Brown University

NSF Awards · FY 2024 · 2024-08

The area of study of this project lies within algebraic geometry, the branch of mathematics devoted to geometric shapes called algebraic varieties, defined by polynomial equations. Algebraic geometry has significant applications in in coding, industrial control, computation, and in theoretical physics, where physicists consider algebraic varieties as a piece of the fine structure of our universe. One focus of this project is moduli theory, which studies a remarkable phenomenon in which the collection of all algebraic varieties of the same type is often manifested as an algebraic variety, called a moduli space, in its own right. Thus in algebraic geometry, the metaphor of thinking about a community of "organisms" as itself being an "organism" is not just a metaphor but a rigorous and quite useful fact. A second focus in this project is birational geometry, focusing here on resolution of singularities. Resolution of singularities is a fundamental procedure where "bad" points of an algebraic variety are removed and replaced by "good" points; it is the most powerful tool in the hands of a binational geometer. The project will provide research training opportunities for graduate students. In more detail, regarding moduli spaces the PI will study the enumerative geometry of certain moduli spaces of surfaces, a decades-old challenge. In an area where birational geometry and moduli spaces overlap, the PI will continue to study the birational geometry of stack theoretic weighted blowups, a transformation that occurs frequently on moduli spaces that has proven instrumental in describing their geometry. Regarding resolutions of singularities, new algorithms will be developed for logarithmic resolution that are remarkably simpler than earlier ones, an algorithm for resolution in the presence of a nested family of foliations will be developed, and singularity invariants in positive characteristic will be studied that will lead to new insights into the formidable challenges of resolution in positive characteristic. These efforts will serve as platforms to directly mentor PhD students and young researchers, and for lectures and training programs reaching broader audiences. 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

Many biological and physical systems exhibit high degrees of internal complexity that resist direct mathematical description. Examples include ecological dynamics in a varied environment and fluctuating flame fronts in combustion. Nonetheless, such systems often exhibit tractable behavior when viewed at large or fine scales. This "asymptotic" behavior plays a major role in applications, and often has a universal character that unites the study of disparate systems. In this project, the principal investigator (PI) will combine several mathematical methods to identify and justify asymptotic phenomena in partial differential equations (PDEs) originating in the sciences. This work has the potential to shed light on a variety of systems including ecological invasion, atomic deposition, and fluid shock formation. The PI is committed to undergraduate and graduate mentorship, with the particular aim of supporting students from underrepresented backgrounds. This project will explore the asymptotic behavior of various deterministic and stochastic PDEs in significant limiting regimes. The project comprises three interconnected lines of work. (1) The PI will study the long-time propagation speed and front structure of solutions to reaction-diffusion equations in heterogeneous and random environments. This investigation encompasses a dual analysis of associated branching particle systems. (2) The PI will combine analytic and probabilistic methods to study long-time and white-noise limits of several physically motivated stochastic PDEs, including stochastic conservation laws and stochastic heat equations near criticality. (3) The PI will investigate the action of weak viscosity on internal shock formation in the compressible Navier--Stokes equations. This involves a delicate coupling between hyperbolic and parabolic approximations. 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 will fund research to develop new methods to guide economic and other public policy making in rapidly changing environments. Policy makers, such as Central bankers, fiscal authorities, and foreign currency dealers, make decisions in rapidly changing environments in which they often have to learn and adapt on-the-fly. However, economists do not currently have methods that can effectively guide policy makers in these changing environments. This research will build on methods in statistical decision theory, frameworks for establishing causation from data, and research in machine learning to develop new methods to inform evidence-based policy choices while accounting for uncertainty about the environment. The results of this project will make important contributions to economic science by offering new methods to guide policymakers. The results will improve the quality of policy making, increase efficiency, productivity, and economic growth hence improve the living standards of citizens. There are many challenges inherent in studying optimal policy choices in dynamic environments, including nonstationary dynamic causal effects, non-iid observations, ensuring external validity, and agents' reactions to changes in policy rules. This award funds research that builds on recent development in time-series, statistical decision theory, and machine learning to develop new econometric methods for policy analyses in dynamic environments. The first part studies short-run policy choice problems where reactions to policy choices by agents and long-run equilibrium spillovers are ruled out. The second part focuses on learning in a long-run policy rule considering agents’ reactions to changes in policy (i.e. robust to Lucas critique). The third part considers an unsupervised learning setting where the policymaker interacts with agents and the environment over time to collect data, learn about parameters governing the causal structure and data generating process, and chooses policy to maximize welfare. The results establish foundations for statistical decision theory and policy learning in dynamic settings and provide new methods to guide policy making in a wide range of empirical contexts. The results of this research will improve policy making, increase efficiency, productivity, and economic growth hence improve the living standards of citizens. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

