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1. The regulatory T cell-selective interleukin-2 receptor agonist rezpegaldesleukin in the treatment of inflammatory skin diseases: two randomized, double-blind, placebo-controlled phase 1b trials

Author

Silverberg, Jonathan I., Rosmarin, David, Chovatiya, Raj, Bieber, Thomas, Schleicher, Stephen, Beck, Lisa, Gooderham, Melinda, Chaudhry, Sohail, Fanton, Christie, Yu, Danni, Levy, Joshua, Liu, Yi, Miyazaki, Takahiro, Tagliaferri, Mary, Schmitz, Carsten, Nirula, Ajay, Kotzin, Brian, and Zalevsky, Jonathan

Published
2024
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3. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

Author

Latif, Alaa, Mullen, Julia, Alkuzweny, Manar, Hufbauer, Emory, Tsueng, Ginger, Haag, Emily, Zeller, Mark, Aceves, Christine, Zaiets, Karina, Cano, Marco, Zhou, Xinghua, Qian, Zhongchao, Sattler, Rachel, Matteson, Nathaniel, Levy, Joshua, Lee, Raphael, Freitas, Lucas, Maurer-Stroh, Sebastian, Wu, Chunlei, Su, Andrew, Andersen, Kristian, Hughes, Laura, Suchard, Marc, and Gangavarapu, Karthik

Subjects
Humans, SARS-CoV-2, COVID-19, Genomics, Disease Outbreaks, Mutation
Abstract

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Published
2023

4. Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

Author

Matteson, Nathaniel L, Hassler, Gabriel W, Kurzban, Ezra, Schwab, Madison A, Perkins, Sarah A, Gangavarapu, Karthik, Levy, Joshua I, Parker, Edyth, Pride, David, Hakim, Abbas, De Hoff, Peter, Cheung, Willi, Castro-Martinez, Anelizze, Rivera, Andrea, Veder, Anthony, Rivera, Ariana, Wauer, Cassandra, Holmes, Jacqueline, Wilson, Jedediah, Ngo, Shayla N, Plascencia, Ashley, Lawrence, Elijah S, Smoot, Elizabeth W, Eisner, Emily R, Tsai, Rebecca, Chacón, Marisol, Baer, Nathan A, Seaver, Phoebe, Salido, Rodolfo A, Aigner, Stefan, Ngo, Toan T, Barber, Tom, Ostrander, Tyler, Fielding-Miller, Rebecca, Simmons, Elizabeth H, Zazueta, Oscar E, Serafin-Higuera, Idanya, Sanchez-Alavez, Manuel, Moreno-Camacho, Jose L, García-Gil, Abraham, Schafer, Ashleigh R Murphy, McDonald, Eric, Corrigan, Jeremy, Malone, John D, Stous, Sarah, Shah, Seema, Moshiri, Niema, Weiss, Alana, Anderson, Catelyn, Aceves, Christine M, Spencer, Emily G, Hufbauer, Emory C, Lee, Justin J, Ramesh, Karthik S, Nguyen, Kelly N, Saucedo, Kieran, Robles-Sikisaka, Refugio, Fisch, Kathleen M, Gonias, Steven L, Birmingham, Amanda, McDonald, Daniel, Karthikeyan, Smruthi, Martin, Natasha K, Schooley, Robert T, Negrete, Agustin J, Reyna, Horacio J, Chavez, Jose R, Garcia, Maria L, Cornejo-Bravo, Jose M, Becker, David, Isaksson, Magnus, Washington, Nicole L, Lee, William, Garfein, Richard S, Esparza, Marco A Luna-Ruiz, Alcántar-Fernández, Jonathan, Henson, Benjamin, Jepsen, Kristen, Olivares-Flores, Beatriz, Barrera-Badillo, Gisela, Lopez-Martínez, Irma, Ramírez-González, José E, Flores-León, Rita, Kingsmore, Stephen F, Sanders, Alison, Pradenas, Allorah, White, Benjamin, Matthews, Gary, Hale, Matt, McLawhon, Ronald W, Reed, Sharon L, Winbush, Terri, McHardy, Ian H, Fielding, Russel A, Nicholson, Laura, Quigley, Michael M, Harding, Aaron, Mendoza, Art, Bakhtar, Omid, and Browne, Sara H

Subjects
Prevention, Human Genome, Genetics, Good Health and Well Being
Abstract

Summary: The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of SARS-CoV-2 lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of ‘local’ when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Published
2023

5. GRANDPA: GeneRAtive Network sampling using Degree and Property Augmentation applied to the analysis of partially confidential healthcare networks

Author

Bobak, Carly A., Zhao, Yifan, Levy, Joshua J., and O'Malley, A. James

Subjects
Statistics - Applications
Abstract

Protecting medical privacy can create obstacles in the analysis and distribution of healthcare graphs and statistical inferences accompanying them. We pose a graph simulation model which generates networks using degree and property augmentation (GRANDPA) and provide a flexible R package that allows users to create graphs that preserve vertex attribute relationships and approximating retaining topological properties observed in the original graph (e.g., community structure). We support our proposed algorithm using a case study based on Zachary's karate network and a patient-sharing graph generated from Medicare claims data in 2019. In both cases, we find that community structure is preserved, and normalized root mean square error between cumulative distributions of the degrees is low (0.0508 and 0.0514 respectively)., Comment: 13 pages, 5 figures

Published
2022

6. Wastewater surveillance for public health

Author

Levy, Joshua I, Andersen, Kristian G, Knight, Rob, and Karthikeyan, Smruthi

Subjects
Engineering, Public Health, Health Sciences, Chemical Engineering, Environmental Engineering, Good Health and Well Being, Disease Outbreaks, Wastewater, Wastewater-Based Epidemiological Monitoring, Humans, General Science & Technology
Abstract

Wastewater contains information on pathogen spread, evolution, and outbreak risk.

