# data[].attributes.title
Collaborative Research: Transport of model-virus through the lung liquid lining

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The novel coronavirus SARS-CoV-2, responsible for the COVID-19 pandemic, is similar to other respiratory coronaviruses, such as SARS-CoV (2002) and MERS-CoV (2012). All these viruses cause dangerous respiratory disorders with high mortality and grave impacts on society. This virus destroys the cells that produce lung surfactants which, among other things, keep the alveoli air-sacks from collapsing, and eventually the lungs fill with liquid. The two primary functions of lung surfactants are regulating the interfacial tension and surface viscosities of the liquid lining of the alveoli, and providing a first line of immune defense against airborne pathogens.  The fluid dynamic interactions between the liquid lining of the lung, the lung surfactants, and the respiratory virus are presently not well understood. This project addresses this gap by conducting experiments and numerical modeling to capture the essential fluid dynamics of a model virus interacting with the primary insoluble component of lung surfactant. The expansion and contraction of the alveoli will be modeled using an open cavity with oscillatory sidewalls that expand and compress the liquid layer. The numerical models will then be used to simulate physiologically relevant scales, not accessible experimentally.  Aside from the flow in the lung liquid lining, the present knowledge gap in predictive modeling of interfacial dilation and compression is hampering developments in other areas, such as in water waves, which are of utmost importance in modeling of carbon dioxide gas exchange between the atmosphere and the oceans.Predictive models for the transport of small particles in a surfactant-covered liquid layer will be developed. A key advancement in the proposed modeling of surface elasticity is to measure the equation-of-state of the monolayer in a state corresponding to that found when it has been subjected to a large number of dilation/compression cycles. The usual approach of determining properties of a recently spread monolayer undergoing slow compression is inappropriate for modeling monolayer hydrodynamics coupled to an oscillatory bulk flow, as the monolayer is in a different state with very different interfacial properties. The role of interfacial dilatational viscosity and its significance relative to surface elasticity remains poorly understood and presents a major impediment to the predictive modeling of free-surface flows. The PIs have a proven track record of productive multidisciplinary collaboration, and will continue to provide a unique educational opportunity for the graduate and undergraduate students.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.

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NSF-BSF: Willingness to Vaccinate Against COVID-19: Psychological Mechanisms and Ways to Increase Responsiveness

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The COVID-19 pandemic has claimed the lives of over 4 million people worldwide, and over 190 million people have been affected in varying degrees of severity. The vaccine against the coronavirus has dramatically reduced the number of infected people, saving the lives of millions. However, although the vaccine has been proved to be highly effective, and its safety profile is satisfactory, in most countries there is still a relatively large percentage of people who are opposed, who endanger the entire population in their country as well as worldwide. Since COVID-19 continues to pose a threat to humans, it is essential to understand what causes this resistance and to find ways to increase vaccination rates. This research relies on previous works on terror management theory, as well as on insights from recent work on people's willingness to donate organs after death, to suggest psychological mechanisms that may explain people’s resistance to the vaccine, and to offer effective interventions to increase vaccination rates.Given the necessity of getting more people vaccinated, understanding the reasons behind peoples’ negative attitudes towards Covid vaccines and their reluctance to be vaccinated is of great importance. This research analyzes the multiple factors that may influence people’s attitudes toward the coronavirus and their decisions to be vaccinated against COVID-19 or not. The study entails 11 experiments reflecting two research directions: The first (Part I) examines the influence of different descriptions of COVID-19 on people’s attitudes and willingness to be vaccinated. Manipulations to increase thoughts of life saving (rather than death) may override defense mechanisms that might create negative attitudes toward the vaccine, thus increasing willingness to vaccinate. Additional studies manipulate the status quo (making the decision not to vaccinate a deviation from the default) and examine the effect on risk perceptions and on increasing willingness to vaccinate. Part II of the studies focus on individual differences in fears and beliefs that are hypothesized to play a major role in people’s decisions about vaccination, including the fear of death, religious beliefs, belief in a just world and belief in tempting fate.This project is being supported by a partnership between the National Science Foundation and the U.S.-Israel Bi-national Science Foundation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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RUI: Mathematical Modeling of Immune Response to SARS-CoV-2

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Mathematical and Physical Sciences (MPS)