PROJECT ABSTRACT Objective: The purpose of this application is to identify a rate of normative cognitive aging (i.e., cognitive aging in individuals without a neurocognitive disorder), and determine how much quicker cognitive aging is among individuals living with Alzheimer disease or related dementias (AD/ADRD). Using data from the Children of the Depression (CODA) cohort of the Health and Retirement Study (HRS), we will first identify a latent variable estimate of general cognitive performance (Aim 1). Afterwards, we will identify the pace of normative cognitive aging in standard deviations per year (SD/year), both unadjusted and adjusted for known covariables that influence cognition (e.g., age, sex, race and ethnicity, education; Aim 2). Finally, we will use Centers for Medicare & Medicaid Services (CMS) data linked to the HRS to identify participants who receive a dementia diagnosis during follow-up, and determine how much faster cognitive aging is among individuals living with AD/ADRD (Aim 3). In line with PAR-23-179, this proposal will also develop the career for early-stage investigator, Dr. Kunicki, and will serve as a basis for future NIH grant proposals. Significance: Cognitive ability declines with age, and tends to aging quicker later in life. Whereas neurocognitive disorders such AD/ADRD are known to increase the rate of cognitive aging, there is no well- established reference of normative cognitive aging. Lacking this reference pace makes it difficult for researchers to evaluate the results of their studies (i.e., determining if a cohort is declining at a normal or quicker than usual pace). Moreover, we will estimate how much quicker cognitive aging is among individuals living with AD/ADRD. Approach: We will use data from the CODA cohort of the HRS, which is comprised of over 2,000 participants aged 68-74 at baseline with 22 years of follow-up data available. Using the HRS cognitive measures, we will identify a latent variable of cognitive aging, and use the latent variable to identify the pace of normative cognitive aging in SD/year. Then, using CMS data, we will identify participants who received a dementia diagnosis during follow-up and determine how much faster cognitive aging is among individuals living with AD/ADRD. Sensitivity analyses will also examine the pace of normative cognitive aging by different cognitive domains. Innovation: This study is innovative because it addresses a major gap in the literature in cognitive aging by identifying a pace of normative cognitive aging and among individuals living with AD/DARD. These paces will be useful for researchers to use as reference points to interpret results of clinical trials to slow cognitive aging. Despite a lack of normative cognitive aging rates being a well-known issue in the field of aging, this study will be the first to identify a nationally representative rate of cognitive aging among older adults.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY Young adulthood (ages 18-25) is a critical developmental period during which individuals often escalate or mature out of substance use. Indeed, young adults (YA) use cannabis at the highest rate of any age group, with 35% reporting past year use, thus it is critical to identify factors associated with hazardous cannabis use given the potential impact on this vulnerable developmental group. Heavy cannabis use among YA is related to poorer health later in life, deleterious long-term effects on cognition, engagement in hazardous behaviors while under the influence (e.g., driving), and myriad other cannabis-related problems. As such, it is imperative to understand factors that influence the time course of cannabis use patterns and mechanisms underlying transition from casual to heavy or hazardous use levels. Notably, young adulthood is characterized by frequent, smaller-scale transitions (i.e., micro-transitions) and critical life events that can lead to an escalation or reduction in cannabis use, likely depending on their subjective evaluation (i.e., valence). Certain transitions may increase cannabis use frequency (e.g., college entrance), while others may be protective (e.g., marriage). A behavioral economic (BE) framework can help explain how micro-transitions during young adulthood influence prospective changes in cannabis use. BE domains are influenced by internal (e.g., craving) and external (e.g., new employment) influences and include (1) access to and preference for alternative reinforcers (i.e., lack of alternative activities that compete with cannabis), (2) discounting of delayed rewards (i.e., inordinate preference for smaller immediate rewards, such as positive cannabis effects), and (3) relative cannabis value (i.e., demand; willingness to pay prohibitively high prices for cannabis despite limited resources or income). Further, motives for cannabis use (e.g., coping, enhancement) are key variables that likely account for the relation between micro-transitions and changes in cannabis use among YA as well. Investigating factors that relate to escalation or reduction in cannabis use are of clear

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT In the United States (US), it is recommended to not use cannabis during pregnancy or while breastfeeding (referred to as “perinatal cannabis use”) because use during pregnancy has been associated with negative parental-child outcomes. Also, tetrahydrocannabinol (the principal psychoactive component of cannabis) can be transferred through breastfeeding. Yet, rates of use as well as perception of cannabis safety are increasing within the pregnant population. Further, a subset of perinatal people persist in use despite knowledge of the risks. Medical providers in the US are thus advised to educate and counsel patients about perinatal cannabis use. This counseling can be particularly complex for patients who are unable or unwilling to entirely discontinue use even once educated about the risks. However, medical providers currently lack the training support needed for effectively engaging in discussions about perinatal cannabis use. Harm reduction strategies, which aim to reduce the negative effects of health behaviors without necessarily discontinuing those behaviors entirely, have demonstrated effectiveness in promoting health for individuals using substances. Taking a harm reduction approach to discussing perinatal cannabis use with patients would involve delivering education and counseling about modifiable risk factors related to cannabis use in a way that aligns with philosophical principles of harm reduction (e.g., respect for patient autonomy). Despite multiple calls for harm reduction strategies, such as in the National Institute on Drug Abuse's priority area #2, medical providers in the US have not adopted a harm reduction approach towards the discussion of perinatal cannabis use with patients. The proposed study will address this gap by utilizing implementation science methodology to create a toolkit that enables US provider adoption of a harm reduction approach to the discussion of perinatal cannabis use. A key feature of this toolkit will be a Canadian evidence-based practice resource on harm reduction for perinatal cannabis use that we will adapt for a US audience. The study will involve key informants (providers, patients, and leaders in healthcare and public health) in the creation of this toolkit to ensure that it matches the needs “on the ground”. The study will take the following steps to build this toolkit: 1) conduct a core components analysis of the Canadian practice resource, 2) hold interviews with stakeholders to assess: a) understanding of harm reduction, b) determinants of adopting a harm reduction approach to perinatal cannabis use, and c) needed adaptions of the Canadian practice resource for a US audience, and 3) bring together information from the analysis and interviews to create a toolkit to support adoption of a harm reduction approach to US provider discussion of perinatal cannabis use. This toolkit will be prototyped, presented to providers for feedback, and refined in accordance with the feedback. In a future hybrid type II R01 study, the toolkit will be tested on its ability to: 1) impact frequency of discussion and provider skill in discussing perinatal cannabis use, and 2) increase US provider adoption of a harm reduction approach to the discussion of perinatal cannabis use.

NIH Research Projects · FY 2025 · 2024-07

Harnessing culturally-appropriate, technology-assisted methods to advance suicide prevention among youth in Colombian school settings PROJECT SUMMARY This study will investigate a multi-level approach that leverages innovative, culturally appropriate technology-assisted methods to reduce suicidal thoughts and behaviors (SIB) and promote mental health among Colombian youth: (a) digital platform for youth; (b) digital platform for teachers; and (c) hybrid mental health training diploma program for teachers. This approach will leverage school settings and technology- assisted methods to reduce gaps in access to evidence-based suicide prevention interventions in a low- and middle- income country. The open-access digital platform was developed through a co-design process with Colombian youth to ensure that it was culturally appropriate, engaging, and easy to use. Key features of the digital platform include mental health self-help tools, a customizable safety plan, links to online counseling services, and gamification elements. The digital platform’s teacher interface will incorporate psychoeducation, brief suicide-risk screening and decision support tools to enhance teacher capacity to assess risk for suicide and refer youth to the appropriate level of services. The Exploration, Preparation, Implementation, Sustainment (EPIS) framework, a broad multilevel, context-sensitive implementation science model, will guide the study. A hybrid-type 1 implementation-effectiveness stepped wedge pilot trial design will allow for evaluation of the digital platform and hybrid mental health teacher training program implemented within secondary schools in Bogota, Colombia. Study aims are to: Aim 1. Conduct an initial needs assessment with key stakeholders (youth, teachers, caregivers, school administrators) to identify implementation barriers and facilitators. A mixed methods evaluation will guide co- creation of a tailored implementation blueprint prior to the implementation phase. User-centered design methods will be used to refine the digital platform and incorporate end-user feedback into the final prototype. Aim 2. Evaluate the feasibility, acceptability, usability and preliminary effects of the digital platform implemented within school settings (N=3) in reducing SIB and improving mood (e.g., anxiety/depression) among Colombian youth aged 14-19 (N=150) via a mixed methods approach. Safety planning skills acquisition (youth-level) and linkages to mental health services (service-level) will be assessed as mechanisms of change. Aim 3. Implement a multi-disciplinary capacity building model to strengthen behavioral health and implementation science research, healthcare delivery capacity, and mental health policy. In partnership with Pontificia Javeriana University, experts in suicide prevention, data science, and implementation science will be engaged to build in-country research capacity at the individual and institutional level. Engaging with the Bogota Mayor’s Office and community advisors will help to better disseminate research findings to key stakeholders.