Published
2023

7. Contributors

Author

Abello, Eric H., primary, Abiri, Arash, additional, Adappa, Nithin D., additional, Ahmad, Meleha, additional, Almeida, Joao Paulo, additional, Alrasheed, Abdulaziz S., additional, Arrighi-Allisan, Annie, additional, Barton, Blair M., additional, Bastien, Amanda J., additional, Battaglia, Paolo, additional, Biousse, Valérie, additional, Bleier, Benjamin S., additional, Carrau, Ricardo L., additional, Castelnuovo, Paolo, additional, Chaaban, Mohamad R., additional, Chan, David, additional, Chemas-Velez, Maria Manuela, additional, Chen, Benson S., additional, Chou, Brian W., additional, Cote, David J., additional, Cutsforth-Gregory, Jeremy K., additional, Czaczkes, Camilla, additional, Dalfino, Gianluca, additional, Das, Paramita, additional, Junior de Andrade, Erion, additional, Filho, Leonardo de Macedo, additional, Eloy, Jean Anderson, additional, Evans, James J., additional, Fang, Christina H., additional, Filip, Peter, additional, Francis, Courtney E., additional, Gallia, Gary L., additional, Gardner, Paul A., additional, Gentili, Fred, additional, Georgalas, Christos, additional, Ginat, Daniel T., additional, Gogia, Angad S., additional, Govindaraj, Satish, additional, Grayson, Jessica W., additional, Gudis, David A., additional, Halderman, Ashleigh A., additional, Harvey, Richard J., additional, Hernandez, Stephen, additional, Higgins, Mamie, additional, Horowitz, Peleg Moshe, additional, Illing, Elisa A., additional, Ishii, Masaru, additional, Kamel, Peter, additional, Kang, Keiko M., additional, Karp, Emily, additional, Karsy, Michael J., additional, Kshettry, Varun R., additional, Kshirsagar, Rijul S., additional, Kuan, Edward C., additional, Lamarre, Eric, additional, Levy, Joshua M., additional, Link, Michael, additional, Little, Andrew S., additional, Liu, Ann, additional, Liu, James K., additional, Locatelli, Davide, additional, Luna, Licia P., additional, Mahomva, Chengetai, additional, McCulley, Timothy J., additional, Medina, Eduardo J., additional, Michael, Alex P., additional, Miglani, Amar, additional, Mihal, David, additional, Moe, Kris S., additional, Nadgir, Rohini, additional, Nicholson, Patrick, additional, Nyquist, Gurston G., additional, Oostra, Amanda, additional, Oswald, Aaron, additional, Palmer, James N., additional, Parsel, Sean M., additional, Patel, Prayag, additional, Petrauskas, Laura, additional, Porto, Edoardo, additional, Pradilla, Gustavo, additional, Prashant, Giyarpuram N., additional, Prevedello, Daniel, additional, Qureshi, Hannan A., additional, Rabinowitz, Mindy R., additional, Radovanovic, Ivan, additional, Ramanathan, Murugappan, additional, Rao, Shilpa M., additional, Ray, Amrita, additional, Rech, Matheus, additional, Recinos, Pablo F., additional, Barbero, J. Manuel Revuelta, additional, Rincon-Torroella, Jordina, additional, Rosen, Marc R., additional, Roxbury, Christopher R., additional, Ruggieri, Paul, additional, Safi, Chetan, additional, Santarelli, Griffin D., additional, Sarris, Christina E., additional, Schlosser, Rodney J., additional, Schwartz, Theodore H., additional, Shah, Janki, additional, Sindwani, Raj, additional, Snyderman, Carl H., additional, Sommerfeld, Connor, additional, Soriano, Roberto M., additional, Sreenath, Satyan B., additional, Stokken, Janalee, additional, Tabaee, Abtin, additional, Tang, Dennis M., additional, Timmons, Shelly D., additional, Ting, Jonathan, additional, Turri-Zanoni, Mario, additional, Vorster, Sarel J., additional, Vyskocil, Erich, additional, Warren, Zachary, additional, Watley, Duncan C., additional, White, Alexandra J., additional, Williams, Michael A., additional, Woodard, Troy D., additional, Woodworth, Bradford A., additional, Workman, Alan D., additional, Wormald, Peter-John, additional, Wrobel, Bozena B., additional, Zada, Gabriel, additional, Zanation, Adam, additional, Zhao, Jiawei, additional, and Zimmer, David I., additional

Published
2024
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9. Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission

Author

Karthikeyan, Smruthi, Levy, Joshua I, De Hoff, Peter, Humphrey, Greg, Birmingham, Amanda, Jepsen, Kristen, Farmer, Sawyer, Tubb, Helena M, Valles, Tommy, Tribelhorn, Caitlin E, Tsai, Rebecca, Aigner, Stefan, Sathe, Shashank, Moshiri, Niema, Henson, Benjamin, Mark, Adam M, Hakim, Abbas, Baer, Nathan A, Barber, Tom, Belda-Ferre, Pedro, Chacón, Marisol, Cheung, Willi, Cresini, Evelyn S, Eisner, Emily R, Lastrella, Alma L, Lawrence, Elijah S, Marotz, Clarisse A, Ngo, Toan T, Ostrander, Tyler, Plascencia, Ashley, Salido, Rodolfo A, Seaver, Phoebe, Smoot, Elizabeth W, McDonald, Daniel, Neuhard, Robert M, Scioscia, Angela L, Satterlund, Alysson M, Simmons, Elizabeth H, Abelman, Dismas B, Brenner, David, Bruner, Judith C, Buckley, Anne, Ellison, Michael, Gattas, Jeffrey, Gonias, Steven L, Hale, Matt, Hawkins, Faith, Ikeda, Lydia, Jhaveri, Hemlata, Johnson, Ted, Kellen, Vince, Kremer, Brendan, Matthews, Gary, McLawhon, Ronald W, Ouillet, Pierre, Park, Daniel, Pradenas, Allorah, Reed, Sharon, Riggs, Lindsay, Sanders, Alison, Sollenberger, Bradley, Song, Angela, White, Benjamin, Winbush, Terri, Aceves, Christine M, Anderson, Catelyn, Gangavarapu, Karthik, Hufbauer, Emory, Kurzban, Ezra, Lee, Justin, Matteson, Nathaniel L, Parker, Edyth, Perkins, Sarah A, Ramesh, Karthik S, Robles-Sikisaka, Refugio, Schwab, Madison A, Spencer, Emily, Wohl, Shirlee, Nicholson, Laura, McHardy, Ian H, Dimmock, David P, Hobbs, Charlotte A, Bakhtar, Omid, Harding, Aaron, Mendoza, Art, Bolze, Alexandre, Becker, David, Cirulli, Elizabeth T, Isaksson, Magnus, Schiabor Barrett, Kelly M, Washington, Nicole L, Malone, John D, Schafer, Ashleigh Murphy, Gurfield, Nikos, Stous, Sarah, Fielding-Miller, Rebecca, Garfein, Richard S, Gaines, Tommi, Anderson, Cheryl, and Martin, Natasha K

Subjects
Biodefense, Human Genome, Lung, Emerging Infectious Diseases, Vaccine Related, Biotechnology, Genetics, Prevention, Infection, Good Health and Well Being, COVID-19, Humans, RNA, Viral, SARS-CoV-2, Sequence Analysis, RNA, Wastewater, Wastewater-Based Epidemiological Monitoring, General Science & Technology
Abstract

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Published
2022

12. Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Author

Crits-Christoph, Alexander, Levy, Joshua I., Pekar, Jonathan E., Goldstein, Stephen A., Singh, Reema, Hensel, Zach, Gangavarapu, Karthik, Rogers, Matthew B., Moshiri, Niema, Garry, Robert F., Holmes, Edward C., Koopmans, Marion P.G., Lemey, Philippe, Peacock, Thomas P., Popescu, Saskia, Rambaut, Andrew, Robertson, David L., Suchard, Marc A., Wertheim, Joel O., Rasmussen, Angela L., Andersen, Kristian G., Worobey, Michael, and Débarre, Florence

Published
2024
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15. The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

Author

Worobey, Michael, Levy, Joshua I, Serrano, Lorena Malpica, Crits-Christoph, Alexander, Pekar, Jonathan E, Goldstein, Stephen A, Rasmussen, Angela L, Kraemer, Moritz UG, Newman, Chris, Koopmans, Marion PG, Suchard, Marc A, Wertheim, Joel O, Lemey, Philippe, Robertson, David L, Garry, Robert F, Holmes, Edward C, Rambaut, Andrew, and Andersen, Kristian G

Subjects
Prevention, Emerging Infectious Diseases, Biodefense, Vaccine Related, Lung, Good Health and Well Being, Animals, COVID-19, China, Humans, Pandemics, SARS-CoV-2, Seafood, Viral Zoonoses, General Science & Technology
Abstract

Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 is critical to preventing future zoonotic outbreaks before they become the next pandemic. The Huanan Seafood Wholesale Market in Wuhan, China, was identified as a likely source of cases in early reports, but later this conclusion became controversial. We show here that the earliest known COVID-19 cases from December 2019, including those without reported direct links, were geographically centered on this market. We report that live SARS-CoV-2-susceptible mammals were sold at the market in late 2019 and that within the market, SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals. Although there is insufficient evidence to define upstream events, and exact circumstances remain obscure, our analyses indicate that the emergence of SARS-CoV-2 occurred through the live wildlife trade in China and show that the Huanan market was the epicenter of the COVID-19 pandemic.

