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32 results on '"Zhu, Jeffrey"'

1. Detecting Political Biases of Named Entities and Hashtags on Twitter

Author

Xiao, Zhiping, Zhu, Jeffrey, Wang, Yining, Zhou, Pei, Lam, Wen Hong, Porter, Mason A., and Sun, Yizhou

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Physics - Physics and Society, 68T09 (Primary) 68T07 (Secondary)
Abstract

Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a corpus of text, one can attempt to describe and discern the polarity of that text. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term "pro-choice" are likely to be liberal, whereas people who use the term "pro-life" are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding's polarity dimension and its semantic dimensions. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well classification tasks. We examine a variety of applications and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the it to real-world scenarios., Comment: Submitted to EPJ -- Data Science, under review

Published
2022

2. Detecting political biases of named entities and hashtags on Twitter

Author

Xiao, Zhiping, Zhu, Jeffrey, Wang, Yining, Zhou, Pei, Lam, Wen Hong, Porter, Mason A, and Sun, Yizhou

Subjects
Information and Computing Sciences, Language, Communication and Culture, Political Science, Human Society, Political-polarity detection, Word embeddings, Multi-task learning, Adversarial training, Data sets
Abstract

Abstract: Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.

Published
2023

4. TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data

Author

Piriyakulkij, Wasu, Menghini, Cristina, Briden, Ross, Nayak, Nihal V., Zhu, Jeffrey, Raisi, Elaheh, and Bach, Stephen H.

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

Machine learning practitioners often have access to a spectrum of data: labeled data for the target task (which is often limited), unlabeled data, and auxiliary data, the many available labeled datasets for other tasks. We describe TAGLETS, a system built to study techniques for automatically exploiting all three types of data and creating high-quality, servable classifiers. The key components of TAGLETS are: (1) auxiliary data organized according to a knowledge graph, (2) modules encapsulating different methods for exploiting auxiliary and unlabeled data, and (3) a distillation stage in which the ensembled modules are combined into a servable model. We compare TAGLETS with state-of-the-art transfer learning and semi-supervised learning methods on four image classification tasks. Our study covers a range of settings, varying the amount of labeled data and the semantic relatedness of the auxiliary data to the target task. We find that the intelligent incorporation of auxiliary and unlabeled data into multiple learning techniques enables TAGLETS to match-and most often significantly surpass-these alternatives. TAGLETS is available as an open-source system at github.com/BatsResearch/taglets., Comment: Paper published at MLSys 2022. It passed the artifact evaluation earning two ACM badges: (1) Artifacts Evaluated Functional v1.1 and (2) Artifacts Available v1.1

Published
2021

6. Prospective Cohort Study to Compare Long-Term Lung Cancer-Specific and All-Cause Survival of Clinical Early Stage (T1a–b; ≤20 mm) NSCLC Treated by Stereotactic Body Radiation Therapy and Surgery

