Search

Your search keyword '"Harris, Paul"' showing total 3,263 results
Start Over Author "Harris, Paul" Search Limiters Full Text

Refine your results

more »

more »

more »

more »

more »

more »
3,263 results on '"Harris, Paul"'

2. Identifying and Extracting Rare Disease Phenotypes with Large Language Models

Author

Shyr, Cathy, Hu, Yan, Harris, Paul A., and Xu, Hua

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text and time-consuming to extract manually. While natural language processing (NLP) models can perform named entity recognition (NER) to automate extraction, a major bottleneck is the development of a large, annotated corpus for model training. Recently, prompt learning emerged as an NLP paradigm that can lead to more generalizable results without any (zero-shot) or few labeled samples (few-shot). Despite growing interest in ChatGPT, a revolutionary large language model capable of following complex human prompts and generating high-quality responses, none have studied its NER performance for RDs in the zero- and few-shot settings. To this end, we engineered novel prompts aimed at extracting RD phenotypes and, to the best of our knowledge, are the first the establish a benchmark for evaluating ChatGPT's performance in these settings. We compared its performance to the traditional fine-tuning approach and conducted an in-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the zero- and few-shot settings, respectively). Despite this, ChatGPT achieved similar or higher accuracy for certain entities (i.e., rare diseases and signs) in the one-shot setting (F1 of 0.776 and 0.725). This suggests that with appropriate prompt engineering, ChatGPT has the potential to match or outperform fine-tuned language models for certain entity types with just one labeled sample. While the proliferation of large language models may provide opportunities for supporting RD diagnosis and treatment, researchers and clinicians should critically evaluate model outputs and be well-informed of their limitations.

Published
2023
Full Text
View/download PDF

3. A linearization for stable and fast geographically weighted Poisson regression

Author

Murakami, Daisuke, Tsutsumida, Narumasa, Yoshida, Takahiro, Nakaya, Tomoki, Lu, Binbin, and Harris, Paul

Subjects
Statistics - Methodology
Abstract

Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found for linear geographically weighted regression (GWR), where many extensions (e.g., multiscale GWR, scalable GWR) have been proposed. The weak development of GWPR can be attributed to the computational cost and identification problem in the underpinning Poisson regression model. This study proposes linearized GWPR (L-GWPR) by introducing a log-linear approximation into the GWPR model to overcome these bottlenecks. Because the L-GWPR model is identical to the Gaussian GWR model, it is free from the identification problem, easily implemented, computationally efficient, and offers similar potential for extension. Specifically, L-GWPR does not require a double-loop algorithm, which makes GWPR slow for large samples. Furthermore, we extended L-GWPR by introducing ridge regularization to enhance its stability (regularized L-GWPR). The results of the Monte Carlo experiments confirmed that regularized L-GWPR estimates local coefficients accurately and computationally efficiently. Finally, we compared GWPR and regularized L-GWPR through a crime analysis in Tokyo.