NSF Awards · FY 2024 · 2024-07

Navigating the Arctic Ocean presents daunting challenges due to its darkness, remoteness, and harsh conditions, but has nevertheless prompted human incursions spanning millennia. The ability to traverse the Arctic Ocean has become of increasing importance over time, supporting people living in the Arctic and the strategic development of commercial opportunities. Navigability – the capacity to support safe passage for vessels – is critical for all Arctic operations, from global shipping to local fisheries, from resource extraction to military deployment, from tourism to traditional lifeways. Key to the assessment of this maritime access is understanding and predicting the frequency and intensity of extreme weather in the Arctic. This project will use computational approaches to develop future scenarios for the conditions that lead to shipboard ice accretion and dangerous sea ice convergence. The results will have far-reaching impacts including assessing the utility of these scenarios for Arctic operational planning on decadal time horizons, developing approaches to emerging high resolution climate information that can be generalized to other applications, and training of early career scientists in these challenging computational techniques. To address the research goals, first, numerical weather prediction at daily resolutions will be used to develop a model of the probability along maritime shipping routes of (i) icing conditions leading to adverse operational impacts and (ii) sea ice convergence leading to dangerous ridging conditions. Second, the project team will develop and deploy machine learning techniques to enhance the spatial resolution of sea ice projections in geographically constrained segments of maritime routes, such as the heavily trafficked Arctic straits and emerging Canadian Archipelago routes. Third, these perspectives will be incorporated into future multi-model climate projections to develop more accurate scenarios of marine accessibility with regard to climate futures, jurisdictions, and routes. These generalizable insights will be developed on the use of machine learning to enhance the spatial resolution of climate model output variables for a range of applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The national interest depends on high-quality decision-making in both the public and private sector. Most important decisions are the result of group deliberation. Therefore, a theory of effective discourse is proposed called adversarial cooperation. The proposed process divides the cognitive labor of a group into two distinct phases: conjecture and refutation. During the conjecture phase, the group leverages its members’ unique knowledge and skills to generate hypothesized solutions to a problem. In the refutation phase, the group stress-tests each solution, with group members acting as either supporters of their own proposed solution or as antagonists of other solutions. The group achieves consensus for the best decision on the assumption that reasoners are better at evaluating hypotheses that they disagree with than their own. So as long as the conjecture phase generates enough distinct potential solutions, the refutation phase is likely to converge on the best solution. The proposed research program has four strands of behavioral experiments. In all cases, small groups of partisans are asked to collectively reason about politicized problems. The prediction is that the groups whose members are more adversarial that decide by deliberation will outperform 1) more ideologically heterogeneous groups that deliberate and 2) groups that decide via some other collective action mechanism, such as judgment aggregation. The first experimental strand examines reasoning that requires creative idea generation (i.e. conjecturing). The second and the third strands examine cases that require rigorous stress-testing of existing ideas (i.e. refutations). The second strand focuses on evidence-based refutations and the third strand on argument-based refutations. The final strand applies the proposal to judgment at a larger scale. Are larger groups that include people with adversarial perspectives able to make more accurate judgments and predictions than group lacking adversarial perspectives? 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 Health disparities for Latinas remain a persistent problem leading to increased incidence of chronic disease, comorbidities and premature mortality. The NIH has called for interventions that improve adherence to prevention regimens (NOT-OD-21-100) because of the potential to decrease health disparities by reducing disparities in meeting health guidelines. There has been little research into interventions to improve meeting and adhering to behavioral health guidelines, especially in understudied Latinas. Often the process of adopting new health habits is challenging for marginalized communities who have fewer resources and time to invest in healthy behaviors. In particular, stress can be a barrier to adopting new habits. This study aims to address the everyday stress faced in the general population of Latina women in their daily lives that can be interfering with their ability to adhere to prevention regimens such as meeting physical activity (PA) guidelines. Our research team has significantly improved total PA minutes and the number of Latinas meeting PA guidelines through rigorous research and theory-based interventions, delivered remotely with technology. Nonetheless, only 40% of women meet PA guidelines at 6 months. We have shown that participants who experience the stress relieving benefits of PA, regardless of baseline stress levels, are more likely to meet and adhere to PA guidelines and that Mindfulness-Based Stress Reduction (MBSR) can increase PA minutes, improve other health behaviors, as well as reduce stress. It is our goal to optimize our PA intervention by integrating the most potent ingredients from MBSR for those women who do not meet PA guidelines at 6 months. With an innovative SMART design, all participants, inactive Latinas aged 18-65 (n=258), will receive our evidence based PA intervention. At 6 months, participants will be objectively assessed for meeting PA guidelines. Women who meet the guidelines will continue to receive the standard intermittent PA for another 6 months. Women who do not meet the PA guidelines (»60%), will be randomized to continue intensive PA counseling or attention matched PA counseling incorporating the most potent components from MBSR adapted for PA. Participants in all arms will be followed and compared over 18 months. We will also rigorously evaluate stress as a mechanism for meeting PA guidelines using multiple measures to better assess the causal relationship including cortisol, self report, and daily Ecological Momentary Assessment and stress context. Finally, we will strengthen evidence for the longer term heart health benefits of meeting PA guidelines and reducing stress, by using laboratory based assessments of weight, blood pressure, HbA1c, and lipid profiles. This study has the potential to advance intervention science by optimizing an evidence based intervention to deliver greater improvements in health behaviors and health outcomes.

