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186 results on '"Greene, Casey S"'

2. The Coming of Age of Nucleic Acid Vaccines during COVID-19

Author

Rando, Halie M., Lordan, Ronan, Kolla, Likhitha, Sell, Elizabeth, Lee, Alexandra J., Wellhausen, Nils, Naik, Amruta, Kamil, Jeremy P., Consortium, COVID-19 Review, Gitter, Anthony, and Greene, Casey S.

Subjects
Quantitative Biology - Biomolecules
Abstract

In the 21st century, several emergent viruses have posed a global threat. Each pathogen has emphasized the value of rapid and scalable vaccine development programs. The ongoing SARS-CoV-2 pandemic has made the importance of such efforts especially clear. New biotechnological advances in vaccinology allow for recent advances that provide only the nucleic acid building blocks of an antigen, eliminating many safety concerns. During the COVID-19 pandemic, these DNA and RNA vaccines have facilitated the development and deployment of vaccines at an unprecedented pace. This success was attributable at least in part to broader shifts in scientific research relative to prior epidemics; the genome of SARS-CoV-2 was available as early as January 2020, facilitating global efforts in the development of DNA and RNA vaccines within two weeks of the international community becoming aware of the new viral threat. Additionally, these technologies that were previously only theoretical are not only safe but also highly efficacious. Although historically a slow process, the rapid development of vaccines during the COVID-19 crisis reveals a major shift in vaccine technologies. Here, we provide historical context for the emergence of these paradigm-shifting vaccines. We describe several DNA and RNA vaccines and in terms of their efficacy, safety, and approval status. We also discuss patterns in worldwide distribution. The advances made since early 2020 provide an exceptional illustration of how rapidly vaccine development technology has advanced in the last two decades in particular and suggest a new era in vaccines against emerging pathogens.

Published
2022
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3. Application of Traditional Vaccine Development Strategies to SARS-CoV-2

Author

Rando, Halie M., Lordan, Ronan, Lee, Alexandra J., Naik, Amruta, Wellhausen, Nils, Sell, Elizabeth, Kolla, Likhitha, Consortium, COVID-19 Review, Gitter, Anthony, and Greene, Casey S.

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

Over the past 150 years, vaccines have revolutionized the relationship between people and disease. During the COVID-19 pandemic, technologies such as mRNA vaccines have received attention due to their novelty and successes. However, more traditional vaccine development platforms have also yielded important tools in the worldwide fight against the SARS-CoV-2 virus. A variety of approaches have been used to develop COVID-19 vaccines that are now authorized for use in countries around the world. In this review, we highlight strategies that focus on the viral capsid and outwards, rather than on the nucleic acids inside. These approaches fall into two broad categories: whole-virus vaccines and subunit vaccines. Whole-virus vaccines use the virus itself, either in an inactivated or attenuated state. Subunit vaccines contain instead an isolated, immunogenic component of the virus. Here, we highlight vaccine candidates that apply these approaches against SARS-CoV-2 in different ways. In a companion manuscript, we review the more recent and novel development of nucleic-acid based vaccine technologies. We further consider the role that these COVID-19 vaccine development programs have played in prophylaxis at the global scale. Well-established vaccine technologies have proved especially important to making vaccines accessible in low- and middle-income countries. Vaccine development programs that use established platforms have been undertaken in a much wider range of countries than those using nucleic-acid-based technologies, which have been led by wealthy Western countries. Therefore, these vaccine platforms, though less novel from a biotechnological standpoint, have proven to be extremely important to the management of SARS-CoV-2.

Published
2022
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5. Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

Author

Rando, Halie M., Brueffer, Christian, Lordan, Ronan, Dattoli, Anna Ada, Manheim, David, Meyer, Jesse G., Mundo, Ariel I., Perrin, Dimitri, Mai, David, Wellhausen, Nils, Consortium, COVID-19 Review, Gitter, Anthony, and Greene, Casey S.

Subjects
Quantitative Biology - Biomolecules
Abstract

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.

Published
2022

6. Using genome-wide expression compendia to study microorganisms

Author

Lee, Alexandra J., Reiter, Taylor, Doing, Georgia, Oh, Julia, Hogan, Deborah A., and Greene, Casey S.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.