Author

Flores, Raja, Kaufman, Andrew, Lee, Dong-Seok, Nicastri, Daniel, Wolf, Andrea, Song, Kimberly, Rosenzweig, Kenneth, Gomez, Jorge, Samstein, Robert, Dutta, Pinaki, Beasley, Mary Beth, Zakowski, Maureen, Chung, Michael, Yankelevitz, David F., Henschke, Claudia I., Taioli, Emanuela, Schwartz, Rebecca, Chan, Huiwen, Zhu, Jeffrey, Kantor, Sydney, Woode, Sydney, Hakami, Ardeshir, Buyuk, Arzu, Friedman, Adie, Dreifuss, Ronald, Verzosa, Stacey, Yakubox, Mariya, Aloferdova, Karina, Stacey, Patricia, De Nobrega, Simone, Lentini, Lauren, Pass, Harvey, Cooper, Benjamin, Moreirea, Andre, Sorensen, Audrey, Kohman, Leslie, Dunton, Robert, Wallen, Jason, Curtiss, Christopher, Scalzetti, Ernest, Ellinwood, Linda, Tannous, Henry, Connery, Cliff P., Torres, Emilo, Cruzer, Dan, Gendron, Bruce, Alyea, Sonya, Krumholtz, Pramila, Watkins, Ammara, Servais, Elliot, Stock, Cameron, McKee, Andrea, Lopez, Edilin, Hsu, Howard, Hunter, Kaudia, Lemons, Jeffrey, Nixon, Asa, Osa, Etin-Osa, Lee, Paul, Hyman, Kevin, Jurado, Julisa, Zeltman, David, Glassman, Lawrence, Sharma, Rajiv, Singh, Vijay, Milhelis, Efstathia, Karan, Nandanee, Rzyman, Witold, Dziedzic, Robert, Henschke, Claudia, Yankelevitz, David, Jirapatnakul, Artit, Yip, Rowena, Zulueta, Javier J., Reeves, A.P., Altorki, Nasser K., Smith, James P., Libby, Daniel M., Pasmantier, Mark, Markowitz, Steven, Miller, Albert, Deval, Jose Cervera, Shaham, Dorith, Seijo, Luis, Bastarika, Gorka, Montuenga, Luis M., Spring, Silver, Aylesworth, Cheryl, Klingler, Karl, Schöb, Othmar, Andaz, Shahriyour, Straznicka, Michaela, Chin, Cynthia, Weiser, Todd, Sone, Shusuke, Hanaoka, Takaomi, Roberts, Heidi, Patsios, Demetris, Scopetuolo, M., Brown, Andrew, Wu, Ning, Cole, Enser, Yeh, Diana, Luedke, Dan, Liu, Xueguo, Herzog, Gary, Aye, Ralph, Rifkin, Matthew, Veronesi, Giula, Infante, Maurizio, Vafai, Davood, Kopel, Samuel, Taylor, Jana, Thurer, Richard, Villamizar, Nestor, Austin, John H.M., Pearson, Gregory D.N., Klippenstein, Donald, Litwin, Alan, Loud, Peter A., Kohman, Leslie J., Scalzetti, Ernest M., Khan, Arfa, Shah, Rakesh, Mayfield, William, Frumiento, Carmine, Smith, Michael V., Thorsen, M. Kristin, Hansen, Richard, Naidich, David, McGuinness, Georgeann, Widmann, Mark, Korst, Robert, Lowry, Joseph, Salvatore, Mary, Walsh, James, Bertsch, David, Scheinberg, Paul, Sheppard, Barry, Cecchi, Gary, Ginsberg, Michelle S., Welch, Laura, Raz, Dan J., Rotter, Arnold, Connery, Cliff, Matalon, Terence A.S., Cheung, Edson H., Glassberg, Robert, Olsen, David, Mullen, David, Odzer, Shari-Lynn, Wiernik, Peter H., Ray, Daniel, DeCunzo, Louis, Endress, Carmen, Vacca, Anthony, Kondapaneni, M., Lim, Melissa, Kalafer, Michael, Green, Jeremy, Yoder, Mark, Shah, Palmi, Camacho, Elmer, O’Brien, James, Willey, James C., Gordon, David, Kocha, Albert, Sun, Qi, Li, Pengfei, and Flores, Raja M.

Published
2024
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8. New or enlarging hiatal hernias after thoracic surgery for early lung cancer