Published
2023

4. Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

Author

Diray-Arce, Joann, Fourati, Slim, Jayavelu, Naresh Doni, Patel, Ravi, Maguire, Cole, Chang, Ana C, Dandekar, Ravi, Qi, Jingjing, Lee, Brian H, van Zalm, Patrick, Schroeder, Andrew, Chen, Ernie, Konstorum, Anna, Brito, Anderson, Gygi, Jeremy P, Kho, Alvin, Chen, Jing, Pawar, Shrikant, Gonzalez-Reiche, Ana Silvia, Hoch, Annmarie, Milliren, Carly E, Overton, James A, Westendorf, Kerstin, Network, IMPACC, Abraham, James, Adkisson, Michael, Albert, Marisa, Torres, Luz Altamirano, Alvarenga, Bonny, Anderson, Matthew L, Anderson, Evan J, Arnett, Azlann, Asashima, Hiromitsu, Atkinson, Mark A, Baden, Lindsey R, Barton, Brenda, Beach, Katherine, Beagle, Elizabeth, Becker, Patrice M, Bell, Matthew R, Bernui, Mariana, Bime, Chris, Kumar, Arun Boddapati, Booth, Leland J, Borresen, Brittney, Brakenridge, Scott C, Bristow, Laurel, Bryant, Robert, Calfee, Carolyn S, Manuel, Juan Carreño, Carrillo, Sidney, Chak, Suzanna, Chang, Iris, Connors, Jennifer, Conway, Michelle, Corry, David B, Cowan, David, Croen, Brett, Dela Cruz, Charles S, Cusimano, Gina, Eaker, Lily, Edwards, Carolyn, Ehrlich, Lauren IR, Elashoff, David, Erickson, Heidi, Erle, David J, Farhadian, Shelli, Farrugia, Keith, Fatou, Benoit, Fernandes, Andrea, Fernandez-Sesma, Ana, Fragiadakis, Gabriela K, Furukawa, Sara, Geltman, Janelle N, Ghale, Rajani, Bermúdez, Maria González Carolina, Goonewardene, Michael I, Sanchez, Estella Guerrero, Guirgis, Faheem W, Hafler, David A, Hamilton, Sydney, Harris, Paul, Nemati, Arash Hayati, Hendrickson, Carolyn M, Agudelo, Nelson I Higuita, Hodder, Thomas, Holland, Steven M, Hough, Catherine L, Huerta, Christopher, Hurley, Kerin C, Hutton, Scott R, Iwasaki, Akiko, Jauregui, Alejandra, Jha, Meenakshi, Johnson, Brandi, Joyner, David, Kangelaris, Kirsten N, Kelly, Geoffrey, Khalil, Zain, and Khan, Zenab

Subjects
Biomedical and Clinical Sciences, Immunology, Prevention, Rare Diseases, Clinical Research, 2.1 Biological and endogenous factors, Detection, screening and diagnosis, Aetiology, 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being, Humans, COVID-19, SARS-CoV-2, Longitudinal Studies, Multiomics, Disease Progression, IMPACC Network, immunophenotyping, longitudinal modeling, multi-omics, systems immunology, Biomedical and clinical sciences
Abstract

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Published
2023

5. Index

Author

Harris, Paul

Published
1999

6. Notes

Author

Harris, Paul

Published
1999

21. Contents

Author

Harris, Paul

Published
1999

24. Title Page

Author

Harris, Paul

Published
1999

31. The Petriverse of Italo Calvino

Author

Harris, Paul

Subjects
geologic turn, Anthropocene, environmental humanities, Italo Calvino
Abstract

This essay considers Calvino’s Six Memos and other works in the context of the ‘geologic turn’ in contemporary environmental humanities. The introductory section shows how Calvino anticipated key notions now becoming prevalent in this work, including an ontology and ethics that respects “the unity of all things, animate or inanimate” (Memos). The next section, “Worlds Composed of Rocks,” rereads Calvino texts through a geologic lens—it treats “Lightness” through the lens of what Gaston Bachelard calls “the Medusa Complex,” examines geologic elements in the three retellings of the Orpheus myth in The Complete Cosmicomics, and briefly compares Calvino’s “The Stone Sky” to N.K. Jemisin’s recent geo-fiction The Stone Sky. The final section, Words Composed of Rocks, takes up Calvino’s semiotics as expressed in the Six Memos, Cosmicomics, and essays in Collection of Sand. Calvino’s interest in giving voice to natural materials is connected to Serpil Opperman’s notion of ‘Storied Matter,’ Roger Caillois’s ‘writing of stones,’ and Francis Ponge’s prose poems, among other sources. The essay features artworks and photos by the author, reflective of a contemporary merging of critical and creative engagements with literature in producing theoretical work in the geo-humanities.