Published
2022

11. Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine

Author

Wiley, Laura K., Shortt, Jonathan A., Roberts, Emily R., Lowery, Jan, Kudron, Elizabeth, Lin, Meng, Mayer, David, Wilson, Melissa, Brunetti, Tonya M., Chavan, Sameer, Phang, Tzu L., Pozdeyev, Nikita, Lesny, Joseph, Wicks, Stephen J., Moore, Ethan T., Morgenstern, Joshua L., Roff, Alanna N., Shalowitz, Elise L., Stewart, Adrian, Williams, Cole, Edelmann, Michelle N., Hull, Madelyne, Patton, J. Tacker, Axell, Lisen, Ku, Lisa, Lee, Yee Ming, Jirikowic, Jean, Tanaka, Anna, Todd, Emily, White, Sarah, Peterson, Brett, Hearst, Emily, Zane, Richard, Greene, Casey S., Mathias, Rasika, Coors, Marilyn, Taylor, Matthew, Ghosh, Debashis, Kahn, Michael G., Brooks, Ian M., Aquilante, Christina L., Kao, David, Rafaels, Nicholas, Crooks, Kristy R., Hess, Steve, Barnes, Kathleen C., and Gignoux, Christopher R.

Published
2024
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12. Biomonitoring and precision health in deep space supported by artificial intelligence

Author

Scott, Ryan T., Sanders, Lauren M., Antonsen, Erik L., Hastings, Jaden J. A., Park, Seung-min, Mackintosh, Graham, Reynolds, Robert J., Hoarfrost, Adrienne L., Sawyer, Aenor, Greene, Casey S., Glicksberg, Benjamin S., Theriot, Corey A., Berrios, Daniel C., Miller, Jack, Babdor, Joel, Barker, Richard, Baranzini, Sergio E., Beheshti, Afshin, Chalk, Stuart, Delgado-Aparicio, Guillermo M., Haendel, Melissa, Hamid, Arif A., Heller, Philip, Jamieson, Daniel, Jarvis, Katelyn J., Kalantari, John, Khezeli, Kia, Komarova, Svetlana V., Komorowski, Matthieu, Kothiyal, Prachi, Mahabal, Ashish, Manor, Uri, Garcia Martin, Hector, Mason, Christopher E., Matar, Mona, Mias, George I., Myers, Jr, Jerry G., Nelson, Charlotte, Oribello, Jonathan, Parsons-Wingerter, Patricia, Prabhu, R. K., Qutub, Amina Ann, Rask, Jon, Saravia-Butler, Amanda, Saria, Suchi, Singh, Nitin Kumar, Snyder, Michael, Soboczenski, Frank, Soman, Karthik, Van Valen, David, Venkateswaran, Kasthuri, Warren, Liz, Worthey, Liz, Yang, Jason H., Zitnik, Marinka, and Costes, Sylvain V.

Published
2023
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13. Biological research and self-driving labs in deep space supported by artificial intelligence

Author

Sanders, Lauren M., Scott, Ryan T., Yang, Jason H., Qutub, Amina Ann, Garcia Martin, Hector, Berrios, Daniel C., Hastings, Jaden J. A., Rask, Jon, Mackintosh, Graham, Hoarfrost, Adrienne L., Chalk, Stuart, Kalantari, John, Khezeli, Kia, Antonsen, Erik L., Babdor, Joel, Barker, Richard, Baranzini, Sergio E., Beheshti, Afshin, Delgado-Aparicio, Guillermo M., Glicksberg, Benjamin S., Greene, Casey S., Haendel, Melissa, Hamid, Arif A., Heller, Philip, Jamieson, Daniel, Jarvis, Katelyn J., Komarova, Svetlana V., Komorowski, Matthieu, Kothiyal, Prachi, Mahabal, Ashish, Manor, Uri, Mason, Christopher E., Matar, Mona, Mias, George I., Miller, Jack, Myers, Jr., Jerry G., Nelson, Charlotte, Oribello, Jonathan, Park, Seung-min, Parsons-Wingerter, Patricia, Prabhu, R. K., Reynolds, Robert J., Saravia-Butler, Amanda, Saria, Suchi, Sawyer, Aenor, Singh, Nitin Kumar, Snyder, Michael, Soboczenski, Frank, Soman, Karthik, Theriot, Corey A., Van Valen, David, Venkateswaran, Kasthuri, Warren, Liz, Worthey, Liz, Zitnik, Marinka, and Costes, Sylvain V.

