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DOE OSTI2021

Albumin in patients with liver disease shows an altered conformation

Abstract Human serum albumin (HSA) constitutes the primary transporter of fatty acids, bilirubin, and other plasma compounds. The binding, transport, and release of its cargos strongly depend on albumin conformation, which is affected by bound ligands induced by physiological and pathological conditions. HSA is both highly oxidized and heavily loaded with fatty acids and bilirubin in chronic liver disease. By employing small-angle X-ray scattering we show that HSA from the plasma of chronic liver disease patients undergoes a distinct opening compared to healthy donors. The extent of HSA opening correlates with clinically relevant variables, such as the model of end-stage liver disease score, bilirubin, and fatty acid levels. Although the mild oxidation of HSA in vitro does not alter overall structure, the alteration of patients’ HSA correlates with its redox state. This study connects clinical data with structural visualization of albumin dynamicity in solution and underlines the functional importance of albumin’s inherent flexibility.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

A porcine ligated loop model reveals new insight into the host immune response against Campylobacter jejuni

The symptoms of infectious diarrheal disease are mediated by a combination of a pathogen’s virulence factors and the host immune system. Campylobacter jejuni is the leading bacterial cause of diarrhea worldwide due to its near-ubiquitous zoonotic association with poultry. One of the outstanding questions is to what extent the bacteria are responsible for the diarrheal symptoms via intestinal cell necrosis versus immune cell initiated tissue damage. To determine the stepwise process of inflammation that leads to diarrhea, we used a piglet ligated intestinal loop model to study the intestinal response to C. jejuni. Pigs were chosen due to the anatomical similarity between the porcine and the human intestine. We found that the abundance of neutrophil related proteins increased in the intestinal lumen during C. jejuni infection, including proteins related to neutrophil migration (neutrophil elastase and MMP9), actin reorganization (Arp2/3), and antimicrobial proteins (lipocalin-2, myeloperoxidase, S100A8, and S100A9). The appearance of neutrophil proteins also corresponded with increases of the inflammatory cytokines IL-8 and TNF-a. Compared to infection with the C. jejuni wild-type strain, infection with the noninvasive C. jejuni ?ciaD mutant resulted in a blunted inflammatory response, with less inflammatory cytokines and neutrophil markers. These findings indicate that intestinal inflammation is driven by C. jejuni virulence and that neutrophils are the predominant cell type responding to C. jejuni infection. We propose that this model can be used as a platform to study the early immune events during infection with intestinal pathogens.

disease model, INNATE IMMUNITY, proteomics, intest↗

NASA NTRS2012

Paramyxovirus Infection Mimics In Vivo Cellular Dynamics in Three-Demensional Human Bronchio-Epithelial Tissue-Like Assemblies

