# data[].attributes.title
Collaborative Research: Unraveling Structural and Mechanistic Aspects of RNA Viral Frameshifting Elements by Graph Theory and Molecular Modeling

# data[].attributes.funder_divisions[]
Mathematical and Physical Sciences (MPS)

# data[].attributes.principal_investigator.affiliations[].name
University of North Carolina at Chapel Hill

# data[].attributes.awardee_organization.name
University of North Carolina at Chapel Hill

# data[].attributes.abstract
Programmed ribosomal frameshifting is indispensable to many viruses, including HIV and SARS-associated coronaviruses, to translate overlapping reading frames on the mRNA so that essential viral proteins can be produced. Because modulation of frameshifting has been shown to dramatically influence viral viability, the RNA frameshifting element (FSE) has been an attractive anti-viral drug target. However, the complex aspects of frameshifting must be understood before therapeutic strategies can succeed. Following a 2020 NSF RAPID award, the Schlick mathematics/computational biology lab, in collaboration with the Laederach experimental RNA group, will combine graph theory applications to RNA (RAG: RNA-As-Graphs) with biophysical studies and biomolecular modeling/simulation to unravel structures and mechanisms of the RNA FSE of SARS-CoV-2 and related viruses. The collaborative research program will be the basis for interdisciplinary training of students and postdoctoral fellows, including women and minorities, in mathematics, computer science, biology, physics, chemistry, and engineering, through computer program development, data analysis, and biological interpretations. Students and postdocs will learn to analyze, process, and visualize biological data; devise and validate models; develop simulation algorithms and coarse-grained models; and collect and interpret structural/functional patterns to yield new mathematical and biophysical relationships. The project will describe conformations and structural transitions of the FSE of SARS-CoV-2 from phylogenetic and biophysical viewpoints by exploiting global representation of mathematical RNA graphs. Specifically, the researchers will gain insight into the evolutionary path of the FSE of coronaviruses by computing and validating experimentally RNA secondary-structure conformational landscapes of the FSE of SARS-CoV-2 relatives; probe frameshifting mechanisms by determining the SARS-CoV-2 FSE's transition pathway; and identify and test experimentally structure-altering mutations to transform the FSE into complex intertwined motifs by RAG inverse folding and genetic algorithms to hamper frameshifting. This unique approach applied to frameshifting elements in coronaviruses including SARS-CoV-2 using novel mathematical graph-theory tools and biophysical models will yield crucial insights into the structure, mechanisms, and evolutionary trends in related viruses to explain the relationship between viral structure and frameshifting efficiency/viral viability. By looking at structure from a global graph theory point of view, patterns can be discerned and related more easily than sequence or atomic-based models. The determined structures, mechanisms, and structure-altering mutations define gene therapy and anti-viral targets for therapeutic interventions.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.

# data[].attributes.title
Collaborative Research: Unraveling Structural and Mechanistic Aspects of RNA Viral Frameshifting Elements by Graph Theory and Molecular Modeling

# data[].attributes.funder_divisions[]
Mathematical and Physical Sciences (MPS)

# data[].attributes.abstract
Programmed ribosomal frameshifting is indispensable to many viruses, including HIV and SARS-associated coronaviruses, to translate overlapping reading frames on the mRNA so that essential viral proteins can be produced. Because modulation of frameshifting has been shown to dramatically influence viral viability, the RNA frameshifting element (FSE) has been an attractive anti-viral drug target. However, the complex aspects of frameshifting must be understood before therapeutic strategies can succeed. Following a 2020 NSF RAPID award, the Schlick mathematics/computational biology lab, in collaboration with the Laederach experimental RNA group, will combine graph theory applications to RNA (RAG: RNA-As-Graphs) with biophysical studies and biomolecular modeling/simulation to unravel structures and mechanisms of the RNA FSE of SARS-CoV-2 and related viruses. The collaborative research program will be the basis for interdisciplinary training of students and postdoctoral fellows, including women and minorities, in mathematics, computer science, biology, physics, chemistry, and engineering, through computer program development, data analysis, and biological interpretations. Students and postdocs will learn to analyze, process, and visualize biological data; devise and validate models; develop simulation algorithms and coarse-grained models; and collect and interpret structural/functional patterns to yield new mathematical and biophysical relationships.The project will describe conformations and structural transitions of the FSE of SARS-CoV-2 from phylogenetic and biophysical viewpoints by exploiting global representation of mathematical RNA graphs. Specifically, the researchers will gain insight into the evolutionary path of the FSE of coronaviruses by computing and validating experimentally RNA secondary-structure conformational landscapes of the FSE of SARS-CoV-2 relatives; probe frameshifting mechanisms by determining the SARS-CoV-2 FSE's transition pathway; and identify and test experimentally structure-altering mutations to transform the FSE into complex intertwined motifs by RAG inverse folding and genetic algorithms to hamper frameshifting. This unique approach applied to frameshifting elements in coronaviruses including SARS-CoV-2 using novel mathematical graph-theory tools and biophysical models will yield crucial insights into the structure, mechanisms, and evolutionary trends in related viruses to explain the relationship between viral structure and frameshifting efficiency/viral viability. By looking at structure from a global graph theory point of view, patterns can be discerned and related more easily than sequence or atomic-based models. The determined structures, mechanisms, and structure-altering mutations define gene therapy and anti-viral targets for therapeutic interventions.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.