Author

Flores, Raja, Kaufman, Andrew, Lee, Dong-Seok, Nicastri, Daniel, Wolf, Andrea, Song, Kimberly, Rosenzweig, Kenneth, Gomez, Jorge, Beasley, Mary Beth, Zakowski, Maureen, Chung, Michael, Yankelevitz, David F., Henschke, Claudia I., Taioli, Emanuela, Schwartz, Rebecca, Chan, Huiwen, Zhu, Jeffrey, Kantor, Sydney, Adler, Shana, Raad, Wissam, Buyuk, Zrzu, Friedman, Adie, Dreifuss, Ronald, Verzosa, Stacey, Yakubox, Mariya, Aloferdova, Karina, Stacey, Patricia, De Nobrega, Simone, Hakami, Ardeshir, Pass, Harvey, Crawford, Berne, Donnington, Jessica, Cooper, Benjamin, Moreirea, Andre, Sorensen, Audrey, Kohman, Leslie, Dunton, Robert, Wallen, Jason, Curtiss, Christopher, Scalzetti, Ernest, Ellinwood, Linda, Connery, Clifford P., Torres, Emilo, Cruzer, Dan, Gendron, Bruce, Alyea, Sonya, Lackaye, Daniel, Studer, Lauren, Henschke, Claudia, Yankelevitz, David, Becker, Betsy, Jirapatnakul, Artit, Yip, Rowena, You, Nan, Libby, Daniel M., Smith, James P., Pasmantier, Mark, Reeves, A.P., Markowitz, Steven, Miller, Albert, Deval, Jose Cervera, Roberts, Heidi, Patsios, Demetris, Sone, Shusuke, Hanaoka, Takaomi, Zulueta, Javier, de-Torres, Juan P., Lozano, Maria D., Aye, Ralph, Manning, Kristin, Care, Christiana, Bauer, Thomas, Canitano, Stefano, Giunta, Salvatore, Cole, Enser, Klingler, Karl, Austin, John H.M., Pearson, Gregory D.N., Shaham, Dorith, Aylesworth, Cheryl, Meyers, Patrick, Andaz, Shahriyour, Vafai, Davood, Naidich, David, McGuinness, Georgeann, Sheppard, Barry, Rifkin, Matthew, Thorsen, M. Kristin, Hansen, Richard, Kopel, Samuel, Mayfield, William, Luedke, Dan, Klippenstein, Donald, Litwin, Alan, Loud, Peter A., Kohman, Leslie J., Scalzetti, Ernest M., Thurer, Richard, Villamizar, Nestor, Khan, Arfa, Shah, Rakesh, Liu, Xueguo, Herzog, Gary, Yeh, Diana, Wu, Ning, Lowry, Joseph, Salvatore, Mary, Frumiento, Carmine, Mendelson, David S., Smith, Michael V., Korst, Robert, Taylor, Jana, Ginsberg, Michelle S., Straznicka, Michaela, Widmann, Mark, Cecchi, Gary, Matalon, Terence A.S., Scheinberg, Paul, Odzer, Shari-Lynn, Olsen, David, Grannis, Fred, Rotter, Arnold, Ray, Daniel, Mullen, David, Wiernik, Peter H., Cheung, Edson H., Lim, Melissa, DeCunzo, Louis, Glassberg, Robert, Endress, Carmen, Yoder, Mark, Shah, Palmi, Welch, Laura, Kalafer, Michael, Green, Jeremy, Walsh, James, Bertsch, David, Camacho, Elmer, Chin, Cynthia, O'Brien, James, Willey, James C., Song, Kimberly J., Cai, Qiang, Zhu, Yeqing, Singh, Ayushi, and Lewis, Erik E.

Published
2022
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9. Genome-wide screen identifies a novel prognostic signature for breast cancer survival

Author

Mao, Xuan Y, Lee, Matthew J, Zhu, Jeffrey, Zhu, Carissa, Law, Sindy M, and Snijders, Antoine M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Human Genome, Cancer, Genetics, Breast Cancer, Biotechnology, Detection, screening and diagnosis, 2.1 Biological and endogenous factors, Aetiology, 4.1 Discovery and preclinical testing of markers and technologies, Biomarkers, Tumor, Breast Neoplasms, Carcinoma, Ductal, Breast, Disease-Free Survival, Female, Gene Expression Profiling, Gene Regulatory Networks, Genome-Wide Association Study, Humans, Kaplan-Meier Estimate, Prognosis, Transcriptome, breast cancer, prognostic score, relapse-free survival, gene biomarkers, Oncology and carcinogenesis
Abstract