Respiratory syncytial virus and parainfluenza virus cause severe respiratory disease, especially in infants, children and the elderly. An in vitro model that accurately mimics infection of the human respiratory epithelium (HRE) would facilitate vaccine development greatly. Monolayer cultures traditionally used to study these viruses do not accurately and precisely differentiate the replication efficiencies of wild type and attenuated viruses. Therefore, we engineered novel three-dimensional (3D) tissue-like assemblies (TLAs) of human broncho-epithelial (HBE) cells to produce a more physiologically relevant in vitro model of the HRE. TLAs resemble HRE structurally and by expression of differentiated epithelial cell markers. Most significantly, wild type viruses exhibited a clear growth advantage over attenuated strains in TLAs unlike monolayer cultures. In addition, the TLAs responded to virus infection by secreting pro-inflammatory mediators similar to the respiratory epithelia of infected children. These characteristics make the TLA model a valuable platform technology to develop and evaluate live, attenuated respiratory virus vaccine candidates for human use. Respiratory virus diseases, the most frequent and least preventable of all infectious diseases, range in severity from the common cold to severe bronchiolitis and pneumonia . Two paramyxoviruses, respiratory syncytial virus (RSV) and parainfluenza virus type 3 (PIV3), are responsible for a majority of the most severe respiratory diseases of infants and young children. RSV causes 70% of all bronchiolitis cases and is a major cause of morbidity and mortality worldwide, especially in infants. PIV3 causes 10-15% of bronchiolitis and pneumonia during infancy, second only to RSV, and 40% of croup in infants To date, licensed vaccines are not available to prevent these respiratory diseases. At present, traditional monkey kidney (Vero and LLC-MK2) and human (HEp-2) tissue culture cells and small animal models (mouse, cotton rat, guinea pig, ferret, and hamster) fail to accurately imitate viral replication and human disease states (8). Lacking an authentic model has impeded the development and evaluation of live, attenuated vaccine candidates. Development of a physiologically relevant in vitro tissue culture model that reproduces characteristics of the HRE, the primary target of RSV and PIV3, would aid in predicting clinical attenuation and safety of vaccine candidates. Successful tissue engineering of a 3D human intestinal model using novel NASA technology inspired the development of a tri-culture 3D model for the HRE. Sequential layering of primary mesenchymal cells (comprised of normal human fibroblasts and endothelial cells) followed by BEAS-2B epithelial cells derived from human bronchi and tracheae were recapitulated on Cultisphere and/or cytodex3 microcarriers in cylindrical vessels that rotate horizontally creating an organized epithelial structure. Horizontal rotation randomizes the gravity vector modeling aspects of microgravity. Mesenchymal and epithelial cells grown under these conditions reproduce the structural organization, multi-cellular complexity, and differentiation state of the HRE. The opportunity to study respiratory viruses in a nasal epithelium model is invaluable because the most promising respiratory virus vaccine candidates are live attenuated viruses for intranasal administration. Here we characterize the interactions of respiratory viruses and epithelial cells grown under modeled microgravity in comparison to gravity-ladened monolayers. 3D HBE TLAs and traditional monolayers (2D) are infected at 35 C, the upper temperature of the upper HRE, to simulate in vivo infection conditions. Growth kinetics of wild type (wt) RSV and PIV3 viruses were compared in 2D and 3D cells to that of strains attenuated in humans or rhesus macaques. This novel 3D HBE model also offers an opportunity to study whether the epithelial cell function, especially in host defenses recapitulated by mimicking the structural organization of the HRE. In vivo, airway epithelial cells play a significant and dynamic role in host defense by blocking paracellular permeability and modulating airway function through cellular interactions or tight junctions. As regulators of the innate immune response, epithelial cells constitutively express cytokines, chemokines, and colony stimulating factors including RANTES, IL-8, IL-6, GM-CSF, and G-CSF for proactive host defense. In response to viral infection, epithelial cells induce potent immuno-modulatory and pro-inflammatory cytokines that recruit phagocytic and inflammatory cells to clear the virus and enhance protection. Although disease pathogenesis is classically attributed to the cytopathic effects of the pathogen, severe disease states associated with RSV and PIV3 are attributed to the inflammatory response, especially in infants. RSV is a potent inducer of cytokines and pro-inflammatory mediators in epithelial cells in vivo. A differentiated human epithelial model independent of the complete functional immune system will help elucidate the role of epithelial cells in respiratory disease. We reported here, virus and host cell interactions in 3D HBE TLAs are similar to that in vivo. Because the epithelial cell organization of the TLAs impacts not only the expression of airway epithelial characteristics, but also cellular communication, the TLAs represent a more physiologically relevant model of the HRE than BEAS-2B or other non-tumour monolayer models of respiratory disease. As a result, wild type respiratory viruses have a clear growth advantage over attenuated viruses in TLAs unlike traditional monolayers. In addition, the TLAs respond to wild type virus infection by secreting pro-inflammatory mediators characteristic of infected HRE. TLAs expressing microbial defense mechanisms provide an excellent model to study the interactions of respiratory pathogens with their host and to identify the innate immunity mediators. Therefore, 3D HBE TLAs offer advantages for the study of respiratory viruses and the development of viral vaccine candidates.