Published
2022

16. The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

Author

Pekar, Jonathan E, Magee, Andrew, Parker, Edyth, Moshiri, Niema, Izhikevich, Katherine, Havens, Jennifer L, Gangavarapu, Karthik, Malpica Serrano, Lorena Mariana, Crits-Christoph, Alexander, Matteson, Nathaniel L, Zeller, Mark, Levy, Joshua I, Wang, Jade C, Hughes, Scott, Lee, Jungmin, Park, Heedo, Park, Man-Seong, Ching Zi Yan, Katherine, Lin, Raymond Tzer Pin, Mat Isa, Mohd Noor, Noor, Yusuf Muhammad, Vasylyeva, Tetyana I, Garry, Robert F, Holmes, Edward C, Rambaut, Andrew, Suchard, Marc A, Andersen, Kristian G, Worobey, Michael, and Wertheim, Joel O

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Coronaviruses, Infectious Diseases, Lung, Emerging Infectious Diseases, 2.2 Factors relating to the physical environment, Infection, Good Health and Well Being, Animals, COVID-19, Computer Simulation, Genetic Variation, Genomics, Humans, Molecular Epidemiology, Pandemics, Phylogeny, SARS-CoV-2, Viral Zoonoses, General Science & Technology
Abstract

Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted "A" and "B." Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.

Published
2022

19. Utilizing river and wastewater as a SARS-CoV-2 surveillance tool in settings with limited formal sewage systems

Author

Barnes, Kayla G., Levy, Joshua I., Gauld, Jillian, Rigby, Jonathan, Kanjerwa, Oscar, Uzzell, Christopher B., Chilupsya, Chisomo, Anscombe, Catherine, Tomkins-Tinch, Christopher, Mbeti, Omar, Cairns, Edward, Thole, Herbert, McSweeney, Shannon, Chibwana, Marah G., Ashton, Philip M., Jere, Khuzwayo C., Meschke, John Scott, Diggle, Peter, Cornick, Jennifer, Chilima, Benjamin, Jambo, Kondwani, Andersen, Kristian G., Kawalazira, Gift, Paterson, Steve, Nyirenda, Tonney S., and Feasey, Nicholas

Published
2023
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21. SARS-CoV-2 genomic surveillance in wastewater as a model for monitoring evolution of endemic viruses

Author

Yousif, Mukhlid, Rachida, Said, Taukobong, Setshaba, Ndlovu, Nkosenhle, Iwu-Jaja, Chinwe, Howard, Wayne, Moonsamy, Shelina, Mhlambi, Nompilo, Gwala, Sipho, Levy, Joshua I., Andersen, Kristian G., Scheepers, Cathrine, von Gottberg, Anne, Wolter, Nicole, Bhiman, Jinal N., Amoako, Daniel Gyamfi, Ismail, Arshad, Suchard, Melinda, and McCarthy, Kerrigan

Published
2023
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25. Comparison of RT-PCR and antigen test sensitivity across nasopharyngeal, nares, and oropharyngeal swab, and saliva sample types during the SARS-CoV-2 omicron variant

Author

Damhorst, Gregory L., Lin, Jessica, Frediani, Jennifer K., Sullivan, Julie A., Westbrook, Adrianna, McLendon, Kaleb, Baugh, Tyler J., O'Sick, William H., Roback, John D., Piantadosi, Anne L., Waggoner, Jesse J., Bassit, Leda, Rao, Anuradha, Greenleaf, Morgan, O'Neal, Jared W., Swanson, Seegar, Pollock, Nira R., Martin, Greg S., Lam, Wilbur A., and Levy, Joshua M.

Published
2024
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26. Contrastive Unsupervised Learning for Speech Emotion Recognition

Author

Li, Mao, Yang, Bo, Levy, Joshua, Stolcke, Andreas, Rozgic, Viktor, Matsoukas, Spyros, Papayiannis, Constantinos, Bone, Daniel, and Wang, Chao

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled datasets. To circumvent this problem, we investigate how unsupervised representation learning on unlabeled datasets can benefit SER. We show that the contrastive predictive coding (CPC) method can learn salient representations from unlabeled datasets, which improves emotion recognition performance. In our experiments, this method achieved state-of-the-art concordance correlation coefficient (CCC) performance for all emotion primitives (activation, valence, and dominance) on IEMOCAP. Additionally, on the MSP- Podcast dataset, our method obtained considerable performance improvements compared to baselines.

Published
2021

30. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations

Author

Gangavarapu, Karthik, Latif, Alaa Abdel, Mullen, Julia L., Alkuzweny, Manar, Hufbauer, Emory, Tsueng, Ginger, Haag, Emily, Zeller, Mark, Aceves, Christine M., Zaiets, Karina, Cano, Marco, Zhou, Xinghua, Qian, Zhongchao, Sattler, Rachel, Matteson, Nathaniel L., Levy, Joshua I., Lee, Raphael T. C., Freitas, Lucas, Maurer-Stroh, Sebastian, Suchard, Marc A., Wu, Chunlei, Su, Andrew I., Andersen, Kristian G., and Hughes, Laura D.