Large genomic datasets in combination with clinical data can be used as an unbiased tool to identify genes important in patient survival and discover potential therapeutic targets. We used a genome-wide screen to identify 587 genes significantly and robustly deregulated across four independent breast cancer (BC) datasets compared to normal breast tissue. Gene expression of 381 genes was significantly associated with relapse-free survival (RFS) in BC patients. We used a gene co-expression network approach to visualize the genetic architecture in normal breast and BCs. In normal breast tissue, co-expression cliques were identified enriched for cell cycle, gene transcription, cell adhesion, cytoskeletal organization and metabolism. In contrast, in BC, only two major co-expression cliques were identified enriched for cell cycle-related processes or blood vessel development, cell adhesion and mammary gland development processes. Interestingly, gene expression levels of 7 genes were found to be negatively correlated with many cell cycle related genes, highlighting these genes as potential tumor suppressors and novel therapeutic targets. A forward-conditional Cox regression analysis was used to identify a 12-gene signature associated with RFS. A prognostic scoring system was created based on the 12-gene signature. This scoring system robustly predicted BC patient RFS in 60 sampling test sets and was further validated in TCGA and METABRIC BC data. Our integrated study identified a 12-gene prognostic signature that could guide adjuvant therapy for BC patients and includes novel potential molecular targets for therapy.

Published
2017

10. Towards a neutrally stable compressible metamaterial

Author

Zhu, Jeffrey (author) and Zhu, Jeffrey (author)

Abstract

Neutrally stable metamaterials can maintain differ- ent shapes without any energy input, making it a key innovation in the quest for more energy-efficient technologies. Despite this intriguing property, the research in this area is scarce. This study proposes a method for achieving neutral stability in metama- terials. This method is validated with a novel unit cell design that utilizing two identical beam elements that are mirrored. Each element displays a constant force characteristic. By pre- tensioning these elements, we align their constant force regions, thereby inducing a state of neutral stability. Through finite element method (FEM) simulations and geometrical optimisation, the beam of this design is optimised to achieve the optimal constant force response. A prototype is made and a test setup is constructed to validate the accuracy of the simulations and the feasibility of the method for achieving neutral stability. Results indicate that while perfect neutral stability was not fully achieved, this method can be applied on other constant force mechanisms to create neutrally stable metamaterials., Mechanical Engineering | Mechatronic System Design (MSD)

Published
2024

11. Prospective Cohort Study to Compare Long-Term Lung Cancer-Specific and All-Cause Survival of Clinical Early Stage (T1a–b; ≤20 mm) NSCLC Treated by Stereotactic Body Radiation Therapy and Surgery