Deatly, Anne M.↗

NASA NTRS2018

Satellite and Reanalysis Air Quality Data and Services at NASA GES DISC for Public Health Study

Outbreaks of infectious diseases and health can be influenced by airborne and water-borne pollutants. Furthermore, air and water quality are associated with climate variability, industrialization, land use and land cover change, and water resource management. It is therefore crucial to understand environment-disease connections with existing long-term observed and modeled data, particularly for development of early warning systems for infectious disease outbreaks. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) (https://disc.gsfc.nasa.gov) archives large volumes of global environment data that are useful for research and applications regarding environmental factors and public health. Examples of air quality measurements are: Daily satellite remotely sensed data, including Aerosol Index (AI), O3, SO2, CO, and NO2 from Aura/OMI (October 2014 to present), and OMPS-NPP (January 2012 to present, currently research data products only) Hourly and monthly reanalysis modeled data, including PM2.5, O3, CO, SO2, BC, dust, AOD, and aerosol types from MERRA-2 (January 1980 to present) Examples of surface meteorology and land surface measurements are: hourly, daily, and monthly satellite precipitation from TRMM (December 1997 to March 2015) 30-minute and monthly satellite precipitation from GPM (March 2014 to present); Hourly and monthly modeled surface meteorology and land surface condition from MERRA-2 (January 1980 to present) and land surface assimilation models (January 1948 to present), including precipitation, surface temperature, relative humidity, wind, and soil moisture.This presentation will give an overview of relevant environmental data at the NASA GES DISC. Through a number of use cases, such as dust events and active fires, we will introduce data services that assist in finding the right data, enable visualization and analysis of the data online, and allow downloading of data in user-preferred format.

remote sensing data↗

DOE OSTI2023

Modeling immunity in microphysiological systems

There is a need for better predictive models of the human immune system to evaluate safety and efficacy of immunomodulatory drugs and biologics for successful product development and regulatory approvals. Current in vitro models, which are often tested in two-dimensional (2D) tissue culture polystyrene, and preclinical animal models fail to fully recapitulate the function and physiology of the human immune system. Microphysiological systems (MPSs) that can model key microenvironment cues of the human immune system, as well as of specific organs and tissues, may be able to recapitulate specific features of the in vivo inflammatory response. This minireview provides an overview of MPS for modeling lymphatic tissues, immunity at tissue interfaces, inflammatory diseases, and the inflammatory tumor microenvironment in vitro and ex vivo. Broadly, these systems have utility in modeling how certain immunotherapies function in vivo, how dysfunctional immune responses can propagate diseases, and how our immune system can combat pathogens.

Research & Experimental Medicine↗

DOE OSTI2022

Modeling Down Syndrome Myeloid Leukemia by Sequential Introduction of GATA1 and STAG2 Mutations in Induced Pluripotent Stem Cells with Trisomy 21

Children with Down syndrome (DS) have a high risk for acute myeloid leukemia (DS-ML). Genomic characterization of DS-ML blasts showed the presence of unique mutations in GATA1, an essential hematopoietic transcription factor, leading to the production of a truncated from of GATA1 (GATA1s). GATA1s, together with trisomy 21, is sufficient to develop a pre-leukemic condition called transient abnormal myelopoiesis (TAM). Approximately 30% of these cases progress into DS-ML by acquisition of additional somatic mutations in a stepwise manner. We previously developed a model for TAM by introducing disease-specific GATA1 mutation in trisomy 21-induced pluripotent stem cells (iPSCs), leading to the production of N-terminally truncated short form of GATA1 (GATA1s). In this model, we used CRISPR/Cas9 to introduce a co-operating mutation in STAG2, a member of the cohesin complex recurrently mutated in DS-ML but not in TAM. Hematopoietic differentiation of GATA1 STAG2 double-mutant iPSC lines confirmed GATA1s expression and the loss of functional STAG2 protein, leading to enhanced production of immature megakaryocytic population compared to GATA1 mutant alone. Megakaryocyte-specific lineage expansion of the double-mutant HSPCs exhibited close resemblance to the DS-ML immunophenotype. Transcriptome analysis showed that GATA1 mutation resulted in downregulation of megakaryocytic and erythrocytic differentiation pathways and interferon α/β signaling, along with an upregulation of pathways promoting myeloid differentiation such as toll-like receptor cascade. The co-occurrence of STAG2 knockout partially reverted the expression of genes involved in myeloid differentiation, likely leading to enhanced self-renewal and promoting leukemogenesis. In conclusion, we developed a DS-ML model via hematopoietic differentiation of gene-targeted iPSCs bearing trisomy 21.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Modeling epidemic spread with spike-based models