Published
2023
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32. Speech Sentiment and Customer Satisfaction Estimation in Socialbot Conversations

Author

Kim, Yelin, Levy, Joshua, and Liu, Yang

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

For an interactive agent, such as task-oriented spoken dialog systems or chatbots, measuring and adapting to Customer Satisfaction (CSAT) is critical in order to understand user perception of an agent's behavior and increase user engagement and retention. However, an agent often relies on explicit customer feedback for measuring CSAT. Such explicit feedback may result in potential distraction to users and it can be challenging to capture continuously changing user's satisfaction. To address this challenge, we present a new approach to automatically estimate CSAT using acoustic and lexical information in the Alexa Prize Socialbot data. We first explore the relationship between CSAT and sentiment scores at both the utterance and conversation level. We then investigate static and temporal modeling methods that use estimated sentiment scores as a mid-level representation. The results show that the sentiment scores, particularly valence and satisfaction, are correlated with CSAT. We also demonstrate that our proposed temporal modeling approach for estimating CSAT achieves competitive performance, relative to static baselines as well as human performance. This work provides insights into open domain social conversations between real users and socialbots, and the use of both acoustic and lexical information for understanding the relationship between CSAT and sentiment scores., Comment: To be published in Interspeech 2020

Published
2020

33. Longevity Associated Geometry Identified in Satellite Images: Sidewalks, Driveways and Hiking Trails

Author

Levy, Joshua J., Lebeaux, Rebecca M., Hoen, Anne G., Christensen, Brock C., Vaickus, Louis J., and MacKenzie, Todd A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Importance: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: Investigate prediction of county-level mortality rates in the U.S. using satellite images. Design: Satellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Main Outcomes and Measures: County mortality was predicted using satellite images. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Conclusion and Relevance: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Tools that are able to identify image features associated with health-related outcomes can inform targeted public health interventions.

Published
2020

34. Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

Author

Matteson, Nathaniel L., Hassler, Gabriel W., Kurzban, Ezra, Schwab, Madison A., Perkins, Sarah A., Gangavarapu, Karthik, Levy, Joshua I., Parker, Edyth, Pride, David, Hakim, Abbas, De Hoff, Peter, Cheung, Willi, Castro-Martinez, Anelizze, Rivera, Andrea, Veder, Anthony, Rivera, Ariana, Wauer, Cassandra, Holmes, Jacqueline, Wilson, Jedediah, Ngo, Shayla N., Plascencia, Ashley, Lawrence, Elijah S., Smoot, Elizabeth W., Eisner, Emily R., Tsai, Rebecca, Chacón, Marisol, Baer, Nathan A., Seaver, Phoebe, Salido, Rodolfo A., Aigner, Stefan, Ngo, Toan T., Barber, Tom, Ostrander, Tyler, Fielding-Miller, Rebecca, Simmons, Elizabeth H., Zazueta, Oscar E., Serafin-Higuera, Idanya, Sanchez-Alavez, Manuel, Moreno-Camacho, Jose L., García-Gil, Abraham, Murphy Schafer, Ashleigh R., McDonald, Eric, Corrigan, Jeremy, Malone, John D., Stous, Sarah, Shah, Seema, Moshiri, Niema, Weiss, Alana, Anderson, Catelyn, Aceves, Christine M., Spencer, Emily G., Hufbauer, Emory C., Lee, Justin J., King, Alison J., Ramesh, Karthik S., Nguyen, Kelly N., Saucedo, Kieran, Robles-Sikisaka, Refugio, Fisch, Kathleen M., Gonias, Steven L., Birmingham, Amanda, McDonald, Daniel, Karthikeyan, Smruthi, Martin, Natasha K., Schooley, Robert T., Negrete, Agustin J., Reyna, Horacio J., Chavez, Jose R., Garcia, Maria L., Cornejo-Bravo, Jose M., Becker, David, Isaksson, Magnus, Washington, Nicole L., Lee, William, Garfein, Richard S., Luna-Ruiz Esparza, Marco A., Alcántar-Fernández, Jonathan, Henson, Benjamin, Jepsen, Kristen, Olivares-Flores, Beatriz, Barrera-Badillo, Gisela, Lopez-Martínez, Irma, Ramírez-González, José E., Flores-León, Rita, Kingsmore, Stephen F., Sanders, Alison, Pradenas, Allorah, White, Benjamin, Matthews, Gary, Hale, Matt, McLawhon, Ronald W., Reed, Sharon L., Winbush, Terri, McHardy, Ian H., Fielding, Russel A., Nicholson, Laura, Quigley, Michael M., Harding, Aaron, Mendoza, Art, Bakhtar, Omid, Browne, Sara H., Olivas Flores, Jocelyn, Rincon Rodríguez, Diana G., Gonzalez Ibarra, Martin, Robles Ibarra, Luis C., Arellano Vera, Betsy J., Gonzalez Garcia, Jonathan, Harvey-Vera, Alicia, Knight, Rob, Laurent, Louise C., Yeo, Gene W., Wertheim, Joel O., Ji, Xiang, Worobey, Michael, Suchard, Marc A., Andersen, Kristian G., Campos-Romero, Abraham, Wohl, Shirlee, and Zeller, Mark

Published
2023
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37. Diagnosis and Management of Depression in CRS: A Knowledge, Attitudes and Practices Survey

Author

Gill, Amarbir S, Levy, Joshua M, Wilson, Machelle, Strong, E Bradley, and Steele, Toby O

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Behavioral and Social Science, Depression, Serious Mental Illness, Mental Illness, Brain Disorders, Major Depressive Disorder, Clinical Research, Mental Health, 4.2 Evaluation of markers and technologies, Good Health and Well Being, depression, chronic rhinosinusitis, screen, diagnosis, management, quality of life
Abstract