Published
2023
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14. OpenPBTA: The Open Pediatric Brain Tumor Atlas

Author

Shapiro, Joshua A., Gaonkar, Krutika S., Spielman, Stephanie J., Savonen, Candace L., Bethell, Chante J., Jin, Run, Rathi, Komal S., Zhu, Yuankun, Egolf, Laura E., Farrow, Bailey K., Miller, Daniel P., Yang, Yang, Koganti, Tejaswi, Noureen, Nighat, Koptyra, Mateusz P., Duong, Nhat, Santi, Mariarita, Kim, Jung, Robins, Shannon, Storm, Phillip B., Mack, Stephen C., Lilly, Jena V., Xie, Hongbo M., Jain, Payal, Raman, Pichai, Rood, Brian R., Lulla, Rishi R., Nazarian, Javad, Kraya, Adam A., Vaksman, Zalman, Heath, Allison P., Kline, Cassie, Scolaro, Laura, Viaene, Angela N., Huang, Xiaoyan, Way, Gregory P., Foltz, Steven M., Zhang, Bo, Poetsch, Anna R., Mueller, Sabine, Ennis, Brian M., Prados, Michael, Diskin, Sharon J., Zheng, Siyuan, Guo, Yiran, Kannan, Shrivats, Waanders, Angela J., Margol, Ashley S., Kim, Meen Chul, Hanson, Derek, Van Kuren, Nicholas, Wong, Jessica, Kaufman, Rebecca S., Coleman, Noel, Blackden, Christopher, Cole, Kristina A., Mason, Jennifer L., Madsen, Peter J., Koschmann, Carl J., Stewart, Douglas R., Wafula, Eric, Brown, Miguel A., Resnick, Adam C., Greene, Casey S., Rokita, Jo Lynne, and Taroni, Jaclyn N.

Published
2023
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16. BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.

Author

Davidson, Natalie R., Zhang, Fan, and Greene, Casey S.

Subjects
AUTOENCODER, EXPERIMENTAL design, COMPARATIVE method, THERAPEUTICS, CORPORA
Abstract

While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution. [ABSTRACT FROM AUTHOR]

Published
2025
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20. The future is now: Implementation of standard of care, preemptive pharmacogenomic testing in patients with gastrointestinal cancers.

Author

Hearst, Emily C, primary, Martin, James L, additional, Crooks, Kristy R, additional, Greene, Casey S, additional, Hess, Kaitlyn W, additional, Johnson, Natalie, additional, Kao, David P, additional, Rafaels, Nicholas, additional, Carson, Katelyn, additional, Meguid, Cheryl L, additional, Taucher, Kate, additional, Davis, S. Lindsey, additional, Lieu, Christopher Hanyoung, additional, and Aquilante, Christina L, additional

Published
2024
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21. A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring.

Author

Pividori, Milton and Greene, Casey S

Abstract

Objective Investigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts. Materials and Methods For this purpose, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly texts. Our AI-based revision workflow employs a prompt generator that incorporates manuscript metadata into templates, generating section-specific instructions for the language model. The model then generates revised versions of each paragraph for human authors to review. We evaluated this methodology through 5 case studies of existing manuscripts, including the revision of this manuscript. Results Our results indicate that these models, despite some limitations, can grasp complex academic concepts and enhance text quality. All changes to the manuscript are tracked using a version control system, ensuring transparency in distinguishing between human- and machine-generated text. Conclusions Given the significant time researchers invest in crafting prose, incorporating large language models into the scholarly writing process can significantly improve the type of knowledge work performed by academics. Our approach also enables scholars to concentrate on critical aspects of their work, such as the novelty of their ideas, while automating tedious tasks like adhering to specific writing styles. Although the use of AI-assisted tools in scientific authoring is controversial, our approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI's role in scientific writing. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms

Author

Davidson, Natalie R., primary, Barnard, Mollie E., additional, Hippen, Ariel A., additional, Campbell, Amy, additional, Johnson, Courtney E., additional, Way, Gregory P., additional, Dalley, Brian K., additional, Berchuck, Andrew, additional, Salas, Lucas A., additional, Peres, Lauren C., additional, Marks, Jeffrey R., additional, Schildkraut, Joellen M., additional, Greene, Casey S., additional, and Doherty, Jennifer A., additional

Published
2024
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31. Figure S6 from Induction of ADAM10 by Radiation Therapy Drives Fibrosis, Resistance, and Epithelial-to-Mesenchyal Transition in Pancreatic Cancer