Published
2023

34. The All of Us Research Program: Data quality, utility, and diversity

Author

Ramirez, Andrea H, Sulieman, Lina, Schlueter, David J, Halvorson, Alese, Qian, Jun, Ratsimbazafy, Francis, Loperena, Roxana, Mayo, Kelsey, Basford, Melissa, Deflaux, Nicole, Muthuraman, Karthik N, Natarajan, Karthik, Kho, Abel, Xu, Hua, Wilkins, Consuelo, Anton-Culver, Hoda, Boerwinkle, Eric, Cicek, Mine, Clark, Cheryl R, Cohn, Elizabeth, Ohno-Machado, Lucila, Schully, Sheri D, Ahmedani, Brian K, Argos, Maria, Cronin, Robert M, O’Donnell, Christopher, Fouad, Mona, Goldstein, David B, Greenland, Philip, Hebbring, Scott J, Karlson, Elizabeth W, Khatri, Parinda, Korf, Bruce, Smoller, Jordan W, Sodeke, Stephen, Wilbanks, John, Hentges, Justin, Mockrin, Stephen, Lunt, Christopher, Devaney, Stephanie A, Gebo, Kelly, Denny, Joshua C, Carroll, Robert J, Glazer, David, Harris, Paul A, Hripcsak, George, Philippakis, Anthony, Roden, Dan M, Program, the All of Us Research, Ahmedani, Brian, Johnson, Christine D Cole, Ahsan, Habib, Antoine-LaVigne, Donna, Singleton, Glendora, Topol, Eric, Baca-Motes, Katie, Steinhubl, Steven, Wade, James, Begale, Mark, Jain, Praduman, Sutherland, Scott, Lewis, Beth, Behringer, Melissa, Gharavi, Ali G, Bier, Louise, Brilliant, Murray H, Murali, Narayana, Hebbring, Scott Joseph, Farrar-Edwards, Dorothy, Burnside, Elizabeth, Drezner, Marc K, Taylor, Amy, Channamsetty, Veena, Montalvo, Wanda, Sharma, Yashoda, Chinea, Carmen, Jenks, Nancy, Thibodeau, Steve, Holmes, Beverly Wilson, Schlueter, Eric, Collier, Ever, Winkler, Joyce, Corcoran, John, D’Addezio, Nick, Daviglus, Martha, Winn, Robert, Roden, Dan, Denny, Joshua, Doheny, Kim, Nickerson, Debbie, Eichler, Evan, Jarvik, Gail, and Funk, Gretchen

Subjects
Networking and Information Technology R&D (NITRD), Clinical Research, Cardiovascular, Cancer, Generic health relevance, Good Health and Well Being, All of Us Research Program, cloud-based analytics, cohort study, electronic health records, precision medicine
Abstract

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Published
2022

35. gwverse: a template for a new generic Geographically Weighted Rpackage

Author

Comber, Alexis, Brunsdon, Chris, Callaghan, Martin, Harris, Paul, Lu, Binbin, and Malleson, Nick

Subjects
Statistics - Applications, Statistics - Computation, Statistics - Methodology
Abstract

GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework is increasingly used to accommodate different types of models and analyses reflecting a wider desire to explore spatial variation in model parameters or components. However the growth in the use of GWR and different GW models has only been partially supported by package development in both R and Python, the major coding environments for spatial analysis. The result is that refinements have been inconsistently included (if at all) within GWR and GW functions in any given package. This paper outlines the structure of a new `gwverse` package, that will over time replace `GWmodel`, that takes advantage of recent developments in the composition of complex, integrated packages. It conceptualises `gwverse` as having a modular structure, that separates core GW functionality and applications such as GWR. It adopts a function factory approach, in which bespoke functions are created and returned to the user based on user-defined parameters. The paper introduces two demonstrator modules that can be used to undertake GWR and identifies a number of key considerations and next steps.

Published
2021

36. Multi-parameter photon-by-photon hidden Markov modeling

Author

Harris, Paul David, Narducci, Alessandra, Gebhardt, Christian, Cordes, Thorben, Weiss, Shimon, and Lerner, Eitan

Subjects
Bioengineering, Fluorescence Resonance Energy Transfer, Molecular Conformation, Photons
Abstract

Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H2MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H2MM (mpH2MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH2MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations and their origin.