NIH Research Projects · FY 2024 · 2024-07

Image-guided thermal ablation (IGTA) is a minimally invasive, low cost, and accessible cancer treatment for patients including those who are too ill to be candidates for surgery or radiotherapy. However, it remains under- utilized due to relatively higher recurrence rates. This is likely due to inaccurate estimates of treatment zone boundaries. This research program proposes to address this challenge by applying advanced techniques in image analysis (specifically deep learning) to detect and mitigate potentially undetected incomplete treatment in liver tumor ablation through multiple stages of the procedure and follow-up period. These critical improvements will help broaden the applicability and increase the success rate of IGTA, while maintaining its many advantages. Specifically, we will develop a novel fully automated pipeline of kidney segmentation and registration based on deep learning that could reduce the impact of undetected incomplete treatment, improve years of cancer-free survival, and make IGTA a more attractive therapy for more patients. We hypothesize that 1) deep learning techniques can segment the kidney and the renal lesion in a manner indistinguishable from experienced radiologists and 2) deep learning can supplant biomedical modeling in generating deformation vector fields at a speed that is suitable for clinical application. The deliverables from our work would improve the treatment of renal tumor in several ways. First, the 3-dimensional assessment of delivered ablation zone based on pre-operative diagnostic quality images will establish “virtual margins” when the patient is still on the table and allow real-time adjustments by the operator to decrease recurrence rates. Second, the inclusion of the entire process within a single deep learning architecture will make a single, easily implementable program for the clinic. The proposed research is interdisciplinary, engaging clinicians and imaging scientists in a comprehensive effort to curate a large amount of high quality treatment imaging and to leverage this data in developing deep learning algorithms for segmentation and registration, and prediction strategies that are well-suited to this problem domain. The technology would facilitate identification of incomplete treatment in real-time and use pre-operative diagnostic quality images to improve accuracy in estimating the treatment zone, resulting in a decrease in the rate of post-treatment recurrence.

NSF Awards · FY 2024 · 2024-07

The Leadership Alliance at Brown University will convene a pilot Regional Conference on STEM Mentoring entitled the "STEM Diversity Drivers Conference" in the fall of 2024 at Johns Hopkins University. This will be a two-day regional conference that convenes roughly 100 in-person and 120 virtual undergraduate and graduate faculty as well as research professionals. Regional conference institutions participating include: Howard University, Johns Hopkins University, Morgan State University, University of Maryland-Baltimore County and University of Virginia. The specific aims of the conference are to: (1) increase awareness and discuss the barriers that underrepresented groups in STEM disciplines face in the STEM enterprise; (2) elucidate varied experiences that collectively contribute to increased identity, efficacy and persistence in the STEM workforce; and, (3) create a collaborative interdisciplinary and multi-sectoral community for near-peer mentorship models. Regional communities of scholars will leverage expertise and experiences to inform novel and exportable approaches to diversify the STEM research workforce. The Leadership Alliance is the recipient of the organizational Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM), the nation's highest award for STEM mentoring, and has over 30 institutional members. The Louis Stokes Alliances for Minority Participation (LSAMP) program assists universities and colleges in their efforts to significantly increase the numbers of students matriculating into and successfully completing high quality degree programs in science, technology, engineering and mathematics (STEM) disciplines in order to diversify the STEM workforce and supports the production of scholarly research in STEM broadening participation. Particular emphasis is placed on transforming undergraduate STEM education through innovative, evidence-based recruitment and retention strategies, and relevant educational experiences. These strategies facilitate the production of highly competitive students motivated to pursue STEM degrees and careers in STEM. For the United States to remain globally competitive, it is vital that it taps into the talent of all its citizens and provides exceptional educational preparedness in STEM areas that underpin the knowledge-based economy. 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 This collaborative project between Brown University and Beijing Normal University responds to RFA-MH-23-260 by developing and testing a multi-level intervention program (“Together We Can”) to engage adolescents (age 12-19), school teachers, and caregivers in suicide prevention in rural China. Suicide has become the top 1 leading cause of death among adolescents in China. In particular, adolescents in rural China – a population exposed to chronic poverty and resource deprivation – are at high risk of suicide. There is a lack of mental health literacy while stigma against suicide and seeking professional psychological help runs deep in rural China. No training exists for teachers and caregivers to recognize signs of suicide risk, normalize conversations related to mental health, and refer at-risk adolescents to resources. Further, the current psychological education in China lacks teaching applicable coping and help-seeking skills and stigma reduction on mental health and suicide. There is an urgent need to address this public health crisis in school settings and develop interventions that engage adolescents, teachers, and caregivers in the rural, low-resource context. Our research team has taken the first step to collectively developed and test a teacher-focused gatekeeper program (“Life Gatekeeper”) for teachers in rural China, with promising findings from a recent RCT with teachers. Building on our prior work and leveraging our existing relationships with schools in rural Guangdong, we aim to build the next building blocks of a universal intervention (“Together We Can”) by engaging adolescents (via socioemotional learning), teachers (via the existing “Life Gatekeeper” program) and caregivers (via adapting the “Life Gatekeepers” for caregivers). We will also incorporate technology-mediated support via existing mobile platforms to maximize scalability. By developing a low-cost, multi-level suicide prevention program and establishing a protocol with community- centered and implementation perspectives, the “Together We Can” program has the potential to achieve scalability and sustainability, if proven to be efficacious. The aims of this early phase, clinical project include (1) to conduct focus groups with caregivers, school teachers, and individual interviews with adolescents on needs related to suicide prevention on various levels, preferred intervention content, and implementation strategies for successful delivery, (2) to prepare the RCT phase by creating the multi-level “Together We Can” program, forming an implementation resource team, and conducting workshops and staff training, and (3) to evaluate the feasibility, acceptability, safety, and preliminary effects of the Together We Can program via a randomized controlled trial (RCT) with two schools in rural China (intervention vs. usual care). We will conduct assessment at baseline, 4-months, and 8-months follow-ups. We will also collect data on barriers and facilitators to implementation of the “Together We Can” program through surveys and exit-interviews. This developmental grant will provide essential data to guide subsequent application for a fully powered RCT with multiple schools in rural area and invest in capacity development for researchers and local professionals in China.