The Susceptible-Infected-Recovered/Removed model is a standard model for epidemiological spread of disease through vulnerable populations. In this paper we show how SIR network dynamics can be implemented using spiking neurons.

Hamilton, Kathleen↗

NASA NTRS2002

A Good Neighborhood for Cells: Bioreactor Demonstration System (BDS-05)

Good neighborhoods help you grow. As with a city, the lives of a cell are governed by its neighborhood connections Connections that do not work are implicated in a range of diseases. One of those connections - between prostate cancer and bone cells - will be studied on STS-107 using the Bioreactor Demonstration System (BDS-05). To improve the prospects for finding novel therapies, and to identify biomarkers that predict disease progression, scientists need tissue models that behave the same as metastatic or spreading cancer. This is one of several NASA-sponsored lines of cell science research that use the microgravity environment of orbit in an attempt to grow lifelike tissue models for health research. As cells replicate, they "self associate" to form a complex matrix of collagens, proteins, fibers, and other structures. This highly evolved microenvironment tells each cell who is next door, how it should grow arid into what shapes, and how to respond to bacteria, wounds, and other stimuli. Studying these mechanisms outside the body is difficult because cells do not easily self-associate outside a natural environment. Most cell cultures produce thin, flat specimens that offer limited insight into how cells work together. Ironically, growing cell cultures in the microgravity of space produces cell assemblies that more closely resemble what is found in bodies on Earth. NASA's Bioreactor comprises a miniature life support system and a rotating vessel containing cell specimens in a nutrient medium. Orbital BDS experiments that cultured colon and prostate cancers have been highly promising.

Chung, Leland W. K.↗

NASA NTRS2011

The Increasing Use of Remote Sensing Data in Studying the Climatological Impacts on Public Health

One of the more fortunate outcomes of the capture and transformation of remote sensing data into applied information is their usefulness and impacts to better understanding climatological impacts on public health. Today, with petabytes of remote sensing data providing global coverage of climatological parameters, public health research and policy decision makers have an unprecedented (and growing) data record that relates the effects of climatic parameters, such as rainfall, heat, soil moisture, etc. to incidences and spread of disease, as well as predictive modeling. In addition, tools and services that specifically serve public health researchers and respondents have grown in response to needs of the these information users.

Kempler, Steven↗

NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗

DOE OSTI2024

Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). Here we proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Self‐gated, dynamic contrast‐enhanced magnetic resonance imaging with compressed‐sensing reconstruction for evaluating endothelial permeability in the aortic root of atherosclerotic mice

High‐risk atherosclerotic plaques are characterized by active inflammation and abundant leaky microvessels. We present a self‐gated, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) acquisition with compressed sensing reconstruction and apply it to assess longitudinal changes in endothelial permeability in the aortic root of Apoe −/− atherosclerotic mice during natural disease progression. Twenty‐four, 8‐week‐old, female Apoe −/− mice were divided into four groups (n = 6 each) and imaged with self‐gated DCE‐MRI at 4, 8, 12, and 16 weeks after high‐fat diet initiation, and then euthanized for CD68 immunohistochemistry for macrophages. Eight additional mice were kept on a high‐fat diet and imaged longitudinally at the same time points. Aortic‐root pseudo‐concentration curves were analyzed using a validated piecewise linear model. Contrast agent wash‐in and washout slopes ( b 1 and b 2 ) were measured as surrogates of aortic root endothelial permeability and compared with macrophage density by immunohistochemistry. b 2 , indicating contrast agent washout, was significantly higher in mice kept on an high‐fat diet for longer periods of time ( p = 0.03). Group comparison revealed significant differences between mice on a high‐fat diet for 4 versus 16 weeks ( p = 0.03). Macrophage density also significantly increased with diet duration ( p = 0.009). Spearman correlation between b 2 from DCE‐MRI and macrophage density indicated a weak relationship between the two parameters (r = 0.28, p = 0.20). Validated piecewise linear modeling of the DCE‐MRI data showed that the aortic root contrast agent washout rate is significantly different during disease progression. Further development of this technique from a single‐slice to a 3D acquisition may enable better investigation of the relationship between in vivo imaging of endothelial permeability and atherosclerotic plaques' genetic, molecular, and cellular makeup in this important model of disease.