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This research investigates the human immune response to SARS-CoV-2 virus to elucidate the key mechanisms responsible for disease severity exhibited by some COVID-19 patients. Despite a significant volume of clinical and experimental studies for the detailed mechanisms of SARS-CoV-2 virus, there is a lack of understanding about the host immune response to the virus, which is largely responsible for the variability in disease severity. To accelerate and supplement our understanding of key target pathways in the immune response, this project will develop and analyze a fundamental, comprehensive model for the host immune dynamics of SARS-CoV-2. Given we continue as a nation under pandemic conditions with new variants emerging and vaccination rollouts, the theoretical explorations through mathematical modeling will serve as a complement to lab-based and data-based approaches. Other features of this work include student involvement in this research, development of a network of collaborators across three institutions, curricula development, recruitment of students from underrepresented groups, and efforts to bring a broad community of researchers studying the host immune dynamics of COVID-19 together to advance our understanding of the interactions between the immune system and SARS-CoV-2. This project aims to accomplish two specific research goals: (i) development of a mathematical model of the host immune dynamics of COVID-19; and (ii) exploration of the model to address important COVID-19 treatment-related questions. For the first goal, the PI will develop and analyze a mathematical model that explicitly represents the virus, immune cells, cytokines, and their interactions, formulated in a system of coupled ordinary and delay differential equations. The main objective is to obtain a better understanding of key aspects of immune response to SARS-CoV-2, specifically its sensitive pathways. For the second goal, the PI will investigate the importance of timing of specific immune responses in disease severity and divergent outcomes, and the emergence of the so-called cytokine storm, excessive production of proinflammatory cytokines in the immune system. The aim is to identify the key mechanisms responsible for disease severity, which could help to identify other pathways to target therapeutically. The primary tools to be used for this project are model parameterization using a series of clinical and experimental data, sensitivity analysis, and numerical simulations. The primary mathematical contribution is the development of computational techniques to analyze high-dimensional nonlinear dynamical systems. In addition, the results from this study on the mechanisms involved in COVID-19 pathology and identification of several therapeutic targets would provide hypotheses to be clinically tested, thus, serving as a foundation for the development of evidence-based treatment protocols to address the global challenge.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.

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CIF: Small: Group Testing for Epidemics Control

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Computer and Information Science and Engineering (CISE)

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This project develops testing and intervention (quarantine) methods in the presence of a pandemic. COVID-19 has revealed the key role of  epidemiological models and testing in the fight against disease spreading. For any new virus or variant of the existing ones, society will always need to be able to expeditiously deploy strategies that allow efficient testing of populations and empower targeted interventions.  Group testing is a method that has recently attracted attention for efficient testing, as it allows to identify the infected individuals in a population with many fewer tests than the ones needed to test everyone individually. A main new observation in this project is that viral diseases like SARS-CoV-2 are governed by community spread, and taking into account (even partial) knowledge of  the community structure in an epidemics model (e.g., the distribution of students in classes of a school) can make such testing much more efficient and effective.Preliminary results indicate that it is possible to estimate the infection spread and evaluate the impact of interventions using a much smaller number of tests than  traditional techniques.Accordingly, the goal of the project is to leverage community structure and epidemic dynamics to enable real-time estimation of infection and intervention with the following attributes: (i) it is robust to model uncertainties; (ii) it offers provable theoretical performance guarantees and (iii) it achieves low complexity of operation. To do so, the proposal combines tools from coding theory and control, and proceeds in two steps. First, assuming complete and perfect knowledge of the true underlying dynamical model, it derives test designs and intervention strategies, as well as fundamental bounds on the number of tests and amount of intervention, for both a static and state-estimation problem formulation. Building on this first step, the proposal then considers approximations to the dynamic models either because the exact dynamics are not perfectly known, or for complexity-reduction reasons. In particular, the proposal develops approximations on the evolution of marginal probabilities for popular epidemic models, derives and analyzes discretized models, explores the effect of parameter uncertainty and investigate decomposable community models; in all cases, the goal is to understand how these approximations provably affect the associated fundamental bounds, test designs, and intervention strategies to contain the disease.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.