Introduction  Comorbid major depressive disorder (MDD) is present in up to 25% of chronic rhinosinusitis (CRS) cases and provides prognostic information for patients undergoing endoscopic sinus surgery (ESS). Clinical visits offer an opportunity to identify at-risk patients. Objective  The purpose of the present study is to evaluate practice patterns among members of the American Rhinologic Society (ARS) in screening for/diagnosing MDD. Methods  A 21-question survey was distributed to 1,206 members of the ARS from May 26, 2018 to June 12, 2018. The impact of demographic factors, including hospital setting, fellowship status, and experience were assessed through chi-squared analysis. Results  A total of 80 members of the ARS completed the survey, yielding a response rate of 7%. Half of the respondents worked in academic settings and 43% had completed a rhinology fellowship. Twenty percent of the participants felt comfortable diagnosing or managing MDD, while only 10% of participants screened for MDD in patients with CRS. Respondents cited a lack of training (76%) and unfamiliarity with diagnostic criteria (76%) as barriers to the routine assessment of MDD. Most respondents (95%) considered comorbid psychiatric illness to negatively impact outcomes following ESS. Fellowship-trained respondents were significantly more likely to implement screening tools in their practice ( p  = 0.05), and believe in the negative impact of MDD on postoperative outcomes ( p  = 0.007), cost of care ( p  = 0.04) and quality of life ( p  = 0.047). Conclusion  Amongst ARS members, 95% of the respondents consider comorbid MDD to negatively impact patient outcomes following ESS. Regardless, a large proportion of surgeons neither screen nor feel comfortable diagnosing MDD.

Published
2021

38. Wastewater sequencing uncovers early, cryptic SARS-CoV-2 variant transmission

Author

Karthikeyan, Smruthi, Levy, Joshua I, De Hoff, Peter, Humphrey, Greg, Birmingham, Amanda, Jepsen, Kristen, Farmer, Sawyer, Tubb, Helena M, Valles, Tommy, Tribelhorn, Caitlin E, Tsai, Rebecca, Aigner, Stefan, Sathe, Shashank, Moshiri, Niema, Henson, Benjamin, Mark, Adam M, Hakim, Abbas, Baer, Nathan A, Barber, Tom, Belda-Ferre, Pedro, Chacón, Marisol, Cheung, Willi, Cresini, Evelyn S, Eisner, Emily R, Lastrella, Alma L, Lawrence, Elijah S, Marotz, Clarisse A, Ngo, Toan T, Ostrander, Tyler, Plascencia, Ashley, Salido, Rodolfo A, Seaver, Phoebe, Smoot, Elizabeth W, McDonald, Daniel, Neuhard, Robert M, Scioscia, Angela L, Satterlund, Alysson M, Simmons, Elizabeth H, Abelman, Dismas B, Brenner, David, Bruner, Judith C, Buckley, Anne, Ellison, Michael, Gattas, Jeffrey, Gonias, Steven L, Hale, Matt, Hawkins, Faith, Ikeda, Lydia, Jhaveri, Hemlata, Johnson, Ted, Kellen, Vince, Kremer, Brendan, Matthews, Gary, McLawhon, Ronald W, Ouillet, Pierre, Park, Daniel, Pradenas, Allorah, Reed, Sharon, Riggs, Lindsay, Sanders, Alison, Sollenberger, Bradley, Song, Angela, White, Benjamin, Winbush, Terri, Aceves, Christine M, Anderson, Catelyn, Gangavarapu, Karthik, Hufbauer, Emory, Kurzban, Ezra, Lee, Justin, Matteson, Nathaniel L, Parker, Edyth, Perkins, Sarah A, Ramesh, Karthik S, Robles-Sikisaka, Refugio, Schwab, Madison A, Spencer, Emily, Wohl, Shirlee, Nicholson, Laura, Mchardy, Ian H, Dimmock, David P, Hobbs, Charlotte A, Bakhtar, Omid, Harding, Aaron, Mendoza, Art, Bolze, Alexandre, Becker, David, Cirulli, Elizabeth T, Isaksson, Magnus, Barrett, Kelly M Schiabor, Washington, Nicole L, Malone, John D, Schafer, Ashleigh Murphy, Gurfield, Nikos, Stous, Sarah, Fielding-Miller, Rebecca, Garfein, Richard S, Gaines, Tommi, Anderson, Cheryl, and Martin, Natasha K

Subjects
Vaccine Related, Infectious Diseases, Biodefense, Emerging Infectious Diseases, Lung, Genetics, Human Genome, Clinical Research, Prevention, Biotechnology, Infection, Good Health and Well Being
Abstract

Summary As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Published
2021

40. Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation

Author

Bodigutla, Praveen Kumar, Wang, Longshaokan, Ridgeway, Kate, Levy, Joshua, Joshi, Swanand, Geramifard, Alborz, and Matsoukas, Spyros

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Statistics - Machine Learning
Abstract

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low inter-rater reliability, limiting generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, based on which we developed turn-level User Satisfaction metric. We introduced five new domain-independent feature sets and experimented with six machine learning models to estimate the new satisfaction metric. Using Response Quality annotation scheme, across randomly sampled single and multi-turn conversations from 26 domains, we achieved high inter-annotator agreement (Spearman's rho 0.94). The Response Quality labels were highly correlated (0.76) with explicit turn-level user ratings. Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels. Multi Layer Perceptron and Gradient Boosting regression models generalized to an unseen domain better (linear correlation 0.67) than other models. Finally, our ablation study verified that our novel features significantly improved model performance., Comment: Implications of Deep Learning for Dialog Modeling - Special session at SIGdial 2019