Published
2022

38. Multiscale Geographically Weighted Discriminant Analysis

Author

Comber, Alexis, Malleson, Nick, Nguyen Thi Thuy, Hang, Bui Quang, Thanh, Kieu, Minh, Huu Phe, Hoang, and Harris, Paul

Abstract

This paper describes the novel development and application of a multi-scale geographically weighted discriminant analysis (MSGWDA). This is applied to a case study of survey data of attitudes to a proposed motorbike / scooter ban in Han Noi, Vietnam. It uses discriminant analysis to examine attitudes to the ban in relation to travel purposes, distances, respondent age and so on. The main part of the paper focuses on describing the novel MSGWDA approach, and the results indicate the varying scales of relationship between the different input variables and the categorical responses variable. The paper also reflects on the pervasive logic of the approaches used to fit multiscale geographically weighted bandwidths (for example in regression). These have historically been based on the iterative back-fitting approaches used in GAMs, but risk missing potentially important variable interactions amongst un-evaluated bandwidths because of the sequence of their application. It is argued that although pragmatic in the 1990s, it may be possible to apply more deterministic approaches with increased memory and readily accessible computing power in order to better navigate such highly dimensional search spaces.

Published
2021

39. Stable geographically weighted Poisson regression for count data

Author

Murakami, Daisuke, Tsutsumida, Narumasa, Yoshida, Takahiro, Nakaya, Tomoki, Lu, Binbin, and Harris, Paul

Abstract

Geographically weighted Poisson regression (GWPR) is widely used for spatial regression analysis of count data. However, it tends to be unstable because of a fundamental drawback of Poisson regression. To overcome the drawback, we introduce a log-linear approximation to estimate GWPR without relying on Poisson regression framework. The proposed approach approximates GWPR using the basic GWR modeling with transformed explained variables. Monte Carlo experiments show that the proposed GWPR outperforms the conventional GWPR in terms of both estimation accuracy and computationally efficiency. Finally, the proposed GWPR is applied to an analysis of coronavirus disease 2019 (COVID-19).

Published
2021

42. POLICY MATTERS FOR BLACK YOUTH MENTAL HEALTH

Author

Mayes, Renae D., Ford, Donna Y., Mims, Lauren C., Harris, Paul C., Dhahan, Mandy K., and Mitchell-Dodge, Kate

Subjects
Mental health -- Laws, regulations and rules, Discrimination in education -- Laws, regulations and rules, Government regulation, Education, Ethnic, cultural, racial issues/studies
Abstract

In a recent op-ed, we described the present challenges Black youth are facing regarding mental health. Sadly, these statistics continue to be alarming as rates remain high in comparison to [...]

Published
2024

43. Naturalization of the microbiota developmental trajectory of Cesarean-born neonates after vaginal seeding

Author

Song, Se Jin, Wang, Jincheng, Martino, Cameron, Jiang, Lingjing, Thompson, Wesley K, Shenhav, Liat, McDonald, Daniel, Marotz, Clarisse, Harris, Paul R, Hernandez, Caroll D, Henderson, Nora, Ackley, Elizabeth, Nardella, Deanna, Gillihan, Charles, Montacuti, Valentina, Schweizer, William, Jay, Melanie, Combellick, Joan, Sun, Haipeng, Garcia-Mantrana, Izaskun, Gil Raga, Fernando, Collado, Maria Carmen, Rivera-Viñas, Juana I, Campos-Rivera, Maribel, Ruiz-Calderon, Jean F, Knight, Rob, and Dominguez-Bello, Maria Gloria

Subjects
Infant Mortality, Genetics, Pediatric, Human Genome, Perinatal Period - Conditions Originating in Perinatal Period, Clinical Research, Reproductive health and childbirth, Good Health and Well Being, Cesarean Section, Citizenship, Female, Humans, Infant, Infant, Newborn, Longitudinal Studies, Microbiota, Pregnancy, RNA, Ribosomal, 16S, Translation to humans
Abstract