Author

Henschke, Claudia I., Yip, Rowena, Sun, Qi, Li, Pengfei, Kaufman, Andrew, Samstein, Robert, Connery, Cliff, Kohman, Leslie, Lee, Paul, Tannous, Henry, Yankelevitz, David F., Taioli, Emanuela, Rosenzweig, Kenneth, Flores, Raja M., Flores, Raja, Kaufman, Andrew, Lee, Dong-Seok, Nicastri, Daniel, Wolf, Andrea, Song, Kimberly, Rosenzweig, Kenneth, Gomez, Jorge, Samstein, Robert, Dutta, Pinaki, Beasley, Mary Beth, Zakowski, Maureen, Chung, Michael, Yankelevitz, David F., Henschke, Claudia I., Taioli, Emanuela, Schwartz, Rebecca, Chan, Huiwen, Zhu, Jeffrey, Kantor, Sydney, Woode, Sydney, Nicastri, Daniel, Hakami, Ardeshir, Buyuk, Arzu, Friedman, Adie, Dreifuss, Ronald, Verzosa, Stacey, Yakubox, Mariya, Aloferdova, Karina, Stacey, Patricia, De Nobrega, Simone, Zhu, Jeffrey, Kantor, Sydney, Woode, Sydney, Hakami, Ardeshir, Zhu, Jeffrey, Kantor, Sydney, Woode, Sydney, Lentini, Lauren, Pass, Harvey, Cooper, Benjamin, Moreirea, Andre, Sorensen, Audrey, Kohman, Leslie, Dunton, Robert, Wallen, Jason, Curtiss, Christopher, Scalzetti, Ernest, Ellinwood, Linda, Tannous, Henry, Connery, Cliff P., Torres, Emilo, Cruzer, Dan, Gendron, Bruce, Alyea, Sonya, Krumholtz, Pramila, Watkins, Ammara, Servais, Elliot, Stock, Cameron, McKee, Andrea, Lopez, Edilin, Hsu, Howard, Hunter, Kaudia, Lemons, Jeffrey, Nixon, Asa, Osa, Etin-Osa, Lee, Paul, Hyman, Kevin, Jurado, Julisa, Zeltman, David, Glassman, Lawrence, Sharma, Rajiv, Singh, Vijay, Milhelis, Efstathia, Karan, Nandanee, Rzyman, Witold, Dziedzic, Robert, Flores, Raja, Henschke, Claudia, Taioli, Emanuela, Yankelevitz, David, Schwartz, Rebecca, Jirapatnakul, Artit, Yip, Rowena, Chan, Huiwen, Henschke, Claudia I., Yankelevitz, David F., Yip, Rowena, Jirapatnakul, Artit, Flores, Raja, Kaufman, Andrew, Wolf, Andrea, Nicastri, Daniel, Zulueta, Javier J., Taioli, Emanuela, Reeves, A.P., Altorki, Nasser K., Smith, James P., Libby, Daniel M., Pasmantier, Mark, Markowitz, Steven, Miller, Albert, Deval, Jose Cervera, Shaham, Dorith, Seijo, Luis, Bastarika, Gorka, Montuenga, Luis M., Spring, Silver, Aylesworth, Cheryl, Klingler, Karl, Schöb, Othmar, Andaz, Shahriyour, Straznicka, Michaela, Chin, Cynthia, Weiser, Todd, Sone, Shusuke, Hanaoka, Takaomi, Roberts, Heidi, Patsios, Demetris, Scopetuolo, M., Brown, Andrew, Wu, Ning, Cole, Enser, Yeh, Diana, Luedke, Dan, Liu, Xueguo, Herzog, Gary, Aye, Ralph, Rifkin, Matthew, Veronesi, Giula, Infante, Maurizio, Vafai, Davood, Kopel, Samuel, Taylor, Jana, Thurer, Richard, Villamizar, Nestor, Austin, John H.M., Pearson, Gregory D.N., Klippenstein, Donald, Litwin, Alan, Loud, Peter A., Kohman, Leslie J., Scalzetti, Ernest M., Khan, Arfa, Shah, Rakesh, Mayfield, William, Frumiento, Carmine, Smith, Michael V., Thorsen, M. Kristin, Hansen, Richard, Naidich, David, McGuinness, Georgeann, Widmann, Mark, Korst, Robert, Lowry, Joseph, Salvatore, Mary, Walsh, James, Bertsch, David, Scheinberg, Paul, Sheppard, Barry, Cecchi, Gary, Ginsberg, Michelle S., Welch, Laura, Raz, Dan J., Rotter, Arnold, Connery, Cliff, Matalon, Terence A.S., Cheung, Edson H., Glassberg, Robert, Olsen, David, Mullen, David, Odzer, Shari-Lynn, Wiernik, Peter H., Ray, Daniel, DeCunzo, Louis, Pass, Harvey, Endress, Carmen, Vacca, Anthony, Kondapaneni, M., Lim, Melissa, Kalafer, Michael, Green, Jeremy, Yoder, Mark, Shah, Palmi, Camacho, Elmer, O’Brien, James, Willey, James C., Gordon, David, and Kocha, Albert

Abstract

We aimed to compare outcomes of patients with first primary clinical T1a-bN0M0 NSCLC treated with surgery or stereotactic body radiation therapy (SBRT).

Published
2024
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12. Prospective Cohort Study to Compare Long-Term Lung Cancer-Specific and All-Cause Survival of Clinical Early Stage (T1a–b; ≤20 mm) NSCLC Treated by Stereotactic Body Radiation Therapy and Surgery