# data[].attributes.title
Accelerating Bayesian Dimension Reduction for Dynamic Network Data with Many Observations

# data[].attributes.funder_divisions[]
Mathematical and Physical Sciences (MPS)

# data[].attributes.abstract
Global viral epidemics produce vast amounts of high-dimensional spatiotemporal data. Scientists, businesses, governments and independent organizations want to learn from this data so they can understand basic biological mechanisms, invest capital, allocate aid and design coherent policy in a changing world. Analyzing spatial associations within viral contagion is, unsurprisingly, an area of immense scientific interest, but the task requires accounting for the dynamic and multiscale transportation networks that shape the global economy. This project seeks to advance knowledge of statistical inference from stochastic process models in the context of massive amounts of dynamic and network-indexed data. The proposed research ideas will avoid costly direct representations of network structure and instead use Bayesian dimension reduction to probabilistically map network dynamics to a continuous domain. The project combines theoretical and methodological developments in scalable Bayesian dimension reduction; develops efficient algorithms into open-source, high performance computing (HPC) software; and applies them to the high-impact analysis of viruses including, but not limited to, SARS-CoV-2. The project will emphasize the combination of rigorous statistical methodology with parallel computing techniques available to any scientist with moderate resources.The project will combine theory, methods and applications in advancing knowledge of statistical inference for network-indexed processes.  Bayesian multidimensional scaling (BMDS) stands as an established tool for probabilistic dimension reduction of network data but the method's quadratic computational complexity prohibits big data application. The project will extend BMDS to the analysis of millions of data points using a multipronged approach.  From a theoretical standpoint, the investigators will show that the classical BMDS model is strictly equivalent to a modified BMDS model with sparse couplings between observations. This 'free lunch' result will amount to a linear reduction in the computational complexity of the classical algorithm, but its use will require an upper bound on the rank of the traditional BMDS distance matrix. A jointly methodological and theoretical investigation will develop a cutting-edge rank estimation procedure for Euclidean distance matrices (EDM) and derive non-asymptotic and asymptotic bounds for the rank estimation error and its impact on the modified BMDS posterior. Bayesian inference with the developed sparse BMDS (S-BMDS) will amount to simulating a massive N-body problem with sparse pairwise couplings. A primary methodological investigation will develop fast parallel algorithms for computing (1) the S-BMDS likelihood and gradient, and (2) the EDM rank in ways that efficiently use multi-core and vectorized central processing units (CPU) and multiple graphics processing units (GPU). The investigators will then allow trends in Google mobility data to inform effective distances between viruses and use our developed machinery to model the spread of, e.g., SARS-CoV-2 through global mobility space. The project also includes an expansive plan for educational, outreach and mentoring activities and will  actively disseminate the research findings in a form of open-source HPC software.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.