Published
2019

42. Effect of Omega-3 Supplementation in Patients With Smell Dysfunction Following Endoscopic Sellar and Parasellar Tumor Resection: A Multicenter Prospective Randomized Controlled Trial

Author

Yan, Carol H, Rathor, Aakanksha, Krook, Kaelyn, Ma, Yifei, Rotella, Melissa R, Dodd, Robert L, Hwang, Peter H, Nayak, Jayakar V, Oyesiku, Nelson M, DelGaudio, John M, Levy, Joshua M, Wise, Justin, Wise, Sarah K, and Patel, Zara M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Prevention, Cancer, Clinical Research, Complementary and Integrative Health, Nutrition, Neurosciences, Rare Diseases, Adult, Dietary Supplements, Fatty Acids, Omega-3, Female, Humans, Male, Middle Aged, Neuroendoscopy, Olfaction Disorders, Pituitary Neoplasms, Postoperative Cognitive Complications, Prospective Studies, Skull Base Neoplasms, Treatment Outcome, Olfactory loss, Skull base, Pituitary, Endoscopic, Smell, Therapeutics, Sella, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BackgroundEndoscopic endonasal approaches pose the potential risk of olfactory loss. Loss of olfaction and potentially taste can be permanent and greatly affect patients' quality of life. Treatments for olfactory loss have had limited success. Omega-3 supplementation may be a therapeutic option with its effect on wound healing and nerve regeneration.ObjectiveTo evaluate the impact on olfaction in patients treated with omega-3 supplementation following endoscopic skull base tumor resection.MethodsIn this multi-institutional, prospective, randomized controlled trial, 110 patients with sellar or parasellar tumors undergoing endoscopic resection were randomized to nasal saline irrigations or nasal saline irrigations plus omega-3 supplementation. The University of Pennsylvania Smell Identification Test (UPSIT) was administered preoperatively and at 6 wk, 3 mo, and 6 mo postoperatively.ResultsEighty-seven patients completed all 6 mo of follow-up (41 control arm, 46 omega-3 arm). At 6 wk postoperatively, 25% of patients in both groups experienced a clinically significant loss in olfaction. At 3 and 6 mo, patients receiving omega-3 demonstrated significantly less persistent olfactory loss compared to patients without supplementation (P = .02 and P = .01, respectively). After controlling for multiple confounding variables, omega-3 supplementation was found to be protective against olfactory loss (odds ratio [OR] 0.05, 95% CI 0.003-0.81, P = .03). Tumor functionality was a significant independent predictor for olfactory loss (OR 32.7, 95% CI 1.15-929.5, P = .04).ConclusionOmega-3 supplementation appears to be protective for the olfactory system during the healing period in patients who undergo endoscopic resection of sellar and parasellar masses.

Published
2020

43. Gradual polyploid genome evolution revealed by pan-genomic analysis of Brachypodium hybridum and its diploid progenitors.

Author

Gordon, Sean P, Contreras-Moreira, Bruno, Levy, Joshua J, Djamei, Armin, Czedik-Eysenberg, Angelika, Tartaglio, Virginia S, Session, Adam, Martin, Joel, Cartwright, Amy, Katz, Andrew, Singan, Vasanth R, Goltsman, Eugene, Barry, Kerrie, Dinh-Thi, Vinh Ha, Chalhoub, Boulos, Diaz-Perez, Antonio, Sancho, Ruben, Lusinska, Joanna, Wolny, Elzbieta, Nibau, Candida, Doonan, John H, Mur, Luis AJ, Plott, Chris, Jenkins, Jerry, Hazen, Samuel P, Lee, Scott J, Shu, Shengqiang, Goodstein, David, Rokhsar, Daniel, Schmutz, Jeremy, Hasterok, Robert, Catalan, Pilar, and Vogel, John P

Subjects
Chromosomes, Plant, Retroelements, Hybridization, Genetic, Genomics, Evolution, Molecular, Phylogeny, Species Specificity, Diploidy, Polyploidy, Polymorphism, Single Nucleotide, Genome, Plant, Genome, Chloroplast, Brachypodium, Chromosomes, Plant, Hybridization, Genetic, Evolution, Molecular, Polymorphism, Single Nucleotide, Genome, Chloroplast
Abstract

Our understanding of polyploid genome evolution is constrained because we cannot know the exact founders of a particular polyploid. To differentiate between founder effects and post polyploidization evolution, we use a pan-genomic approach to study the allotetraploid Brachypodium hybridum and its diploid progenitors. Comparative analysis suggests that most B. hybridum whole gene presence/absence variation is part of the standing variation in its diploid progenitors. Analysis of nuclear single nucleotide variants, plastomes and k-mers associated with retrotransposons reveals two independent origins for B. hybridum, ~1.4 and ~0.14 million years ago. Examination of gene expression in the younger B. hybridum lineage reveals no bias in overall subgenome expression. Our results are consistent with a gradual accumulation of genomic changes after polyploidization and a lack of subgenome expression dominance. Significantly, if we did not use a pan-genomic approach, we would grossly overestimate the number of genomic changes attributable to post polyploidization evolution.