BackgroundEarly microbiota perturbations are associated with disorders that involve immunological underpinnings. Cesarean section (CS)-born babies show altered microbiota development in relation to babies born vaginally. Here we present the first statistically powered longitudinal study to determine the effect of restoring exposure to maternal vaginal fluids after CS birth.MethodsUsing 16S rRNA gene sequencing, we followed the microbial trajectories of multiple body sites in 177 babies over the first year of life; 98 were born vaginally, and 79 were born by CS, of whom 30 were swabbed with a maternal vaginal gauze right after birth.FindingsCompositional tensor factorization analysis confirmed that microbiota trajectories of exposed CS-born babies aligned more closely with that of vaginally born babies. Interestingly, the majority of amplicon sequence variants from maternal vaginal microbiomes on the day of birth were shared with other maternal sites, in contrast to non-pregnant women from the Human Microbiome Project (HMP) study.ConclusionsThe results of this observational study prompt urgent randomized clinical trials to test whether microbial restoration reduces the increased disease risk associated with CS birth and the underlying mechanisms. It also provides evidence of the pluripotential nature of maternal vaginal fluids to provide pioneer bacterial colonizers for the newborn body sites. This is the first study showing long-term naturalization of the microbiota of CS-born infants by restoring microbial exposure at birth.FundingC&D, Emch Fund, CIFAR, Chilean CONICYT and SOCHIPE, Norwegian Institute of Public Health, Emerald Foundation, NIH, National Institute of Justice, Janssen.

Published
2021

44. The GWR route map: a guide to the informed application of Geographically Weighted Regression

Author

Comber, Alexis, Brunsdon, Chris, Charlton, Martin, Dong, Guanpeng, Harris, Rich, Lu, Binbin, Lü, Yihe, Murakami, Daisuke, Nakaya, Tomoki, Wang, Yunqiang, and Harris, Paul

Subjects
Statistics - Methodology, 91B72 (Primary), 62M30 (Secondary), G.3
Abstract

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a global one. Standard GWR assumes that the relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to inform the choice of whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises primary steps: a basic linear regression, a MS-GWR, and investigations of the results of these. The paper provides guidance for deciding whether to use a GWR approach, and if so for determining the appropriate GWR variant. It describes the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided, and further considerations are described in an extensive Appendix., Comment: 34 pages, 8 figures

Published
2020

46. Poly-γ-glutamylation of biomolecules

Author

Bashiri, Ghader, Bulloch, Esther M. M., Bramley, William R., Davidson, Madison, Stuteley, Stephanie M., Young, Paul G., Harris, Paul W. R., Naqvi, Muhammad S. H., Middleditch, Martin J., Schmitz, Michael, Chang, Wei-Chen, Baker, Edward N., and Squire, Christopher J.

Published
2024
Full Text
View/download PDF

49. The All of Us Research Program: Data quality, utility, and diversity

Author

Ahmedani, Brian, Cole Johnson, Christine D., Ahsan, Habib, Antoine-LaVigne, Donna, Singleton, Glendora, Anton-Culver, Hoda, Topol, Eric, Baca-Motes, Katie, Steinhubl, Steven, Wade, James, Begale, Mark, Jain, Praduman, Sutherland, Scott, Lewis, Beth, Korf, Bruce, Behringer, Melissa, Gharavi, Ali G., Goldstein, David B., Hripcsak, George, Bier, Louise, Boerwinkle, Eric, Brilliant, Murray H., Murali, Narayana, Hebbring, Scott Joseph, Farrar-Edwards, Dorothy, Burnside, Elizabeth, Drezner, Marc K., Taylor, Amy, Channamsetty, Veena, Montalvo, Wanda, Sharma, Yashoda, Chinea, Carmen, Jenks, Nancy, Cicek, Mine, Thibodeau, Steve, Holmes, Beverly Wilson, Schlueter, Eric, Collier, Ever, Winkler, Joyce, Corcoran, John, D’Addezio, Nick, Daviglus, Martha, Winn, Robert, Wilkins, Consuelo, Roden, Dan, Denny, Joshua, Doheny, Kim, Nickerson, Debbie, Eichler, Evan, Jarvik, Gail, Funk, Gretchen, Philippakis, Anthony, Rehm, Heidi, Lennon, Niall, Kathiresan, Sekar, Gabriel, Stacey, Gibbs, Richard, Gil Rico, Edgar M., Glazer, David, Grand, Joannie, Greenland, Philip, Harris, Paul, Shenkman, Elizabeth, Hogan, William R., Igho-Pemu, Priscilla, Pollan, Cliff, Jorge, Milena, Okun, Sally, Karlson, Elizabeth W., Smoller, Jordan, Murphy, Shawn N., Ross, Margaret Elizabeth, Kaushal, Rainu, Winford, Eboni, Wallace, Febe, Khatri, Parinda, Kheterpal, Vik, Ojo, Akinlolu, Moreno, Francisco A., Kron, Irving, Peterson, Rachele, Menon, Usha, Lattimore, Patricia Watkins, Leviner, Noga, Obedin-Maliver, Juno, Lunn, Mitchell, Malik-Gagnon, Lynda, Mangravite, Lara, Marallo, Adria, Marroquin, Oscar, Visweswaran, Shyam, Reis, Steven, Marshall, Gailen, Jr., McGovern, Patrick, Mignucci, Deb, Moore, John, Munoz, Fatima, Talavera, Gregory, O'Connor, George T., O'Donnell, Christopher, Ohno-Machado, Lucila, Orr, Greg, Randal, Fornessa, Theodorou, Andreas A., Reiman, Eric, Roxas-Murray, Mercedita, Stark, Louisa, Tepp, Ronnie, Zhou, Alicia, Topper, Scott, Trousdale, Rhonda, Tsao, Phil, Weidman, Lisa, Weiss, Scott T., Wellis, David, Whittle, Jeffrey, Wilson, Amanda, Zuchner, Stephan, Zwick, Michael E., Ramirez, Andrea H., Sulieman, Lina, Schlueter, David J., Halvorson, Alese, Qian, Jun, Ratsimbazafy, Francis, Loperena, Roxana, Mayo, Kelsey, Basford, Melissa, Deflaux, Nicole, Muthuraman, Karthik N., Natarajan, Karthik, Kho, Abel, Xu, Hua, Clark, Cheryl R., Cohn, Elizabeth, Schully, Sheri D., Ahmedani, Brian K., Argos, Maria, Cronin, Robert M., O’Donnell, Christopher, Fouad, Mona, Hebbring, Scott J., Smoller, Jordan W., Sodeke, Stephen, Wilbanks, John, Hentges, Justin, Mockrin, Stephen, Lunt, Christopher, Devaney, Stephanie A., Gebo, Kelly, Denny, Joshua C., Carroll, Robert J., Harris, Paul A., and Roden, Dan M.