Author

Mueller, Adam C., primary, Piper, Miles, primary, Goodspeed, Andrew, primary, Bhuvane, Shiv, primary, Williams, Jason S., primary, Bhatia, Shilpa, primary, Phan, Andy V., primary, Van Court, Benjamin, primary, Zolman, Kathryn L., primary, Peña, Brisa, primary, Oweida, Ayman J., primary, Zakem, Sara, primary, Meguid, Cheryl, primary, Knitz, Michael W., primary, Darragh, Laurel, primary, Bickett, Thomas E., primary, Gadwa, Jacob, primary, Mestroni, Luisa, primary, Taylor, Matthew R.G., primary, Jordan, Kimberly R., primary, Dempsey, Peter, primary, Lucia, M. Scott, primary, McCarter, Martin D., primary, Chiaro, Marco Del, primary, Messersmith, Wells A., primary, Schulick, Richard D., primary, Goodman, Karyn A., primary, Gough, Michael J., primary, Greene, Casey S., primary, Costello, James C., primary, Neto, Antonio Galveo, primary, Lagares, David, primary, Hansen, Kirk C., primary, Van Bokhoven, Adrie, primary, and Karam, Sana D., primary

Published
2023
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32. Supplementary Legends from Induction of ADAM10 by Radiation Therapy Drives Fibrosis, Resistance, and Epithelial-to-Mesenchyal Transition in Pancreatic Cancer

Author

Mueller, Adam C., primary, Piper, Miles, primary, Goodspeed, Andrew, primary, Bhuvane, Shiv, primary, Williams, Jason S., primary, Bhatia, Shilpa, primary, Phan, Andy V., primary, Van Court, Benjamin, primary, Zolman, Kathryn L., primary, Peña, Brisa, primary, Oweida, Ayman J., primary, Zakem, Sara, primary, Meguid, Cheryl, primary, Knitz, Michael W., primary, Darragh, Laurel, primary, Bickett, Thomas E., primary, Gadwa, Jacob, primary, Mestroni, Luisa, primary, Taylor, Matthew R.G., primary, Jordan, Kimberly R., primary, Dempsey, Peter, primary, Lucia, M. Scott, primary, McCarter, Martin D., primary, Chiaro, Marco Del, primary, Messersmith, Wells A., primary, Schulick, Richard D., primary, Goodman, Karyn A., primary, Gough, Michael J., primary, Greene, Casey S., primary, Costello, James C., primary, Neto, Antonio Galveo, primary, Lagares, David, primary, Hansen, Kirk C., primary, Van Bokhoven, Adrie, primary, and Karam, Sana D., primary

Published
2023
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33. Data from Induction of ADAM10 by Radiation Therapy Drives Fibrosis, Resistance, and Epithelial-to-Mesenchyal Transition in Pancreatic Cancer

Author

Mueller, Adam C., primary, Piper, Miles, primary, Goodspeed, Andrew, primary, Bhuvane, Shiv, primary, Williams, Jason S., primary, Bhatia, Shilpa, primary, Phan, Andy V., primary, Van Court, Benjamin, primary, Zolman, Kathryn L., primary, Peña, Brisa, primary, Oweida, Ayman J., primary, Zakem, Sara, primary, Meguid, Cheryl, primary, Knitz, Michael W., primary, Darragh, Laurel, primary, Bickett, Thomas E., primary, Gadwa, Jacob, primary, Mestroni, Luisa, primary, Taylor, Matthew R.G., primary, Jordan, Kimberly R., primary, Dempsey, Peter, primary, Lucia, M. Scott, primary, McCarter, Martin D., primary, Chiaro, Marco Del, primary, Messersmith, Wells A., primary, Schulick, Richard D., primary, Goodman, Karyn A., primary, Gough, Michael J., primary, Greene, Casey S., primary, Costello, James C., primary, Neto, Antonio Galveo, primary, Lagares, David, primary, Hansen, Kirk C., primary, Van Bokhoven, Adrie, primary, and Karam, Sana D., primary

Published
2023
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34. Supplementary Tables from Induction of ADAM10 by Radiation Therapy Drives Fibrosis, Resistance, and Epithelial-to-Mesenchyal Transition in Pancreatic Cancer