# data[].attributes.title
Boston University Conference on Language Development, Post-COVID

# data[].attributes.funder_divisions[]
Social, Behavioral, and Economic Sciences (SBE)

# data[].attributes.abstract
The Boston University Conference on Language Development (BUCLD)is an internationally recognized meeting of researchers in language acquisition and development, including first and second language acquisition, language disorders, bilingualism, and literacy development. BUCLD promotes the progress of science by providing a major venue for dissemination of scholarly findings, for initiation and development of collaborations, and for professional networking. The conference consists of approximately 190 presentations selected through peer review, as well as invited keynote and plenary speakers, a symposium on a topic of current interest, a research funding workshop, a professional development workshop for students and early-career scholars, mentoring opportunities, and a publisher exhibit. Attendees number over 500 researchers from around the world, from undergraduates through senior faculty. Full ASL interpreting coverage is provided throughout the conference, and conference proceedings are published soon after the conference. This award supports accessibility, diversity, and inclusion at BUCLD by subsidizing travel expenses for students, including students from diverse racial/ethnic backgrounds, students with disabilities, and first-generation college students. In addition, funding supports the effort of the student conference organizers, who are graduate students in Linguistics and allied fields. Support for student travel enables student contributions—regularly among the highest-rated abstract submissions—to be presented at the conference without undue hardship. Attendance at BUCLD increases the exposure of students to the top research and researchers in the field in a friendly and interactive environment. Furthermore, support for student organizers allows BUCLD to play an important role in training students as future professionals. Finally, funding supports development of a sustainable and flexible model for BUCLD that enables continuity of the conference in future years and navigates the challenges and opportunities of scientific conferences in a post-COVID world.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.

# data[].attributes.title
I-Corps:  AI-assisted Job Search in the Aftermath of the Covid-19 Crisis

# data[].attributes.funder_divisions[]
Technology, Innovation and Partnerships (TIP)

# data[].attributes.principal_investigator.affiliations[].name
Regents of the University of Michigan - Ann Arbor

# data[].attributes.awardee_organization.name
Regents of the University of Michigan - Ann Arbor

# data[].attributes.abstract
The broader impact/commercial potential of this I-Corps project is the development of a technology that will help customer-facing businesses hire from a pool of candidates that have no prior experience in the sector.  The proposed technology may be used by hiring managers in mid- to large-scale employers of entry-level service workers. It is anticipated that the beachhead market will be retail. The key insights from preliminary customer discovery were that in large stores with >10k employees, the turnover in entry level positions is very high (e.g., 76% in retail in 2019) and each entry-level vacancy attracts a high volume of applications (e.g., up to 1,000 applications per position). Applicants for these vacancies are often indistinguishable given limited technical skills and previous experience.  Therefore, current screening process for entry-level positions relies heavily on arbitrary filtering heuristics, individual judgement, and interviews, and, as a result, can be particularly prone to discrimination. In addition, employers aim to hire candidates with particular soft skills (e.g., conscientiousness, attention to detail, intrinsic motivation, communication), but find it difficult to objectively measure them. In addition, the cost of replacing a worker who leaves, excluding lost productivity, is high (typically 1.5 to 2.5 times the worker’s annual salary).  By focusing on psychometric and simulated tasks, the proposed technology: a) reduces the risk of systemic discrimination while hiring; b) allows employers to cheaply, quickly and fairly identify best-suited candidates from a pool of indistinguishable candidates; c) have the ability to match workers to best-suited occupations by documenting the transferability of skills across occupations and sectors. The hypothesis is that the technology may reduce screening and hiring costs by more than 60% per head hired, in addition to finding employees that attrit at a lower rate.This I-Corps project is based on the development of machine learning classification algorithms that use psychometric profiles and performance on simulated tasks to identify best-suited candidates for entry-level customer facing occupations. These tasks have been designed in partnership with employers in each industry to mirror actual work. To select the optimal algorithm for each task, the proposed technology uses a cost function that takes into account the losses to the employer from wrongfully rejecting qualified individuals as well as from interviewing unqualified applicants. Additionally, the proposed technology uses labor market parameters in balancing false positive and false negative predictions in selecting and calibrating the algorithms, which pushes even the frontier in this space, and is beyond the capabilities of any practical solutions in the market today.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.

# data[].attributes.title
DDRIG: Telemedicine and In-person Practices for the Measurement of Pain

# data[].attributes.funder_divisions[]
Social, Behavioral, and Economic Sciences (SBE)

# data[].attributes.abstract
This project asks how the shift to telemedicine platforms has affected the quality of care for chronic pain patients within the Veterans Healthcare Administration. This project will inform health care practices by revealing how telemedicine has changed pain assessment, documentation, and treatment. Lessons learned from this study will have relevance for other healthcare organizations that utilize telemedicine for pain assessment. This project considers the medical implications of transforming qualitative, subjective experiences, such as pain, into quantitative data that create clinical accountability. This study illuminates how technological practices like telemedicine are re-defining the creation of medical knowledge, the parameters of expert jurisdiction, and organizational protocols. It will be of interest to patients, health-care providers, policy makers and administrators. This project will analyze patient data pre- and post-COVID through an examination of electronic medical records by comparing in-person and remote care. The project aims to identify how practices—such as measuring pain, documenting pain status, prescribing medication, or recommending alternative treatment modalities—are affected by the migration to telemedicine. In doing so, it assesses how the increased use of telemedicine has challenged common assessment and treatment protocols used by health providers.  In tandem, interviews with clinicians will trace when and how health care providers decide to bring patients into the clinic for in-person assessment, rather than relying on telemedicine alone. This study will provide an analysis of the need for creating continuous care plans that bridge distinct delivery platforms. The findings from this project will help policymakers and administrators to support clinicians during both in-person and remote visits for pain care.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.