Author

Henschke, Claudia I., primary, Yip, Rowena, additional, Sun, Qi, additional, Li, Pengfei, additional, Kaufman, Andrew, additional, Samstein, Robert, additional, Connery, Cliff, additional, Kohman, Leslie, additional, Lee, Paul, additional, Tannous, Henry, additional, Yankelevitz, David F., additional, Taioli, Emanuela, additional, Rosenzweig, Kenneth, additional, Flores, Raja M., additional, Flores, Raja, additional, Lee, Dong-Seok, additional, Nicastri, Daniel, additional, Wolf, Andrea, additional, Song, Kimberly, additional, Gomez, Jorge, additional, Dutta, Pinaki, additional, Beasley, Mary Beth, additional, Zakowski, Maureen, additional, Chung, Michael, additional, Henschke, Claudia I., additional, Schwartz, Rebecca, additional, Chan, Huiwen, additional, Zhu, Jeffrey, additional, Kantor, Sydney, additional, Woode, Sydney, additional, Hakami, Ardeshir, additional, Buyuk, Arzu, additional, Friedman, Adie, additional, Dreifuss, Ronald, additional, Verzosa, Stacey, additional, Yakubox, Mariya, additional, Aloferdova, Karina, additional, Stacey, Patricia, additional, De Nobrega, Simone, additional, Lentini, Lauren, additional, Pass, Harvey, additional, Cooper, Benjamin, additional, Moreirea, Andre, additional, Sorensen, Audrey, additional, Dunton, Robert, additional, Wallen, Jason, additional, Curtiss, Christopher, additional, Scalzetti, Ernest, additional, Ellinwood, Linda, additional, Connery, Cliff P., additional, Torres, Emilo, additional, Cruzer, Dan, additional, Gendron, Bruce, additional, Alyea, Sonya, additional, Krumholtz, Pramila, additional, Watkins, Ammara, additional, Servais, Elliot, additional, Stock, Cameron, additional, McKee, Andrea, additional, Lopez, Edilin, additional, Hsu, Howard, additional, Hunter, Kaudia, additional, Lemons, Jeffrey, additional, Nixon, Asa, additional, Osa, Etin-Osa, additional, Hyman, Kevin, additional, Jurado, Julisa, additional, Zeltman, David, additional, Glassman, Lawrence, additional, Sharma, Rajiv, additional, Singh, Vijay, additional, Milhelis, Efstathia, additional, Karan, Nandanee, additional, Rzyman, Witold, additional, Dziedzic, Robert, additional, Henschke, Claudia, additional, Yankelevitz, David, additional, Jirapatnakul, Artit, additional, Zulueta, Javier J., additional, Reeves, A.P., additional, Altorki, Nasser K., additional, Smith, James P., additional, Libby, Daniel M., additional, Pasmantier, Mark, additional, Markowitz, Steven, additional, Miller, Albert, additional, Deval, Jose Cervera, additional, Shaham, Dorith, additional, Seijo, Luis, additional, Bastarika, Gorka, additional, Montuenga, Luis M., additional, Spring, Silver, additional, Aylesworth, Cheryl, additional, Klingler, Karl, additional, Schöb, Othmar, additional, Andaz, Shahriyour, additional, Straznicka, Michaela, additional, Chin, Cynthia, additional, Weiser, Todd, additional, Sone, Shusuke, additional, Hanaoka, Takaomi, additional, Roberts, Heidi, additional, Patsios, Demetris, additional, Scopetuolo, M., additional, Brown, Andrew, additional, Wu, Ning, additional, Cole, Enser, additional, Yeh, Diana, additional, Luedke, Dan, additional, Liu, Xueguo, additional, Herzog, Gary, additional, Aye, Ralph, additional, Rifkin, Matthew, additional, Veronesi, Giula, additional, Infante, Maurizio, additional, Vafai, Davood, additional, Kopel, Samuel, additional, Taylor, Jana, additional, Thurer, Richard, additional, Villamizar, Nestor, additional, Austin, John H.M., additional, Pearson, Gregory D.N., additional, Klippenstein, Donald, additional, Litwin, Alan, additional, Loud, Peter A., additional, Kohman, Leslie J., additional, Scalzetti, Ernest M., additional, Khan, Arfa, additional, Shah, Rakesh, additional, Mayfield, William, additional, Frumiento, Carmine, additional, Smith, Michael V., additional, Thorsen, M. Kristin, additional, Hansen, Richard, additional, Naidich, David, additional, McGuinness, Georgeann, additional, Widmann, Mark, additional, Korst, Robert, additional, Lowry, Joseph, additional, Salvatore, Mary, additional, Walsh, James, additional, Bertsch, David, additional, Scheinberg, Paul, additional, Sheppard, Barry, additional, Cecchi, Gary, additional, Ginsberg, Michelle S., additional, Welch, Laura, additional, Raz, Dan J., additional, Rotter, Arnold, additional, Matalon, Terence A.S., additional, Cheung, Edson H., additional, Glassberg, Robert, additional, Olsen, David, additional, Mullen, David, additional, Odzer, Shari-Lynn, additional, Wiernik, Peter H., additional, Ray, Daniel, additional, DeCunzo, Louis, additional, Endress, Carmen, additional, Vacca, Anthony, additional, Kondapaneni, M., additional, Lim, Melissa, additional, Kalafer, Michael, additional, Green, Jeremy, additional, Yoder, Mark, additional, Shah, Palmi, additional, Camacho, Elmer, additional, O’Brien, James, additional, Willey, James C., additional, Gordon, David, additional, and Kocha, Albert, additional