NSF Awards · FY 2024 · 2024-07

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Matthias Kuehne of Brown University is investigating the thermodynamics of water phase change under extreme nanoconfinement in pores with diameters on the order of 1 nm. The behavior of water in such nanometer-sized cavities is known to deviate strongly from bulk properties, yet prior studies of confined water have reported widely divergent phase transition temperatures for a given pore size. Professor Kuehne and his students will use a unique combination of photoluminescence spectroscopy, Raman excitation spectroscopy, electromechanical resonance measurements, and transient electrothermal methods to address this knowledge gap. Their studies will advance the fundamental understanding of the phase behavior, mass density, and heat capacity of water in well-defined cylindrical nanopores relevant to green energy technology and water desalination. The project outcomes are expected to ultimately benefit society by informing the engineering of advanced separation membranes and electrochemical devices, and by training the next generation of scientists and engineers in this important area of research. As part of the project's broader impacts, two educational videos will also be produced to engage the general public on topics related to fluids at the nanoscale. Professor Kuehne's research examines three key aspects of water thermodynamics under cylindrical nanoconfinement between 4-500 K. First, photoluminescence and Raman excitation spectroscopy will be used to precisely determine water phase transition temperatures in isolated carbon nanotubes based on changes in the nanotube's optical and vibrational properties. Second, the total and interfacial mass densities of the confined water will be measured by combining electromechanical resonance techniques with Raman spectroscopy. Third, a transient electrothermal method will be coupled with Raman spectroscopy to determine the volumetric heat capacity. The research leverages the Kuehne lab's expertise in fabricating carbon nanotube nanofluidic devices and combines advanced optical and electrical measurement techniques in an innovative approach. Together, these complementary techniques will enable more reliable mapping of the phase diagram of water in single-digit nanopores and extracting enthalpies of phase change. The results will aid in resolving contradictions in reported phase transition temperatures for a given pore size and in developing accurate molecular models of nanoconfined water. The experimental tools developed in this project will further pave the way for systematic investigations of other confined fluids and electrolytes beyond water in future work. 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: The fungus Candida albicans is a frequent commensal of the gastrointestinal (GI) tract but is also an important cause of both mucosal and systemic disease. The GI population is particularly relevant to human health as cells can translocate out of this niche to cause disseminated infections. C. albicans cells in the gut also play key roles in regulating local and systemic immune responses that can be either beneficial or detrimental to the host. There is therefore a pressing need to understand how C. albicans colonizes the GI tract and to define how changes to the commensal environment impact fungal behavior. Attention has focused on the ability of C. albicans to transition between yeast and filamentous states, with yeast-locked cells shown to exhibit higher fitness in the GI tract than wildtype cells. However, these studies have extensively relied on models that require antibiotic supplementation for stable fungal colonization. In preliminary studies, we examined C. albicans fitness in colonization models without antibiotic treatment. Surprisingly, we show that yeast-locked cells are defective for gut colonization in hosts containing high bacterial loads, including those colonized with defined bacterial consortia. Furthermore, we demonstrate that Candidalysin, the first toxin identified in a human fungal pathogen, is critical for gut colonization in hosts carrying high bacteria loads but not in those given antibiotics, indicating that this factor supports fungal commensalism by enabling competition with the bacterial microbiota. To build on these observations, we will examine how C. albicans morphology (and co-regulated genes) determine GI colonization fitness. Experiments will utilize a variety of murine GI models carrying native or defined bacterial populations to determine how interkingdom interactions influence fungal gut commensalism (Aim 1). We are particularly interested in determining how Candidalysin regulates fungal fitness in the GI niche, and whether this toxin acts intrinsically on fungal cells, to inhibit bacterial cells, or via its impact on host epithelial cells (Aim 2). Experiments will also perform fitness selection assays to identify novel factors that determine gut colonization fitness in hosts carrying different gut bacterial populations (Aim 3). Together, these experiments will provide novel insights into the fundamental mechanisms used by C. albicans to colonize the gut, including the role of Candidalysin toxin in increasing the fitness of fungal cells in the competitive GI niche. Given the importance of gut colonization to fungal-host interactions, these experiments are critical for understanding how C. albicans operates as a human pathobiont.

NSF Awards · FY 2024 · 2024-07

The swimming motion of bacteria represents a complex phenomenon that intersects the fields of microbiology and fluid dynamics. The movement of bacteria is vital for colonization and infectious pathogenic process, enabling them to exploit resources in diverse locations and escape from unfavorable conditions. However, a complete understanding of the mechanisms underlying bacterial swimming remains elusive due to the intricate interplay of physical and biological factors. To overcome these challenges, the project will leverage the emerging field of robophysical modeling, employing techniques from robotics to realistically emulate biological systems and unravel the influences of various physical and biological factors. The outcomes will advance the mechanistic understanding of the motility behaviors of one of the most ubiquitous and important life forms on Earth. The project will also broaden research participation by undergraduate students through the use of senior design projects and enhance fluid dynamics education by creating education films based on the project outcomes. The aim of this project is to advance the understanding of how fluid mechanical forces shape the ways bacteria navigate their complex environments and interact with each other. This project undertakes a convergence approach, fusing knowledge and techniques in fluid dynamics, microbiology, and robotics to achieve the goal. By adopting techniques of robophysical modeling, the project will isolate physical effects governing bacterial swimming from other uncontrollable, complex biological factors, enabling experimental studies of bacterial swimming with unprecedented flexibility and accuracy. Combined experimental and theoretical investigations will elucidate how different non-Newtonian rheological behaviors impact their locomotion, characterize bacterial swimming near surfaces, and examine the hydrodynamic interactions of neighboring swimming bacteria. The integration of theoretical modeling with direct experimental validations will critically assess existing and new hypotheses pertaining to the physical mechanisms responsible for experimentally observed swimming behaviors of bacteria in complex environments. The project outcomes will support the ultimate goal of transforming fundamental knowledge of bacterial motility into future biomedical and technological advancements. The education and training opportunities provided by the project will also educate future STEM workforce on disciplinary convergence. 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