# data[].attributes.title
III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks

# data[].attributes.funder_divisions[]
Computer and Information Science and Engineering (CISE)

# data[].attributes.abstract
Infectious disease outbreaks, such as the novel coronavirus disease (COVID-19) pandemic, entailed localized conditions with evolution in time and space present a daunting task for policy and decision makers in finding optimal non-pharmaceutical intervention (NPI) strategies at different scales that balance epidemiological benefits and socioeconomic costs. To help tackle this challenging problem, by harnessing the data revolution and advancing capabilities of artificial intelligence (AI), this multidisciplinary project aims to design and develop a data-driven and AI-augmented framework that is tailored to the evolving localized conditions and enables expert-in-the-loop for adaptive NPIs to effectively respond to the dynamics of epidemic while balancing the multidimensional socioeconomic impacts. The proposed work will not only benefit local and federal governments, regional communities, corporations, societal leaders and the public by assisting with effective responses to the public health issues while mitigating negative socioeconomic impacts and various induced crises, but will also facilitate the development of robust science-based decision support systems responding to future natural or man-made disasters. The research will be beneficial to multidisciplinary areas, including data science, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes (e.g., open-source code, data, and models) will be made publicly accessible and broadly distributed through publications, media presses, etc. This project will integrate research with education, including novel curriculum development, student mentoring, professional training and workforce development, and K-12 outreach activities aimed at underrepresented groups.To combat infectious disease outbreaks with robust response planning, this project includes four interconnected research components to develop an intelligent and interactive decision support framework that allows in silico exploration of extensive possible NPIs prior to the potential field implementation phase. First, the team will develop a novel spatial-temporal heterogeneous graph model to abstract dynamics of harnessed multi-source data. Second, the team will develop new techniques to learn node (i.e., area) representations over the constructed graph by integrating both spatial and temporal dependencies while preserving the heterogeneity. Third, based on the learned node representations, given a set of NPIs, the team will design and develop an innovative NPI-aware multi-head transformer for multi-task prediction (i.e., forecasting epidemic dynamics and associated socioeconomic impacts). Fourth, based on the predictions, the team will develop a novel multi-agent reinforcement learning model with inverse reward learning to enable expert-in-the-loop in finding optimal sequential NPIs that balance epidemiological benefits and socioeconomic costs under certain constraints and objectives set by policy and decision makers. The research will advance the field of information integration and informatics through the development of a series of original works including novel deep graph learning techniques with the context of heterogeneous and dynamic graph structures, which will also provide foundational work for addressing similar challenges for future natural or man-made disasters.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.

# data[].attributes.title
FORTE - FORest ciTizenship for disaster rEsilience: learning from COVID-19

# data[].attributes.funder_divisions[]
Social, Behavioral, and Economic Sciences (SBE)

# data[].attributes.abstract
The COVID-19 pandemic had a disproportionately large negative impact on traditional rural societies with little access to modern health care.  This research project will study how forest-dependent indigenous people of the Amazon forest engaged in citizenship to exercise their rights to influence institutions and political processes that increased their resilience and reduced their vulnerability to COVID-19.  The study will use existing data to quantify the mechanisms through which these communities reduced their vulnerability in the past by being more engaged citizens.  The research team, consisting of scientists from Brazil, the UK, and the US, will also collect new data using interviews with people living in traditional rural communities, to better understand the opportunities and barriers to their engagement. The research results will be disseminated to stakeholders, including local, state, and federal governmental agencies, and policy makers in Brazil. The results of this research could provide guidance on policies to strengthen inclusive democratic governance, reduce unequal access to health and other resources, and increase resilience to crises.  The results of this research have global application, hence will help establish the US as a global leader in disaster resilience.Citizenship from below, a process through which marginalized peoples self-organize to claim recognition and rights, is crucial to disaster recovery, resilience, and societal renewal.    This project assembles an international team of social scientists from Brazil, UK, and the US to study how the forest people of Brazilian Amazonia have used collective action to mitigate the negative impacts of the COVID-19 pandemic and strengthen disaster resilience.  The study will: (1) quantify linkages between forest citizenship and COVID-19 resilience; (2) investigate various practices of forest citizenship that were employed during the COVID-19 pandemic; and (3) disseminate learning experiences of the forest people on how to promote forest citizenship and enhance disaster resilience across Amazonia and other parts of the world.  The research project will address these questions through three interlinked projects---quantitative analysis of secondary health, governance, and environmental data (entire Brazilian Amazon), qualitative fieldwork in selected municipalities in Amazonia and Acre State, and action-research.   The results of this research could provide guidance on policies to strengthen inclusive democratic governance, reduce unequal access to health and other public resources, and increase resilience to crises.  The result of this research is scalable, hence can help establish the US as a global leader in disaster resilience.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.