Published
2022
Full Text
View/download PDF

50. Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

Author

Abraham, James, Adkisson, Michael, Albert, Marisa, Altamirano, Luz Torres, Alvarenga, Bonny, Anderson, Matthew L., Anderson, Evan J., Arnett, Azlann, Asashima, Hiromitsu, Atkinson, Mark A., Baden, Lindsey R., Barton, Brenda, Beach, Katherine, Beagle, Elizabeth, Becker, Patrice M., Bell, Matthew R., Bernui, Mariana, Bime, Christian, Boddapati, Arun Kumar, Booth, J. Leland, Borresen, Brittney, Brakenridge, Scott C., Bristow, Laurel, Bryant, Robert, Calfee, Carolyn S., Carreño, Juan Manuel, Carrillo, Sidney, Chak, Suzanna, Chang, Iris, Connors, Jennifer, Conway, Michelle, Corry, David B., Cowan, David, Croen, Brett, Dela Cruz, Charles S., Cusimano, Gina, Eaker, Lily, Edwards, Carolyn, Ehrlich, Lauren I.R., Elashoff, David, Erickson, Heidi, Erle, David J., Farhadian, Shelli, Farrugia, Keith, Fatou, Benoit, Fernandes, Andrea, Fernandez-Sesma, Ana, Fragiadakis, Gabriela K., Furukawa, Sara, Geltman, Janelle N., Ghale, Rajani, Bermúdez González, Maria Carolina, Goonewardene, I. Michael, Guerrero, Estella Sanchez, Guirgis, Faheem W., Hafler, David A., Hamilton, Sydney, Harris, Paul, Hayati, Arash Nemati, Hendrickson, Carolyn M., Agudelo Higuita, Nelson I., Hodder, Thomas, Holland, Steven M., Hough, Catherine L., Huerta, Christopher, Hurley, Kerin C., Hutton, Scott R., Iwasaki, Akiko, Jauregui, Alejandra, Jha, Meenakshi, Johnson, Brandi, Joyner, David, Kangelaris, Kirsten N., Kelly, Geoffrey, Khalil, Zain, Khan, Zenab, Kheradmand, Farrah, Kim, James N., Kimura, Hiroki, Ko, Albert I., Kohr, Bernard, Kraft, Monica, Krummel, Matthew, Kutzler, Michele A., Lasky-Su, Jessica, Lee, Serena, Lee, Deanna, Leipold, Michael, Lentucci, Claudia, Leroux, Carolyn, Lin, Edward, Liu, Shanshan, Love, Christina, Lu, Zhengchun, Maliskova, Lenka, Manning, Brittany Roth, Manohar, Monali, Martens, Mark, McComsey, Grace A., McEnaney, Kerry, McLin, Renee, Melamed, Esther, Melnyk, Nataliya, Mendez, Kevin, Messer, William B., Metcalf, Jordan P., Michelotti, Gregory, Mick, Eran, Mohanty, Subhasis, Mosier, Jarrod, Mulder, Lubbertus C.F., Murphy, Maimouna, Nadeau, Kari R.C., Nelson, Ebony, Nelson, Allison, Nguyen, Viet, Oberhaus, Jordan, Panganiban, Bernadine, Pellegrini, Kathryn L., Pickering, Harry C., Powell, Debra L., Presnell, Scott, Pulendran, Bali, Rahman, Adeeb H., Rashid, Ahmad Sadeed, Raskin, Ariel, Reed, Elaine F., Ribeiro, Susan Pereira, Rivera, Adreanne M., Rogers, Jacob E., Rogers, Angela, Rogowski, Brandon, Rooks, Rebecca, Rosenberg-Hasson, Yael, Rothman, Jessica, Rousseau, Justin F., Salehi-Rad, Ramin, Saluvan, Mehmet, Samaha, Hady, Schaenman, Joanna, Schunk, Ron, Semenza, Nicholas C., Sen, Subha, Sevransky, Jonathan, Seyfert-Margolis, Vicki, Shaheen, Tanzia, Shaw, Albert C., Sieg, Scott, Siegel, Sarah A.R., Sigal, Natalia, Siles, Nadia, Simmons, Brent, Simon, Viviana, Singh, Gagandeep, Sinko, Lauren, Smith, Cecilia M., Smolen, Kinga K., Song, Li-Zhen, Srivastava, Komal, Sullivan, Peter, Syphurs, Caitlin, Tcheou, Johnstone, Tegos, George P., Tharp, Greg K., Tong, Alexandra, Tsitsiklis, Alexandra, Ungaro, Ricardo F., Vaysman, Tatyana, Viode, Arthur, Vita, Randi, Wang, Xiaomei, Ward, Alyssa, Ward, Dawn C., Willmore, Andrew, Woloszczuk, Kyra, Wong, Kari, Woodruff, Prescott G., Xu, Leqi, van Haren, Simon, van de Guchte, Adriana, Zhao, Yujiao, Diray-Arce, Joann, Fourati, Slim, Doni Jayavelu, Naresh, Patel, Ravi, Maguire, Cole, Chang, Ana C., Dandekar, Ravi, Qi, Jingjing, Lee, Brian H., van Zalm, Patrick, Schroeder, Andrew, Chen, Ernie, Konstorum, Anna, Brito, Anderson, Gygi, Jeremy P., Kho, Alvin, Chen, Jing, Pawar, Shrikant, Gonzalez-Reiche, Ana Silvia, Hoch, Annmarie, Milliren, Carly E., Overton, James A., Westendorf, Kerstin, Cairns, Charles B., Rouphael, Nadine, Bosinger, Steven E., Kim-Schulze, Seunghee, Krammer, Florian, Rosen, Lindsey, Grubaugh, Nathan D., van Bakel, Harm, Wilson, Michael, Rajan, Jayant, Steen, Hanno, Eckalbar, Walter, Cotsapas, Chris, Langelier, Charles R., Levy, Ofer, Altman, Matthew C., Maecker, Holden, Montgomery, Ruth R., Haddad, Elias K., Sekaly, Rafick P., Esserman, Denise, Ozonoff, Al, Augustine, Alison D., Guan, Leying, Peters, Bjoern, and Kleinstein, Steven H.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results