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Due to genomic technologies, electronic medical records, and digitized high-throughput experiment readouts, building an independent biomedical research career requires fluency in both biomedical data generation and data science methods for analyzing large-scale biomedical datasets. This dichotomy is challenging to address in doctoral training; biology doctoral students may take quantitative coursework with no emphasis on biomedical data, while computational biology students often focus on one data type as end users. The objective of our Predoctoral Training Program in Biological Data Science at Brown University is to turn “I-shaped” predoctoral students — with strength in one discipline — into “pi-shaped” Biological Data Scientists with fluency in two languages: (1) generating biological data motivated by questions across a range of scales and systems, and (2) developing quantitative methods for modeling and testing hypotheses using large-scale biomedical datasets. The established Biological Data Science training community at Brown University has 34 engaged faculty mentors across multiple disciplines who will jointly and actively mentor a steady state of six NIH-supported predoctoral trainees during the first and second years of doctoral study (resulting in >30 Biological Data Scientists over 5 years) in a variety of didactic, research, and mentoring activities, as well as in research and professional development events that continue to foster interdisciplinary community for senior trainees. These activities will include coursework in inference for genomics and molecular biology, laboratory practicums, computational workshops, a year-long second-year graduate seminar focused on extensive peer review of methods for analyzing biological data, an annual program retreat, and a series of professional development events for interdisciplinary researchers. The resulting community will promote the development of professional skills essential for interdisciplinary biological data science research, including an emphasis on the ability to communicate science to both broad and field-specific audiences, navigate interdisciplinary collaboration and grant applications, interview for academic and industry-based research careers, and conduct reproducible and open biological data science research. The faculty mentors’ research programs cover multiple biological organisms, systems, and problems, ranging across biological and neuronal networks, computational biophysics, computer vision and visualization, evolutionary and statistical genetics, functional genomics, host-pathogen interactions, the microbiome, and the molecular biology of aging. These activities will expand successful activities funded under a previous NIGMS T32 FOA (now in year 5 of funding), resulting in the persistence of 23 trainees in biomedical research. These trainees have secured external fellowships and produced 42 peer-reviewed publications and 8 preprints under review thus far. The mentors have a combined annual research funding base of over $24 million in direct costs this year, offering a strong foundation to bolster this innovative interdisciplinary training program.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Making correct choices, and learning from them, requires a detailed understanding of biological needs. Such needs are governed by fluctuating drives (hunger, thirst) and by larger, underlying rhythms such as the Circadian Cycle. Forebrain circuits, and axonal inputs to them from the Ventral Tegmental Area (VTA), are crucial to computations underlying choice and learning. A key source of body state information for the Forebrain are circulating signals in the vasculature. However, the blood-brain barrier (BBB) is commonly reported to block almost all such signaling molecules to protect the brain from contamination. Our lab has been testing a potential resolution to this tension: Specifically, we have found that BBB permeability is dynamic, only ‘opening’ at behaviorally-relevant moments when the risk of contamination is worth the reward of higher quality state information. Our extensive Preliminary Data show that BBB permeability events occur in response to local VTA axon activity both endogenous and optogenetically-driven. I build on these new findings and my graduate studies, where I discovered substantial vascular dynamics between sleep and waking (Turner et al., 2020)1 and how these relate to indicators of arousal (Turner et al., 2023)2. In Aim I, I will test the hypothesis that VTA axon activity and local dopaminergic (DA) levels peak early in the active (dark) phase, and then decrease progressively. I will test this hypothesis with 1P and 2P imaging of VTA axon activity and DA signaling in Neocortex and Striatum, leveraging behavioral paradigms shown to drive distinct patterns of VTA axon activity (Hamid et al., 2021)3. In Aim II, I will test the hypothesis that VTA-driven BBB permeability events show the same relationship to Circadian Cycle. Specifically, that single axon spikes and optogenetic input drive larger amplitude permeability events early in the active (dark) phase followed by a decreased relative impact across this phase. In these studies, I will test endogenous and optogenetic-driven VTA axon activation while imaging local BBB permeability events and systematically testing that VTA-driven BBB permeability events allow the transmission of multiple active signaling molecules including sex hormones and metabolic cues. In Aim III, I will test the hypothesis that integration of prior reward history will have the strongest impact early in the active (dark) phase. In these studies, I will employ a two- armed bandit task in conjunction with fiber photometry recordings of DA levels in SI, mFC, DS, and NAc. I will fit this data into behavioral computation frameworks developed in my co-sponsor Dr. Michael Frank’s laboratory. These studies will generate unique data that tests how a major determinant of behavioral state, the Circadian Cycle, relates to VTA axon activity, rapid changes in BBB permeability, and behavior itself. Further, these studies are ideal for my training, directly supporting my central career goal of understanding how dynamic neural-vascular interactions reflect and contribute to behavior.

NSF Awards · FY 2024 · 2024-06

Electronic Design Automation (EDA) tools enable the design, analysis, and manufacturing of chips at an unprecedented scale. However, these tools are limited by their reliance on heuristic-based optimization and expert knowledge, which can slow down the chip design process, sometimes taking months or even years. To address this inefficiency, this project introduces an initiative to enhance design productivity by integrating Machine Learning (ML) techniques into EDA optimization algorithms. This integration aims to improve both the performance and speed of these algorithms, potentially revolutionizing the way chip designs are completed. The broader impacts of this project include extensive educational outreach and industry transfer efforts in line with the CHIPS and Science Act, aiming to enhance semiconductor research and workforce training in an Estab­lished Program to Stim­u­late Com­pet­i­tive Research (EPSCoR) state. The proposed ML-EDA co-optimization framework seeks to standardize the application of ML in core EDA combinatorial optimizations across various scenarios. By developing numerical embeddings of chip design instances at every stage of the EDA process, these embeddings are utilized as input features for ML-based optimizations. This setup allows for optimizations that are tailored to specific design instances, leveraging three innovative ML engines: deep metric learning for optimal hyperparameter selection, reinforcement learning for sequential decision-making in EDA tools, and a back-propagation approach that restructures combinatorial EDA problems into graph-based computing models for faster, more effective solutions. 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