# data[].attributes.title
Exploring the Role of Adaptive Capacity on Democratic Performance (ERAC-DP): Governmental and Nonprofit Organizations in the Pandemic

# data[].attributes.funder_divisions[]
Social, Behavioral, and Economic Sciences (SBE)

# data[].attributes.principal_investigator.affiliations[].name
The University of Central Florida Board of Trustees

# data[].attributes.awardee_organization.name
The University of Central Florida Board of Trustees

# data[].attributes.abstract
This research project uses methods from several social sciences and comparative analysis to study how COVID-19 response policies can increase inequality, foster democratic governance, and political participation of vulnerable people.  The research project will answer four questions: (i) what inequalities emerged for vulnerable populations during the COVID-19 pandemic? (ii) how did organizational adaptive capacities affect outcomes for vulnerable populations? (iii) how did adaptive capacities affect policy compliance of vulnerable populations? and (iv) what digital tools will facilitate a sustained collaboration (domestic and international) to enhance global and local action to reduce inequities and increase policy compliance?  The study is based on case studies from Atlanta, GA, Montreal, Canada, Manchester, UK, and Warsaw Poland and uses a combination of theories from several social science fields and rich data collection and analyses.  The results of this research project will help to answer important questions as to why groups are differentially affected by policies designed to help all and why policy compliance may differ across groups.  The results could guide policy formulation and implementation during pandemics and other emergencies.This research uses embedded case studies in four countries---Canada, Poland, UK, and the US---to study how COVID-19 pandemic policies affected inequalities, and how this in turn affected political activism and policy responses of different groups.  Specifically, the project will try to answer four questions: (i) what inequalities emerged during the COVID-19 pandemic? (ii) how did adaptive capacities affect outcomes for vulnerable populations? (iii) how did adaptive capacities affect policy compliance by vulnerable populations? and (iv) what digital tools will facilitate sustained collaboration to enhance global and local action to reduce inequities and increase policy compliance?  The research will answer these questions using a methodology that combines theories from several social and behavioral sciences.  In addition to academic research, the PIs propose to engage diverse policy makers to improve the policy impact of the research project.  By addressing the impact of policies on inequality and political participation, this research project will make significant contributions to social and behavioral science research on policy impacts generally.  The research results of this research project will provide inputs into policies to reduce inequalities as well as enhance political participation.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.

# data[].attributes.title
Pandemic Communication in Time of Populism: Building Resilient Media and Ensuring Effective Pandemic Communication in Divided Societies

# data[].attributes.funder_divisions[]
Social, Behavioral, and Economic Sciences (SBE)

# data[].attributes.abstract
This project uses several methods to study how populist politicians distorted COVID-19 pandemic health communication to encourage polarized attitudes and distrust among citizens, thus making them more vulnerable to misinformation generally. It also studies how best to counter these populist narratives and develop more effective communication channels. The research studies four areas of communication: government led pandemic communication, media policy, media coverage, and public attitudes towards the media.  The project makes an important contribution to research on populist communication and political polarization by bringing two fields of expertise–populist communication and public health–together. The research will inform recommendations aimed at building more resilient media organizations that are better equipped to withstand the challenges of future pandemics in divided societies.  By helping to improve the quality of health communication, this research will help to improve the health, hence living standards of US citizens.  This project will develop the first comprehensive, comparative study of health crisis communication in the context of populist politics and examines the impact of populism on four aspects of the pandemic communication circuit during COVID-19: government-led health crisis communication, media policy, media coverage, and public attitudes. The project will also study how best to counter these populist narratives and develop more efficient and reliable communication. The focus is on four countries---Brazil, Poland, Serbia, and the US---all led by populist leaders during the pandemic and capture different types of populist responses to the pandemic.  The project also takes a transnational perspective to analyze how the interaction between populism and pandemic communication was shaped by China and Russia’s pandemic geopolitics.  By helping to improve the quality of health communication, this research will help to improve the health, hence living standards of US 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.