Calcagno, Claudia↗

DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A deep redox proteome profiling workflow and its application to skeletal muscle of a Duchenne Muscular Dystrophy model

Perturbation to the redox state accompanies many diseases and its effects are viewed through oxidation of biomolecules, including proteins, lipids, and nucleic acids. The thiol groups of protein cysteine residues undergo an array of redox post-translational modifications (PTMs) that are important for regulation of protein and pathway function. To better understand what proteins are redox regulated following a perturbation, it is important to be able to comprehensively profile protein thiol oxidation at the proteome level. Herein, we report a deep redox proteome profiling workflow and demonstrate its application in measuring the changes in thiol oxidation along with global protein expression in skeletal muscle from mdx mice, a model of Duchenne Muscular Dystrophy (DMD). In depth coverage of the thiol proteome was achieved with >18,000 Cys sites from 5608 proteins in muscle being quantified. Compared to the control group, mdx mice exhibit markedly increased thiol oxidation, where ~2% shift in the median oxidation occupancy was observed. Further, pathway analysis for the redox data revealed that coagulation system and immune-related pathways were among the most susceptible to increased thiol oxidation in mdx mice, whereas protein abundance changes were more enriched in pathways associated with bioenergetics. This study illustrates the importance of deep redox profiling in gaining greater insight into oxidative stress regulation and pathways/processes that are perturbed in an oxidizing environment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2020

An Update on Global Satellite-Based Precipitation Products and Services at NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is home to major NASA satellite precipitation measurement missions including the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) as well as other NASA projects. Accurate and timely available global and regional precipitation products play an important role in research and applications around the world. The GES DISC provides near-real-time, near-global precipitation products (e.g.IMERG) to support a wide variety of interdisciplinary research and operational activities including flood modeling, landslides, crop monitoring and assessment, vector-borne diseases, etc. Climate data record products (e.g.GPCP3) are essential for climate research, assessment, model evaluation and applications. To facilitate data access and exploration, the GES DISC has developed data services such asGiovanni, an online visualization and analysis tool for easy access to over 2000 satellite- and model-based variables. Established in the mid 1980s, the GES DISC also distributes data in other disciplines including hydrology, atmospheric chemistry, atmospheric dynamics, etc. In this presentation, we will present an update on global precipitation products and data services including the new IMERG V06B suite, the latest version of GPCP (Version 3) and value-added data subsetting services (L34RS, L2S).

Liu, Zhong↗

NASA NTRS2015

Assessing Disparities of Dengue Virus Transmission Risk across the US-Mexican Border Using a Climate Driven Vector-Epidemiological Model

Dengue fever is a mosquito‐borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Although transmission regularly occurs along the border region in Mexico, dengue virus transmission in bordering Arizona has not occurred. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input and Daymet for temperature and supplemental precipitation input, we modeled dengue transmission along a US‐Mexico transect using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Model runs were performed for 5 cities in Sonora, Mexico and southern Arizona. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. For cities with reported dengue case data, the top model simulations that best reproduced dengue case numbers were retained and their parameter values were extracted for comparison. These parameter values were used to run simulations in areas where dengue virus transmission does not occur or where dengue fever case data was unavailable. Additional model runs were performed to reveal how changes in climate or parameter values could alter transmission risk along the transect. The relative influence of climate variability and model parameters on dengue virus transmission is assessed to help public health workers prepare location specific infection prevention strategies.

Morin, Cory↗