This study will assess how the design of transitional justice institutions in countries affected by conflict and/or authoritarianism affects public opinion, thereby shaping long-run national outcomes including regime type and conflict recurrence. Existing literature on the effects of transitional justice is inconclusive; such work is divided between micro-level, single-country studies and macro-level, cross-country studies as well as between studies of post-authoritarian and post-conflict contexts. By collecting comparable survey measures across countries that vary along several theoretically relevant dimensions and by building a cross-national dataset, this project will bridge these gaps. Further, the researchers will develop and test a novel theory about how public attitudes mediate the relationship between transitional justice institutions and long-run outcomes. Given the widespread use of transitional justice in post-conflict and post-authoritarian countries as well as democracies around the world, there is an urgent need for the field of transitional justice studies to develop a stronger base of evidence for policymaking. This project will shed light on which forms of transitional justice effectively promote peace, democracy, justice, and reconciliation as well as provide insights into how countries’ unique histories may shape the impact of transitional justice there. This project will investigate three questions. First, which kinds of transitional justice do people see as more legitimate? Second, how do people’s perceptions of the legitimacy of transitional justice institutions affect their broader political attitudes concerning the government, principles of democracy and autocracy, and out-groups? Third, do transitional justice institutions perceived as more legitimate contribute to stability following conflict? To address these questions, the research team will draw on their extensive thematic, regional, and methodological expertise to employ a mixed-methods research design which combines survey experiments, qualitative evidence collected from focus groups and elite interviews, and original cross-national data. This approach will leverage the strengths of diverse methodologies by combining (A) micro-level survey and qualitative data that will enable the investigators to assess the effects of varying transitional justice processes on individual attitudes with (B) macro-level cross-national data that will provide insight into the effects of institutions and historical context on national-level outcomes. 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 Alcohol misuse and its consequences peaks during emerging adulthood (the period of life between the ages of 18 to 25). Concurrently, during this time, many individuals head into the workforce. While previous research has documented that there are certain occupations that are associated with alcohol misuse, little is known about how newcomers’ alcohol use changes when entering these occupations that are characterized as high-risk for alcohol misuse. Guided by the organizational socialization literature and social learning theory, the proposed project aims to provide a better understanding of the transition into these high-risk occupations for alcohol misuse and to examine how the social environment influences alcohol use. A national sample of emerging adults (N= 400) before they start at high-risk occupations for alcohol misuse will be recruited. The five high-risk work occupations for alcohol misuse are the following: 1) construction and extraction, 2) installation, maintenance, and repair, 3) food preparation and serving related, 4) transportation and material- moving, and 5) sales and related occupations. Participants will be followed over 2 years, in which they will complete 7 assessments and 5 one-week “bursts” of daily diary assessments. Participants will report on the characteristics of the work environment, including the amount of alcohol use among coworkers and managers at work, and their own alcohol use. Findings from this study will inform prevention and intervention efforts to reduce heavy drinking and alcohol-related consequences. High-risk work environments for alcohol use could provide training on alcohol use, which would be beneficial to the organization (e.g., by reducing absenteeism, turnover, etc.) and the individual (e.g., reducing alcohol-related consequences). Furthermore, most prevention efforts for young adults occur in college settings and ignore those who head directly into the workforce thus putting those that head directly into the workforce after high school at risk.

NIH Research Projects · FY 2025 · 2024-06

PROJECT SUMMARY Autism spectrum disorder (ASD) affects 1 in 36 people and is a highly heterogeneous condition that has limited therapeutic interventions and affects males more often than females. Similarly other disorders like Attention deficit hyperactive (ADHD), Tourette syndrome (TS) and Schizophrenia (SCZ) have sex-specific differences in their prevalence and clinical presentation. However, the precise cellular and molecular mechanisms that underlie sex-specific dimorphisms associated these neuropsychiatric disorders are understudied. Our long- term goal is to define the mechanisms that underlie sex-specific differences related to neuropsychiatric disorders. Human genetics studies have identified high risk variants in genes that encode chromatin regulatory factors associated with ASD, TS, ADHD, and SCZ. Our studies focus on the histone methyltransferase ASH1L which has been identified as a gene of high importance that raises above genome wide significance in large sequencing studies of ASD, TS and SCZ. We find that boys with mutations in ASH1L present more often with ASD and ADHD, while girls with ASH1L mutations are more likely to present seizures. However, the molecular mechanisms underlying sex-specific differences associated with deficits in ASH1L are unknown. We developed a high-risk, high-reward research program that brings together in-depth clinical neuro-phenotyping and stem cell pre-clinical studies to uncover mechanisms underlying sex-specific differences associated with mutations in ASH1L. Our Central hypothesis is that ASH1L modulates transcriptional programs that govern neuronal structure and function, and that sex-specific post-translational histone modifications lead to genome- wide alteration of molecular programs that underlie male and female phenotypic differences in ASH1L related disorders. We will test this hypothesis in the following specific aims: Aim-1 will define the phenotypic diversity and severity of neurological presentations associated with ASH1L disease with respect to sex-specific differences. Aim 2 will define sex-specific cellular and molecular phenotypes caused by ASH1L dysregulation in human neurons derived from male and female individuals with ASH1L mutations. The impact of our work is that defining the clinical diversity and severity of ASH1L neurological phenotypes in a male and female specific manner will be important for patients, families, and their physicians with respect to medical treatment. Our work with patients, will allow us to uncover sex-specific differences clinically and mechanistically using patient iPSC-derived neurons, which will allow us to identify targets for therapeutic interventions. Finally, our work will also allow us to begin to prepare the ASH1L patient community for larger scale clinical studies.

NIH Research Projects · FY 2025 · 2024-06

NMR Fragment-based Design of New β-lactamase Inhibitors. SUMMARY A rapid and widespread increase in antimicrobial resistance over the past few decades has seriously threatened our capability to treat bacterial infections that may persist following treatment with last-resort antibiotics such as carbapenems and polymyxins. Given bacterial β-lactamase enzymes can degrade β-lactam antibiotics, β- lactamase inhibitors have been widely sought to improve the efficacy of this antibiotics class. Since the majority of recently approved antimicrobial agents for Gram-negative pathogens are β-lactam + β-lactamase inhibitor combinations, the validity of this approach is widely accepted. However, microbial β-lactamases are constantly evolving into new forms that can evade the activity of β-lactamase inhibitors. Nuclear magnetic resonance (NMR) spectroscopy can generate high-resolution structural and dynamics information on proteins like β-lactamases and map atomic details of interacting chemical entities like β-lactamase inhibitors. These structural details create new opportunities such as fragment-based drug discovery (FBDD). These techniques are at the forefront of many research programs and have proven successful in antimicrobial drug development. The fragment-based approach is distinct from high-throughput screening of drug libraries and has yet to be applied to β-lactamase inhibitor development. Here our objective is to expand the use of FBDD and discover new chemistry by designing inhibitors against β-lactamases TEM-1, SHV-1, PDC-3, and OXA-40. The specific goals for this project are to develop new inhibitors based on the diazabicyclooctane scaffold and to generate detailed NMR maps of this set of β-lactamases. Backed by our preliminary data that has identified 69 TEM-1 interacting fragments, our multi- faceted approach relies on structural biology, medicinal chemistry, and microbiology approaches to advance the fragment-based methodology for the identification of new β-lactamase inhibitors. These inhibitors will be designed around a drug scaffold that targets the active site of broad-spectrum β-lactamases, therefore our approach should result in the identification of potent inhibitors active against a broad spectrum of resistant bacteria. Moreover, we will perform NMR relaxation studies to explore allosteric mechanisms in β-lactamases. Public Health Impact: The World Health Organization has given the highest priority to anti-microbial research on the Gram-negative bacteria genera Acinetobacter and Pseudomonas, as well as specific species of Enterobacterales in which extensively-drug resistant strains are increasingly emerging. These resistant strains can cause systemic infection and may not respond to known antibiotics that are rendered ineffective due to specialized enzymes produced by the bacteria that degrade and thereby confer resistance to important classes of antibiotics such as β-lactams. The goal of this project is to develop a structure-based methodology to devise inhibitors against a model β-lactamase and then extend the methodology to other enzymes synthesized by extensively-drug resistant bacterial strains. The efficacy of our inhibitors will be tested in conjunction with known β-lactam antibiotics using clinical bacterial isolates and appropriate infection models.

NIH Research Projects · FY 2026 · 2024-06

PROJECT SUMMARY Locus coeruleus (LC) dysfunction and degeneration occurs early in Alzheimer’s Disease (AD). LC degeneration also occurs early in other AD-related neurogenerative disorders, including Down Syndrome (DS) and Parkinson’s Disease (PD). Substantial evidence links LC degeneration in these conditions with clinically meaningful measures of disease progression. While studies support mitochondrial and metabolic mechanisms causing LC vulnerability in AD and related neurodegenerative disease, the mechanisms are poorly understood. My laboratory has new, unique data demonstrating early and selective LC degeneration in a mouse mutant for the mitochondrial enzyme Glutamate Pyruvate Transaminase 2 (GPT2). While several mouse models for AD, DS and PD show LC dysfunction and degeneration, strikingly, the Gpt2-null mouse shows the earliest LC degeneration (by postnatal day 18) of any mouse mutant thus far described. Importantly, recent data also support a link between GPT2-mediated metabolism and AD. Therefore, the study of GPT2-mediated mechanisms in LC health and degeneration provides an important opportunity to understand mechanisms of early LC vulnerability with broad significance to AD and related neurodegenerative disorders. The overriding objective of this R01 application is to define early metabolic mechanisms of LC vulnerability in Gpt2-null mice, and also in AD and DS mouse models, across the lifespan, including in gene-by-environment (GXE) experiments using an extended wakefulness paradigm. Our central hypothesis is that the vulnerable LC – across neurodegeneration mouse models – will exhibit signatures of defective metabolic mechanisms that will be apparent early, prior to LC neuronal death. In Aim 1, we will define the transcriptomic and metabolomic signatures of vulnerable LC neurons in the Gpt2-null mouse and in AD and DS mouse models, preceding and during neuronal death. In Aim 2, we will determine the cell-type specific requirements for Gpt2 in LC through conditional mutagenesis of Gpt2 in LC noradrenergic neurons or in glia. Finally, in Aim 3, we will define the extent to which Gpt2 mutation (in the Gpt2 heterozygote, Gpt2+/-) enhances LC vulnerability in adult brain, using provocations such as an extended wakefulness paradigm, or mating the Gpt2+/- mutation to AD mouse models. Sleep is an important brain function regulated by LC that has been implicated in disease progression in AD. In our preliminary data, we observe accelerated LC degeneration in adult Gpt2+/- mutant brain after provocation using an extended-wakefulness paradigm. Overall, the research in this R01 application will have a sustained impact because our finding of early LC neurodegeneration in the Gpt2-null mouse represents an important opportunity to advance our understanding of the early metabolic mechanisms of vulnerability in LC neurons. Because metabolite supplements that augment these GPT2-mediated mechanisms are available, this research may lead to important new strategies for preventative treatments for LC degeneration in AD/ADRD.

NIH Research Projects · FY 2025 · 2024-06

Project Summary Dr. Rosedahl’s long-term career goal is to understand how vision operates in tasks that involve the interaction between multiple visual processes such as category learning, visual perceptual learning, and visual attention. This knowledge could be used to design better training paradigms for visual tasks and increase the efficiency of visual rehabilitation training. In this project, Dr. Rosedahl will examine how category learning and attention induce visual perceptual learning transfer. Visual Perceptual Learning (VPL) is long-term improvement in visual tasks like telling the difference between the angle of two lines or detecting the presence of stripes. VPL is one of the most promising methods to improve vision in individuals with visual impairment. Unfortunately, research has found that VPL is very specific to the trained task. This specificity greatly limits the use of VPL in training paradigms for visual rehabilitation and motivates the goal of this work: to understand the mechanisms by which VPL can transfer to untrained features and visual field locations. This work focuses on understanding two paradigms that cause VPL to transfer: Category-Learning Induced Transfer of VPL (CIT-VPL) and double- training. To understand the neural mechanism behind transfer in these paradigms and prepare for his independent career, Dr. Rosedahl will receive training in technical and career skills at Brown University. He will be mentored in visual perceptual learning by Prof. Takeo Watanabe, deep neural network models of vision by Prof. Thomas Serre, and brain imaging techniques by Prof. Yuka Sasaki. Dr. Rosedahl will then build a unified model of category learning, visual processing, VPL, and feature-based attention (Aim 1). Dr. Rosedahl will use this model (CAPL) to interpret the results of four experiments to determine if feature-based attention is causing transfer in CIT-VPL (Aim 2). The experiments will measure performance improvement, neural activation using functional Magnetic Resonance Imaging, and changes in neurotransmitter concentrations using Magnetic Resonance Spectroscopy. Dr. Rosedahl will also receive training in essential skills to facilitate the transition to his independent research career, such as grant writing, manuscript preparation, oral communication, lab management, and preparing job application materials. After securing his independent position as an assistant professor, Dr. Rosedahl will expand CAPL to include spatial attention (Aim 3) and use the improved model to interpret experiments testing whether feature-based attention and spatial attention combine to cause transfer in double-training (Aim 4). Overall, the work proposed here will establish a novel unified model of category learning, attention, and VPL (CAPL) and provide insight into the mechanisms of VPL transfer, knowledge that is currently lacking. CAPL will be a valuable resource for the broader scientific community to study visual learning. Additionally, the knowledge gained here could yield novel insights into optimizing VPL training paradigms, providing critical information for developing effective visual rehabilitation methods.