Published
2020

44. PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution

Author

Gordon, Sean P, Levy, Joshua J, and Vogel, John P

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, DNA, Plant, Evolution, Molecular, Genome, Plant, Genomics, Phylogeny, Polyploidy, Repetitive Sequences, Nucleic Acid, Tobacco, Triticum, Unsupervised Machine Learning, Allopolyploid, K-mer, Transposon, Binning, Evolution, Repetitive DNA, Subgenome, Wheat, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundOur understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In theory, existing methods for assigning sequences to individual species from metagenome samples could be used to separate subgenomes in polyploid organisms, however, these methods rely on differences in coarse genome properties like GC content or sequences from related species. Thus, these approaches do not work for subgenomes where gross features are indistinguishable and related genomes are lacking. Here we describe a method that uses rapidly evolving repetitive DNA to circumvent these limitations.ResultsBy using short, repetitive, DNA sequences as species-specific signals we separated closely related genomes from test datasets and subgenomes from two polyploid plants, tobacco and wheat, without any prior knowledge.ConclusionThis approach is ideal for separating the subgenomes of polyploid species with unsequenced or unknown progenitor genomes.

Published
2019

48. Comparison of Symptoms and Antibody Response Following Administration of Moderna or Pfizer SARS-CoV-2 Vaccines

Author

Kelliher, Michael T., Levy, Joshua J., Nerenz, Robert D., Poore, Bradley, Johnston, Abigail A., Rogers, Amanda R., Stella, Mary E.O., Snow, Sarah E., Cervinski, Mark A., and Hubbard, Jacqueline A.

Subjects
Viral antibodies -- Health aspects, Immune response -- Health aspects, Antibodies -- Health aspects, Health, Comirnaty (Vaccine) -- Complications and side effects -- Patient outcomes, Spikevax (Vaccine) -- Complications and side effects -- Patient outcomes
Abstract

* Context.--Moderna (mRNA-1272) and Pfizer (BNT162b2) SARS-CoV-2 vaccines demonstrate favorable safety and efficacy profiles, but direct comparison data are lacking. Objective.--To determine the vaccines' side effect profiles and expected antibody responses. These data may help personalize vaccine selection and identify individuals with a suboptimal vaccine response. Design.--One hundred forty-nine healthy, largely seronegative adults were assigned Moderna (n = 79) or Pfizer (n = 70). Following the second dose, participants completed a survey documenting their side effects. Serum was collected 0 to 4 days prior to dose 2, and 14 [+ or -] 4 days, 30 6 4 days, 90 [+ or -] 10 days, and 180 [+ or -] 20 days after dose 2. Convalescent serum specimens were collected 32 to 54 days from donors after a polymerase chain reaction-confirmed SARS-CoV-2 infection (n = 20). Anti-spike antibodies were measured using the Roche Diagnostics Elecys Anti-SARS-CoV-2 S assay on a Roche cobas e801 instrument. Results.--Participants receiving the Moderna vaccine experienced side effects with greater frequency and severity. Both vaccines elicited a robust antibody response, but median signal was higher in Moderna recipients. Symptom severity decreased with age. Antibody response in Pfizer recipients negatively correlated with age. Antibody response decreased after 6 months (84% reduction in Moderna, 79% Pfizer), but values remained greater than for convalescent donors. Antibody response did not correlate with gender or symptom severity. Conclusions.--Moderna may be preferred in individuals in need of greater immune stimulation (eg, older individuals), whereas Pfizer may be preferred in those concerned about vaccine reactions. Anti-spike antibody signal varies by vaccine, so specific reference intervals will be needed to identify individuals with a suboptimal response., Since its identification as the cause of a novel respiratory pneumonia first documented in Wuhan, China, in December 2019, SARS-CoV-2 has spread rapidly throughout the globe, causing 221 million confirmed [...]

Published
2022
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49. Artificial Intelligence, Bioinformatics, and Pathology: Emerging Trends Part II––Current Applications in Anatomic and Molecular Pathology

Author

Levy, Joshua, Lu, Yunrui, Montivero, Marietta, Ramwala, Ojas, McFadden, Jason, Miles, Carly, Diamond, Adam Gilbert, Reddy, Ramya, Reddy, Ram, Hudson, Taylor, Azher, Zarif, Pamal, Akash, Gabbita, Sameer, Cronin, Tess, Ould Ismail, Abdol Aziz, Goel, Tarushii, Jacob, Sanjay, Suvarna, Anish, Kim, Taein, Zhang, Edward, Reddy, Neha, Ratna, Sumanth, Zavras, Jason, and Vaickus, Louis

Published
2022
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50. Artificial Intelligence, Bioinformatics, and Pathology: Emerging Trends Part I—an Introduction to Machine Learning Technologies

Author

Levy, Joshua, Lu, Yunrui, Montivero, Marietta, Ramwala, Ojas, McFadden, Jason, Miles, Carly, Diamond, Adam Gilbert, Reddy, Ramya, Reddy, Ram, Hudson, Taylor, Azher, Zarif, Pamal, Akash, Gabbita, Sameer, Cronin, Tess, Ould Ismail, Abdol Aziz, Goel, Tarushii, Jacob, Sanjay, Suvarna, Anish, Ratna, Sumanth, Zavras, Jason, and Vaickus, Louis

Published
2022
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