Author

Mueller, Adam C., primary, Piper, Miles, primary, Goodspeed, Andrew, primary, Bhuvane, Shiv, primary, Williams, Jason S., primary, Bhatia, Shilpa, primary, Phan, Andy V., primary, Van Court, Benjamin, primary, Zolman, Kathryn L., primary, Peña, Brisa, primary, Oweida, Ayman J., primary, Zakem, Sara, primary, Meguid, Cheryl, primary, Knitz, Michael W., primary, Darragh, Laurel, primary, Bickett, Thomas E., primary, Gadwa, Jacob, primary, Mestroni, Luisa, primary, Taylor, Matthew R.G., primary, Jordan, Kimberly R., primary, Dempsey, Peter, primary, Lucia, M. Scott, primary, McCarter, Martin D., primary, Chiaro, Marco Del, primary, Messersmith, Wells A., primary, Schulick, Richard D., primary, Goodman, Karyn A., primary, Gough, Michael J., primary, Greene, Casey S., primary, Costello, James C., primary, Neto, Antonio Galveo, primary, Lagares, David, primary, Hansen, Kirk C., primary, Van Bokhoven, Adrie, primary, and Karam, Sana D., primary

Published
2023
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35. Supplementary Methods from Induction of ADAM10 by Radiation Therapy Drives Fibrosis, Resistance, and Epithelial-to-Mesenchyal Transition in Pancreatic Cancer

Author

Mueller, Adam C., primary, Piper, Miles, primary, Goodspeed, Andrew, primary, Bhuvane, Shiv, primary, Williams, Jason S., primary, Bhatia, Shilpa, primary, Phan, Andy V., primary, Van Court, Benjamin, primary, Zolman, Kathryn L., primary, Peña, Brisa, primary, Oweida, Ayman J., primary, Zakem, Sara, primary, Meguid, Cheryl, primary, Knitz, Michael W., primary, Darragh, Laurel, primary, Bickett, Thomas E., primary, Gadwa, Jacob, primary, Mestroni, Luisa, primary, Taylor, Matthew R.G., primary, Jordan, Kimberly R., primary, Dempsey, Peter, primary, Lucia, M. Scott, primary, McCarter, Martin D., primary, Chiaro, Marco Del, primary, Messersmith, Wells A., primary, Schulick, Richard D., primary, Goodman, Karyn A., primary, Gough, Michael J., primary, Greene, Casey S., primary, Costello, James C., primary, Neto, Antonio Galveo, primary, Lagares, David, primary, Hansen, Kirk C., primary, Van Bokhoven, Adrie, primary, and Karam, Sana D., primary

Published
2023
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36. Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms

Author

Davidson, Natalie R., primary, Barnard, Mollie E., additional, Hippen, Ariel A., additional, Campbell, Amy, additional, Johnson, Courtney E., additional, Way, Gregory P., additional, Dalley, Brian K., additional, Berchuck, Andrew, additional, Salas, Lucas A., additional, Peres, Lauren C., additional, Marks, Jeffrey R., additional, Schildkraut, Joellen M., additional, Greene, Casey S., additional, and Doherty, Jennifer A., additional

Published
2023
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42. Macrophages in SHH subgroup medulloblastoma display dynamic heterogeneity that varies with treatment modality

Author

Dang, Mai T., primary, Gonzalez, Michael V., additional, Gaonkar, Krutika S., additional, Rathi, Komal S., additional, Young, Patricia, additional, Arif, Sherjeel, additional, Zhai, Li, additional, Alam, Zahidul, additional, Devalaraja, Samir, additional, Jerrick To, Tsun Ki, additional, Folkert, Ian W., additional, Raman, Pichai, additional, Rokita, Jo Lynne, additional, Martinez, Daniel, additional, Taroni, Jaclyn N., additional, Shapiro, Joshua A., additional, Greene, Casey S., additional, Savonen, Candace, additional, Mafra, Fernanda, additional, Hakonarson, Hakon, additional, Curran, Tom, additional, and Haldar, Malay, additional

Published
2023
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47. Hetnet connectivity search provides rapid insights into how two biomedical entities are related

Author

Himmelstein, Daniel S., primary, Zietz, Michael, additional, Rubinetti, Vincent, additional, Kloster, Kyle, additional, Heil, Benjamin J., additional, Alquaddoomi, Faisal, additional, Hu, Dongbo, additional, Nicholson, David N., additional, Hao, Yun, additional, Sullivan, Blair D., additional, Nagle, Michael W., additional, and Greene, Casey S., additional

Published
2023
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