Published
2023
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15. Cell type-specific disruption of cortico-striatal circuitry drives repetitive patterns of behaviour in fragile X syndrome model mice

Author

Longo, Francesco, primary, Aryal, Sameer, additional, Anastasiades, Paul, additional, Maltese, Marta, additional, Baimel, Corey, additional, Albanese, Federica, additional, Tabor, Joanna, additional, Zhu, Jeffrey D, additional, Oliveira, Mauricio M, additional, Gastaldo, Denise, additional, Bagni, Claudia, additional, Santini, Emanuela, additional, Tritsch, Nicolas X, additional, Carter, Adam G, additional, and Klann, Eric, additional

Published
2022
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16. Additional file 5 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Abstract

Supplementary Material 5

Published
2023
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17. Additional file 4 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Abstract

Supplementary Material 4

Published
2023
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18. Additional file 1 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Abstract

Supplementary Material 1

Published
2023
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19. Additional file 3 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Abstract

Supplementary Material 3

Published
2023
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20. Additional file 6 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Abstract

Supplementary Material 6

Published
2023
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21. Additional file 2 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Abstract

Supplementary Material 2

Published
2023
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22. BioBot: Innovative Offloading of Astronauts for More Effective Exploration

Author

Akin, David L, Malone, Kate, Sack, Brady, and Zhu, Jeffrey

Subjects
Man/System Technology And Life Support
Abstract

The BioBot concept consists of a robotic rover which is capable of traversing the same terrain as a spacesuited human. It carries the primary life support system for the astronaut, including consumables, atmosphere revitalization systems (e.g., CO2 scrubbing, humidity and temperature management, ventilation fan), power system (e.g., battery, power management and distribution),and thermal control system (e.g., water sublimator, cooling water pump), along with umbilical lines to connect to the supported astronaut. Although not technically part of life support, it would be logical for the BioBot to also provide long-range communications, video monitoring, tool and sample transport, and other functions to enable and enhance EVA productivity in planetary surface exploration.The design reference scenario for this concept is that astronauts involved in future lunar or Mars exploration will be on the surface for weeks or months rather than days, and will be involved in regular EVA operations. It is not unreasonable to think of geologists spending several days inEVA exploration each week over a prolonged mission duration, with far more ambitious operational objectives than were typical of Apollo. In this scenario, each astronaut will be accompanied by a "BioBot", which will transport their life support system and consumables, an extended umbilical and umbilical reel, and robotic systems capable of controlling the position and motion of the umbilical. The astronaut will be connected to the robot via the umbilical, carrying only a small emergency open-loop life support system similar to those contained in every PLSS. The robotic mobility base will be designed to be capable of traveling anywhere the astronaut can walk, and will also be useful as a transport for the EVA tools, science instrumentation, and collected samples. In addition, the BioBot can potentially carry the astronaut on traverses as well. Such a system will also be a significant enhancement to public engagement in these future exploration missions, as the robotic vehicles can also support high-resolution cameras and high bandwidth communications gear to providehigh-definition video coverage of each crew throughout each EVA sortie.

Published
2019

23. The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules.

Author

Tahvilian, Shahram, Kuban, Joshua D., Yankelevitz, David F., Leventon, Daniel, Henschke, Claudia I., Zhu, Jeffrey, Baden, Lara, Yip, Rowena, Hirsch, Fred R., Reed, Rebecca, Brown, Ashley, Muldoon, Allison, Trejo, Michael, Katchman, Benjamin A., Donovan, Michael J., and Pagano, Paul C.

Subjects
PULMONARY nodules, LUNG cancer, DISEASE risk factors, FLUORESCENCE in situ hybridization, BLOOD cells
Abstract

Purpose: Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis. Methods: LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated. Results: Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52). Conclusion: Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway. [ABSTRACT FROM AUTHOR]

Published
2023
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24. New or enlarging hiatal hernias after thoracic surgery for early lung cancer

Author

Song, Kimberly J., primary, Yip, Rowena, additional, Chung, Michael, additional, Cai, Qiang, additional, Zhu, Yeqing, additional, Singh, Ayushi, additional, Lewis, Erik E., additional, Yankelevitz, David, additional, Taioli, Emanuela, additional, Henschke, Claudia, additional, Flores, Raja, additional, Kaufman, Andrew, additional, Lee, Dong-Seok, additional, Nicastri, Daniel, additional, Wolf, Andrea, additional, Song, Kimberly, additional, Rosenzweig, Kenneth, additional, Gomez, Jorge, additional, Beasley, Mary Beth, additional, Zakowski, Maureen, additional, Yankelevitz, David F., additional, Henschke, Claudia I., additional, Schwartz, Rebecca, additional, Chan, Huiwen, additional, Zhu, Jeffrey, additional, Kantor, Sydney, additional, Adler, Shana, additional, Raad, Wissam, additional, Buyuk, Zrzu, additional, Friedman, Adie, additional, Dreifuss, Ronald, additional, Verzosa, Stacey, additional, Yakubox, Mariya, additional, Aloferdova, Karina, additional, Stacey, Patricia, additional, De Nobrega, Simone, additional, Hakami, Ardeshir, additional, Pass, Harvey, additional, Crawford, Berne, additional, Donnington, Jessica, additional, Cooper, Benjamin, additional, Moreirea, Andre, additional, Sorensen, Audrey, additional, Kohman, Leslie, additional, Dunton, Robert, additional, Wallen, Jason, additional, Curtiss, Christopher, additional, Scalzetti, Ernest, additional, Ellinwood, Linda, additional, Connery, Clifford P., additional, Torres, Emilo, additional, Cruzer, Dan, additional, Gendron, Bruce, additional, Alyea, Sonya, additional, Lackaye, Daniel, additional, Studer, Lauren, additional, Becker, Betsy, additional, Jirapatnakul, Artit, additional, You, Nan, additional, Libby, Daniel M., additional, Smith, James P., additional, Pasmantier, Mark, additional, Reeves, A.P., additional, Markowitz, Steven, additional, Miller, Albert, additional, Deval, Jose Cervera, additional, Roberts, Heidi, additional, Patsios, Demetris, additional, Sone, Shusuke, additional, Hanaoka, Takaomi, additional, Zulueta, Javier, additional, de-Torres, Juan P., additional, Lozano, Maria D., additional, Aye, Ralph, additional, Manning, Kristin, additional, Care, Christiana, additional, Bauer, Thomas, additional, Canitano, Stefano, additional, Giunta, Salvatore, additional, Cole, Enser, additional, Klingler, Karl, additional, Austin, John H.M., additional, Pearson, Gregory D.N., additional, Shaham, Dorith, additional, Aylesworth, Cheryl, additional, Meyers, Patrick, additional, Andaz, Shahriyour, additional, Vafai, Davood, additional, Naidich, David, additional, McGuinness, Georgeann, additional, Sheppard, Barry, additional, Rifkin, Matthew, additional, Thorsen, M. Kristin, additional, Hansen, Richard, additional, Kopel, Samuel, additional, Mayfield, William, additional, Luedke, Dan, additional, Klippenstein, Donald, additional, Litwin, Alan, additional, Loud, Peter A., additional, Kohman, Leslie J., additional, Scalzetti, Ernest M., additional, