechanisms, both innate and adaptive. Sialophorin (CD43) is implicated in viral, bacterial, parasitic, and fungal infections. CD43 research has shed light on its costimulatory/inhibitory, adhesive/anti-adhesive cell property, inhibiting or promoting apoptosis, receptor microbes/microbial product, binding several ligands, and in the aggressiveness of several lymphoid, myeloid, and epithelial cancer cells. The functionality and expression in several cell types with often counterintuitive phenotypes in the functions of CD43 have clouded our understanding and myopic views of its cell-specific functions. Regrettably, CD43-floxed mice are unavailable and are necessary to address much confounding research, including ours. Our preliminary data showed that CD43 (using global KO) is essential for immunity in a mouse model of pulmonary fungal infections, but we unexpectedly found its role in non-hematopoietic cells, contrary to our understanding its role in hematopoietic cells. In line with this, differing from the mouse model of viral infection, we found an essential role of CD43 (using global CD43 KO) for CD8+ T cell responses to fungal vaccinations and vaccine immunity. Therefore, we propose generating a CD43-floxed mouse to address the cell-specific role of CD43 in immunity against fungal infections by developing conditional CD43 KO. Our overarching hypothesis is that CD43 is integral in hematopoietic and non-hematopoietic cell responses in immunity against respiratory fungal infection. We aim to (1) generate CD43-floxed mice using the CRISPR-Cas9 technology to develop conditional KO mice and (2) Characterize CD43-floxed mice by generating conditional KO mice for immunity against fungal pneumonia and responses to the fungal vaccine. The outcomes of our projects will help advance our understanding of the cell- specific functions of CD43 during fungal infection and open universal applications in biomedical research to address confounding roles of CD43 in infectious, transplantation, tumor, and auto-immunity research thrusting new immunotherapeutic avenues.

NSF Awards · FY 2024 · 2024-06

The scientific objectives of this project are to convene an international summit to discuss the practicability of creating a special-purpose computational system for frontier Earth system science and climate projections at kilometer-scale global resolution. Earth System Models (ESMs) run at high resolution may help to address sources of model bias, which in some cases is significant, the pace of global warming, local and regional impacts (e.g., ecosystems, health, agriculture, inundation), and details of clouds and precipitation including the behavior of high-impact weather (severe storms, hurricanes, flash floods). Although current computers are extraordinarily powerful and theoretically able to perform a quintillion operations per second (exascale), ESMs currently may obtain only a few percent of this peak in practice. Therefore, various types of downscaling from coarser resolution runs are required to provide information about local impacts (information which would be improved with higher resolution), and biases which cannot be addressed without higher resolution remain unresolved. Current projections suggest global ESM resolutions of 1 km will not be attainable until around 2055, which is far beyond the time needed for critical decisions to be made regarding climate mitigation and adaptation. Consequently, it is prudent to examine the possibility of creating a special-purpose computational system for frontier Earth system science and climate projection for use by the global community. An effective first step forward in such an endeavor is holding an international summit at the University of Illinois Urbana-Champaign (UIUC) in fall 2024. The computing system would need to accommodate both ESMs and new approaches that couple or tether such models to artificial intelligence (AI). The summit will assemble international leaders in relevant disciplines from academia, industry, government, and non-profit organizations to examine several key questions relating to ESMs and the role of AI in and around them going forward; possible structures of a special-purpose computational system and alternative pathways; strategies for creating such a system; and expected benefits. The potential Broader Impacts (B.I.) include an opportunity to engage a diverse, international audience and provide an opportunity for practicing researchers, as well as students and post-doctoral scholars across all types and sizes of institutions, to learn not only about a special-purpose computational system for Earth system science and climate projection, but also about the value of using fine-scale model output to inform society more broadly regarding local and regional climate change. Topics to be discussed include economic and social impacts of climate change; risk communication and management; health and epidemiology; international relations; population dynamics;infrastructure; coastal vulnerability; and national security and defense. The summit will be free, streamed live on the Internet, and recorded for subsequent viewing. A final report containing summaries of discussions and a set of recommendations will be prepared and made freely available on the summit website. 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 · 2024-06

Ribosomes synthesize every protein in the cell. In the crowded cellular environment, translating ribosomes pause, stall or even collide, but how these “troubled” ribosomes recover and resume protein synthesis is unclear. Furthermore, little is known about how the cell distinguishes programmed, physiologically important pauses for proper protein folding and targeting versus problematic, even pathological stalls due to translation errors. To uncover the responsible cellular pathways and molecular interactions, this project will investigate how the conserved, developmentally regulated GTP-binding protein Drg promotes protein synthesis when translating ribosomes slow down. The outcomes of this investigation will provide new insights and establish new principles for regulation of gene expression. In addition, the educational goals of the project include new curriculum development and integration of research and education with an emphasis on creating more opportunities for studnets in STEM. As an ancient GTPase, Drg protein has co-evolved with the ribosome, and recent findings indicate that it can serve as a new general translation factor. To explore its role in translation, two complementary aims will be pursued in this study. Aim 1 seeks mechanistic understanding of Drg-mediated functions in bacterial and eukaryotic translation through in vitro biochemical studies of fully reconstituted translation systems from E. coli and S. cerevisiae. Translation assays will be utilized to probe the conformation of the peptidyl transferase center and to measure the kinetics of peptide bond formation, which will clarify the nature and extent to which Drg affects translation when ribosomes pause. Aim 2 seeks to uncover molecular interactions important for Drg function. APEX proximity labeling will be used to determine Drg interactomes in E. coli and S. cerevisiae cells, which will be followed by mutagenesis and biochemical studies. The findings from this study will elucidate an evolutionarily conserved mechanism by which cells can regulate and fine-tune proteostasis. 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 · 2024-06

ABSTRACT The Training Program at the Interface of Reproductive Sciences and Bioengineering will unite two long-standing areas of research excellence on the University of Illinois, Urbana-Champaign campus—reproductive biology and engineering - to train predoctoral students to conduct innovative and high-quality research at the forefront of reproductive biology. This unique educational program will blend techniques from biological sciences and engineering to provide training in research areas that align with the mission of NICHD, and includes studies of germ cell development/meiosis, hypothalamic-pituitary-ovarian function, spermatogenesis & fertilization, embryo-uterine crosstalk & pregnancy, and placentation & preterm birth. Engineering principles that present exciting opportunities to transform research in reproductive sciences are studies involving novel three- dimensional cell culture in natural or synthetic matrices and assembly of organoids to better understand the function and interaction of cells in reproductive tissues, mechanobiology to better understand the relationships between the microstructure, mechanical properties and function of the reproductive tissues, and innovations in diagnostic methods such as advanced imaging and biosensors to assess placental development and function during pregnancy. Twenty-four faculty members from 14 departments or interdisciplinary research institutes across 4 colleges will serve as preceptors for the Training Program, making this a truly interdisciplinary program. Participating faculty are well-funded and clearly have the necessary research support to provide current and future trainees excellent training opportunities. There are 50 federally funded research grants totaling over 8.6 million dollars/year in direct costs, and 5 grants from other sources totaling over half million dollars/year in direct costs among the 24 preceptors of this proposal. Trainees will be drawn from exceptionally strong graduate programs across various departments, which altogether have over 1,787 total predoctorates. Of these predoctorates, 103 work with the participating faculty, of which, 38 are eligible to participate in this training program. This will enable the program to select high-quality predoctorates, expose them to cutting-edge and emerging technologies that can advance progress in reproductive research, and train them to become the next generation of interdisciplinary leaders. Selection of the 6 predoctoral trainees supported by this Program is based on academic success, strength of the proposed research, and relevance of the research to Program goals. Training will include Curricular Activities to ensure breadth across disciplines. It will also focus on trainees’ professional development and prepare them with the skill set to succeed in their careers. This unique program, which enjoys strong institutional support and commitment, is designed to ensure that our trainees are fully equipped to meet the challenges required to succeed in any professional environment such as academia, industry, or government.

NSF Awards · FY 2024 · 2024-06

Monitoring ground motion caused by human activity (e.g. ships, rail and vehicular traffic, explosions, civil infrastructure), biological activity (e.g. marine and terrestrial animal migrations and behavior), and natural disasters (e.g. earthquakes, tsunamis, lightning) is critical for research, public safety, and security. The key considerations for sensors used in these monitoring applications include precision, response time, accuracy, deployability, and cost. The planning work supported through this project will develop the framework for a future mid-scale research infrastructure proposal that would support the build-out of a national testbed facility focused on fiber-based ground motion and deformation sensing. The foundational idea for this facility is that relatively minor modifications to devices that send and receive data signals on fiber-optic cables deployed in the Internet can enable ground motion sensing capabilities that are not possible with standard technologies. The planning activities for the envisioned Internet Sensor Network (Internet-S) testbed will identify the associated technical requirements and develop a framework for the facility that is feasible in terms of costs and operation. This work will also focus on fleshing out details of the key components of the facility, which include (i) an optics laboratory for controlled, repeatable research with optical components; (ii) a dark fiber network for in situ research of optical sensing devices; and (iii) an optical data analytics and visualization workbench for organizing and interpreting fiber-based sensing data. Working groups comprised of thought leaders from key disciplines including computer science, geoscience, physics, and electrical engineering will be organized to develop the Internet-S testbed design. The planning activities will also include two workshops that will convene a broader group of researchers, equipment vendors, network operators, and government entities to discuss Internet-S and seed future use of the envisioned research testbed infrastructure. The mid-scale research infrastructure envisioned in this work will be used by a diverse community of scholars to develop new Internet-based environmental and infrastructure sensing capabilities that go well beyond current technologies in terms of scale, sensitivity, and manageability -- at low cost. The new sensing systems developed in the testbed will be used in a wide range of applications, including computer networking, optics and geoscience research, smart cities, public safety, and security. This work will also influence the development of new concepts and expertise that will be used in computer networking, geoscience, and data science courses. 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 · 2024-06

Project Summary Ribosomally synthesized and post-translationally modified peptides (RiPPs) constitute a large class of natural products with a wide range of biological activities, including anticancer, antinociceptive, antiplasmodial, and antibacterial. The biosynthetic enzymes that convert linear, inactive peptide substrates into constrained bioactive compounds are not well understood, especially with regards to enzyme mechanism, substrate recognition and in their utility for producing new derivatives. The Nair research group has been engaged in research to address these questions for the past 18 years. We seek to carry out biochemical, structural biological, and engineering studies on several classes of RiPP biosynthetic enzymes and address questions regarding substrate recognition, and substrate tolerance. We anticipate that this work will be of use not only towards the discovery of new therapeutic compounds but to enable the production of both of improved versions of these bioactive compounds, as well as new to enable production of peptide-based compounds for which the biosynthetic pathways are not known.

NIH Research Projects · FY 2026 · 2024-05

PROJECT SUMMARY / ABSTRACT Aging has long been known to be a risk factor for the development of postoperative neurocognitive disorder (poNCD), but the mechanisms underlying this observation are largely obscure. Approximately 10% of patients > 60 years of age undergoing surgery and anesthesia develop poNCD, which can be present for several months. Work by others indicates that in young adult mice several general anesthetics lead to a redistribution of α5- containing GABAA receptors (α5-GABAARs) into cell surface membranes, which is associated with an impairment of cognitive function. It has been shown in mice that negative allosteric modulation of α5-GABAARs blocks the sustained memory-impairing effects of general anesthetics. This is the current “dogma” in the field. Our hypothesis is, however, that an age-related functional reduction of the α5-GABAA receptor system at least in part underlies declining cognitive functions with aging. In support of this hypothesis, we have demonstrated that in aged mice (>18 months of age) both increasing the availability of α5-GABAARs (with chronic intermittent propofol) and positive allosteric modulation of α5-GABAARs (with MP-III-022) result in cognitive improvements and an essentially complete block of surgery-induced memory impairments. We now propose to study molecular and cellular mechanisms underlying age-related cognitive decline including the mechanisms by which increased activity of α5-GABAARs can attenuate surgery-induced inflammation. We plan to examine the exact timing of the effects of such an increased functional activity of the α5-GABAARs on blocking the development of postoperative cognitive deficits to examine differential contributions of a reduction of microglial activation and of a normalization of an α5 “deficit” with aging. Transcriptomic studies will reveal whether increased availabiliy of α5-GABAARs on cell surface membranes or positive allosteric modulation of α5-GABAARs reverse at least some of the aging-related changes in gene expression and mitochondrial function. We then want to clarify the role of a specific GABAA receptor subtype on astroytes and of astrocytes in general on the distrubution of α5-GABAARs in cellular membranes, in part using calcium imaging in vivo. Finally, we want to identify cellular targets underlying the prevention or reversal of postoperative cognitive deficits in aged mice. In mice conditionally lacking α5-GABAARs in different principal neurons and interneuronal cell types and on microglia cells we expect to see a contribution of α5-GABAARs on multiple cell types to the attenuation of postoperative cognitive deficits. The involvement of the hippocampus in this process will be assessed by monitoring place cell formation and stability of the memory engram by calcium imaging in vivo. The proposed studies are envisioned to have a positive translational impact, providing a pathway for testing the positive modulation of α5-GABAARs in elderly patients undergoing surgery and anesthesia. Positive allosteric modulators of α5-GABAARs are currently being developed by others, and once such compounds become available for experimental use in humans, our proposed studies provide a fundamental science framework supporting clinical studies for poNCD.

NIH Research Projects · FY 2026 · 2024-05

PROJECT SUMMARY/ABSTRACT The desire to enhance social cohesion is the most widely endorsed reason for consuming alcohol, with social settings being linked to enhanced alcohol reinforcement and socially motivated drinking. With over 85% of everyday drinking episodes taking place in social settings, social drinking contexts are frequently associated with negative consequences including binge drinking and an increased risk for developing Alcohol Use Disorder (AUD). However, the neurocognitive mechanisms underlying the rewarding effects of alcohol in social contexts are largely unknown. Of note, in contrast to real-world settings, paradigms employed in extant neuroimaging research predominantly feature solitary drinking followed by isolated recording settings. This stark discrepancy between laboratory and real-world drinking contexts may hinder scientists from directly examining the foundational research question of what makes alcohol rewarding. The long-term objective of the proposed study is to address this research gap, using novel alcohol administration methods combined with electrophysiological measures through a hyperscanning EEG setup (i.e., simultaneous recording of EEG signals across multiple participants) in the context of in-vivo social settings. Specifically, in line with NIAAA Strategic Plan (Goal 1.1), the proposed project aims to evaluate the neural correlates of social and affective reward gained from alcohol in social contexts as well as to assess whether this reward differs across individual-level (e.g., social anxiety, social bonding) and group-level (social familiarity) factors. The proposed research represents a unique contribution to my sponsor’s ongoing Ro1 study which, once completed, will represent among the larger multi-dose alcohol administration studies conducted to date. In the parent project, participants (N=240) attend three laboratory sessions involving alcohol administration in groups of two. The proposed project examines a subset of these participants, exploring the effects of alcohol dose (0.00%, 0.03%, and 0.09% target BAC) and social familiarity (“fast friends” vs. strangers) as factors manipulated both within and between participants, respectively. Following beverage administration, dyads will participate in a music-listening task together while their EEG signals are acquired simultaneously. Participants will additionally complete self-report measures of social reward using multiple indicators, including reports of affective experience, positive mood, and social bonding. The results of this study promise a range of theoretical and clinical implications, advancing theoretical models of AUD vulnerability within a bio-psycho-social framework and informing prevention and intervention programs by identifying real-world “high-risk” settings associated with enhanced alcohol reinforcement. In addition, and of note, this award will provide critical training to an emerging predoctoral researcher in advanced programming and research skills while at the same time offering experience in modern EEG/ERP methods to develop the applicant’s expertise in the area of etiological factors undergirding the development of AUD.

NIH Research Projects · FY 2025 · 2024-04

Project Summary/Abstract The process of long-term potentiation (LTP), particularly at the hippocampal Schaffer collateral pathway, has been regarded as one of the most relevant cellular processes underlying learning and memory. Numerous studies have demonstrated that changes in gene transcription are required during the execution of LTP. However, it is much less understood regarding how the dysregulation of gene transcription contributes to intellectual disability and what transcription factors are involved. To approach this question, we gathered preliminary data to show an elevation of tumor suppressor p53 following the induction of N-methyl-D-aspartate receptor (NMDAR)- dependent LTP. It has been well-established that LTP requires elevated surface expression of α-amino-3- hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR). Our data showed that knocking down p53 in forebrain excitatory neurons diminished the surface expression of AMPAR during LTP and ultimately impaired learning and memory behavior in mice in vivo. Importantly, we also observed abnormally down-regulated p53 in two mouse models of intellectual disability. We therefore propose to test the hypothesis that p53-dependent transcription is required for activity-dependent synaptic plasticity, learning, and memory, and its dysregulation is associated with intellectual disability. In Aim 1, we propose to characterize how p53-dependent gene transcription mediates AMPAR surface expression and participates in hippocampal LTP. In Aim 2, we will employ genetic and pharmacological approaches to restore p53 in two mouse models of intellectual disability with the intent to improve hippocampal synaptic plasticity and learning behavior. We expect our work to facilitate a much deeper understanding of hippocampal synaptic plasticity and open new avenues for the study and potential correction of neurological and psychiatric disorders that are associated with intellectual disability.