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RAPID: The Impact of COVID on Childrens Well-being in 2022: Continued Evidence from the Understanding America Study

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The COVID-19 pandemic has been a tremendous disruption to the education of U.S. students and their families, and evidence suggests that this disruption has been unequally felt across households by income and race/ethnicity. While other ongoing data collection efforts focus on understanding this disruption from the perspective of students or educators, less is known about the impact of COVID-19 on children’s prek-12 educational experiences as reported by their parents, especially in STEM subjects. This study will build upon the team's prior research from early in the pandemic. Researchers will continue to collect data from families and aims to understand parents’ perspectives on the educational impacts of COVID-19 by leveraging a nationally representative, longitudinal study, the Understanding America Study (UAS). The study will track educational experiences during the Spring and Summer of 2022 and into the 2022-23 school year. The team will analyze student and family overall and for key demographic groups of interest as schooling during the pandemic continues. This RAPID project allows critically important data to continue to be collected and contribute to continued understanding of the impacts of and responses to the pandemic by American families.Since March of 2020, the UAS has been tracking the educational impacts of COVID-19 for a nationally representative sample of approximately 1,400 households with preK-12 children. Early results focused on quantifying the digital divide and documenting the receipt of important educational services--like free meals and special education services--after COVID-19 began. This project will support the continued targeted administration of UAS questions to parents about students’ learning experiences and engagement, overall and in STEM subjects.  The team will conduct data analysis and disseminate findings and results to key stakeholder groups. Findings will be reported overall and across key demographic groups including ethnicity, disability, urbanicity, and socioeconomic status. This project will also produce targeted research briefs addressing pressing policy questions aimed at supporting intervention strategies in states, districts, and schools moving forward.  All cross-sectional and longitudinal UAS data files will be publicly available shortly after conclusion of administration so that other researchers can explore the correlates of, and outcomes associated with, COVID-19.This RAPID award is made by the DRK-12 program in the Division of Research on Learning. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.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.

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Forced Displacement and Community Resilience: Housing Insecurity under COVID-19 in Inland Southern California

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The unique nature of the COVID-19 pandemic created a disaster situation that highlights the importance of stable housing, particularly as recent evidence suggests that eviction increased the risk of COVID-19 infection and mortality. This study will improve knowledge of processes and consequences of evictions before and after the COVID-19 pandemic in Inland Southern California. The first goal is to analyze the demographic and socioeconomic profile of renters who recently experienced an eviction, as well as the relocation process and outcome. The second goal is to analyze how community resilience and neighborhood change, such as neighborhoods that are gentrifying or becoming more impoverished, are tied to outcomes for renters. The third goal is to evaluate whether and how these outcomes are changed by the pandemic. This study will advance our understanding of involuntary residential choices under an external shock like a pandemic. It contributes to resilience scholarship and helps us understand why the root cause of socioeconomic disadvantage is the primary source of vulnerability under disastrous events, and how housing security interacts with community resilience. As eviction is linked to social, economic, and health disparities, and urban poverty, effective eviction-prevention initiatives could go a long way toward addressing these enduring problems. This study provides evidence for policy interventions designed to address eviction and stem its consequences. It will also provide significant implications for practice and policy in housing markets and social welfare to alleviate social and spatial divides by race, ethnicity, and class that have been exacerbated by the pandemic disruption. This study investigates formal and informal eviction and neighborhood change before and after the COVID-19 pandemic in Inland Southern California using a multiscalar, comparative, and mixed-methods framework. Using both public and restrictive datasets, this study will model the prevalence of eviction and the threat of it at both the household and neighborhood levels. As residential mobility shapes the future life course of evicted households and neighborhood dynamics, the team will model the residential choice of evicted renters and neighborhood dynamics. Further, the project conducts in-depth interviews with tenants, landlords, real estate agents and housing developers, non-profit organizations, and government officials to examine the pathways through which individual characteristics, neighborhood environment, and institutional forces contribute to eviction. The multiscalar, mixed-methods and comparative framework will advance knowledge on the process of eviction at the household level, as well as neighborhood dynamics, policy interventions, power relations, and the coping process of local communities during a pandemic-like disruption. Findings from this study will not only directly benefit policymaking and practice in this region, but also contribute to knowledge in the field for national 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.

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REU Site for Oregon Marine Science: From Upper Estuaries to the Deep Sea

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Oregon State University’s (OSU) will host the Research Experience for Undergraduates (REU) Site called “From the Upper Estuaries to the Deep Sea” at the Hatfield Marine Science Center (HMSC) campus in Newport, Oregon. The REU program directly involves undergraduates in all aspects of marine science research from project inception to data collection, analysis, and interpretation, to presentation and publication of research results to both scientific and general audiences. HMSC will support cohorts of 16 interns, 10 traditional onsite and 6 new positions for remote-hybrid interns during a ten-week summer program that takes advantage of the wide range of marine science research opportunities available at HMSC and the experience successfully supporting remote and remote-hybrid interns during the Covid-19 pandemic. The goal is to develop an active inclusive community of students engaged in the interdisciplinary nature of marine science as a collective educational experience. Major objectives of the REU program are to: 1) provide students an opportunity for the highest quality research experience in marine science and an understanding of its interdisciplinary nature; 2) provide students with a professional development training opportunity and exposure to a wide range of marine science pathways leading to STEM careers; 3) increase participation in and access to marine sciences for traditionally under-represented students and nontraditional students through targeted recruitment efforts at regional community colleges and non-research universities serving underrepresented groups; and 4) adapt and learn from the successful support of a virtual cohort research experience during COVID-19 in summer of 2020 and 2021. Students will pursue research projects that fall within three major themes: Theme 1: Biology and Ecology of Marine and Estuarine Organisms; Theme 2: Ocean and Estuarine Ecology and Biogeochemistry; Theme 3: Physics of Estuarine and Ocean Environments: including projects in physical oceanography and estuarine science.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.

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Expanding the Cell Science and Immunological Testing Workforce by Developing a Diverse and Inclusive Credentialed Biotechnology Program

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The life sciences/biotechnology sector has continued to remain resilient during the COVID-19 pandemic, with the Los Angeles region generating $60.8 billion in economic activity in 2020 and hosting more than 1,000 life science innovation companies. It is projected that 16,000 regional technical jobs will be added to this rapidly growing sector within the next three years. The acceleration of the widening supply-and-demand gap, along with the growing awareness that community colleges produce competitive and highly skilled technicians, emphasizes the necessity to develop projects that focus on life sciences/biotechnology technician education to prepare students to become the next generation of highly skilled workers in this dynamic sector. Therefore, this project focusing on Cell Science and Immunological Testing will recruit and train 30 students from diverse and traditionally untapped pools of talent, including system impacted and justice involved students, resulting in 30 industry internship matches and award up to 60 certificates that are part of a career education pathway consisting of two stackable certificates. The project will align academic offerings with industry needs based on the input from an advisory council. Students will be trained in a curriculum that focuses on essential knowledge, state-of-the-art technical skills, and industry-required soft skills. Students will also receive an introduction to nanobiotechnology concepts and their applications in the cell science/gene therapy and immunological testing industries.In this project, Santa Monica College (SMC) will: 1) produce two stackable certificates that will enable students to successfully enter the rapidly growing life sciences/biotechnology industries in the greater Los Angeles region, 2) expand outreach, recruitment, and retention efforts to students from traditionally untapped pools of talent and communities, and 3) grow a diverse and talented workforce while reducing the training, mentorship, and employment equity gaps often associated with the life science/biotechnology industry. Students benefiting from this project will complete 22 units (5 courses) to earn their first stackable certificate and can opt to complete 15 additional units (4 courses) to obtain a second certificate. Regardless of their academic pathway, all participating students will receive an industry appointed mentor and complete an internship. The scope of this project also aims to inform 40 SMC students from special counseling programs, 150 pre-college educators, and four full-time SMC career and academic counselors about the biotechnology industry through planned outreach activities. Finally, mobile biotechnology exploration days will provide 210 pre-college students who are justice-involved or from low-income communities with hands-on activities, information about this project, and employment opportunities available to them in the life sciences/biotechnology sector. The results generated from this project will be disseminated through regional and national conferences, NSF ATE Center platforms, and the California Community College network. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the Nation's 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.

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CAREER:  Discourse Processing and Content Generation for Document Simplification

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Computer and Information Science and Engineering (CISE)

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Simplification is the process of making a text more accessible to a target audience, e.g., language learners, children, and individuals with language impairments, while preserving its meaning and content. The lack of accessible material can exacerbate social issues, for example, the complexity of language used in college admission and financial aid applications has contributed to the lagging access to higher education among emergent bilingual students; the WHO has recognized the urgency of accessible technical information, given the rise of medical misinformation especially in the wake of the COVID-19 pandemic. While there has been much work on sentence simplification, very few datasets are large enough to train supervised models; simplifying a document also involves different operations from those at the sentence level, including content addition, and how sentences connect with each other. This project aims to develop new resources and data-driven approaches for document simplification, with the potential to address information transparency and fair access across a range of high-stake domains. This project will also support the education and training of a diverse body of undergraduate and graduate students across disciplines.To substantially advance document simplification, this CAREER project will tackle several key issues in existing simplification work, including corpora diversity, explanation generation, and document-level approaches. This is achieved by the following research activities: (1) introducing new corpora that tackle the pressing challenge of data diversity in simplification research and enable new application scenarios, especially in the accessibility of technical and jargon-laden texts; (2) tackling content addition and elaboration during simplification---a previously little-explored challenge, and propose a novel, linguistically-informed framework that characterizes and generates elaborations; (3) develop models for document simplification that are informed by structures of discourse, using both coherence structure and entity salience. The innovative ways to integrate discourse target a larger challenge for models to take stretches of discourse into account.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.

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RAPID: #COVID-19: Understanding Community Response in the Emergence and Spread of Novel Coronavirus through Health Risk Communications in Socio-Technical Systems

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Computer and Information Science and Engineering (CISE)

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Risk perception and risk averting behaviors of vulnerable communities in the emergence and spread of COVID-19 are spatio-temporal functions of individual or group interactions with their online social neighbors within or outside their communities and such interactions need to be captured through diverse information channels (e.g. traditional outlets such as radio, television, internet and/or non-traditional outlets such as social media). The primary goal of this Rapid Response Research (RAPID) project is to collect time-sensitive online social media and crowd-sourced data and analyze patterns of health-risk communication and community response in the emergence and spread of novel Coronavirus using data-driven methods and network science theories. The major focus will be towards understanding how individuals are socially influenced online, while communicating risk and interacting in their respective communities as the disease continues to spread. The notion of influence will be captured by quantifying the network effects on such communication behavior and characterizing how information is exchanged among people who are socially connected online and exposed to health risk in such outbreaks of disease. Given that communities responded to COVID-19 with limited or no preparation and there is uncertainty in the length of recovery for the communities already affected while new communities being threatened, the data collection effort requires rapid response for better coverage and careful monitoring. The data will include large-scale ephemeral online interactions of people in the affected communities and public officials who are involved in COVID-19 response, recovery, and mitigation efforts, followed by a data-driven network analytics and infographics of COVID-19 risk communication strategies and risk averting behaviors adopted. The proposed research will not only expand the knowledge base of spatio-temporal dynamics of risk perception and dissemination strategies in the emergence and aftermath of a major disease outbreak, but will also result in data-driven inference techniques to improve our understanding of how people express diverse concerns and how to harness and embed such information for designing intervention measures. The methodologies and findings of this rapid response research will benefit emergency management and public health agencies to define targeted information dissemination policies for public with diverse needs based on how people reacted to COVID-19 and their social network characteristics, activities, and interactions in response to similar public health hazards.Public engagement in risk communication can lead to more effective decision-making and enhanced public feedback to the regulatory process. The primary goal of this RAPID project is to mine and analyze large-scale time-sensitive perishable crowd-sourced and social media data (rich spatio-temporal data) and reveal patterns of health-risk communication and community response in the emergence and spread of novel Coronavirus using data-driven methods and network science theories. The specific aims are threefold: (1) to document how public interact and communicate health risk information through their online social networks during a major disease outbreak; (2) to authenticate data from multiple sources and detect anomalies to avoid information overload and spread of misinformation; and (3) to examine how online social networks influence protective actions (e.g., social distancing, self-quarantine decisions) i.e. information cascades in health risk communication. To achieve the goal and aims, the project will utilize ephemeral time and geo-tagged social media interactions of users, agencies, news sources supplemented with crowd-sourced information on COVID-19. This study will have five theoretical and methodological contributions to the literature. It will: (1) advance our understanding of how individuals are socially influenced online, while communicating health risks and interacting in their respective communities as the disease continues to spread; (2) inform the literature on how information is exchanged among people who are socially connected online and exposed to health risk in such outbreaks of disease; (3) use novel machine-learning and network science models to quantify influence and network effects on such communication behavior; (4) capture the variability in network composition, risk communication strategies and risk averting behaviors adopted based on spatio-temporal correlations of risk and disease contagion; (5) ensure authenticity of the collected data from multiple sources and develop more accurate fully-distributed computational algorithms tailored to health risk anomaly detection in socio-technical systems. The findings from this research will be useful to public health and emergency management agencies for tailoring effective information dissemination policies for diverse user groups based on their social network characteristics, activities, and interactions in response to similar public health hazards. The methodologies, and implications of this research can be transferred in designing effective intervention policies to other natural and man-made disaster contexts in which public health risks become major concerns. The project will engage, mentor, and offer an innovative active learning environment for K-12, undergraduate, and graduate students by giving priority to disadvantaged and underrepresented communities in USA. The project will train students on computational skills required for collecting, storing, processing, analyzing and modeling large-scale data using high performance computational resources.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.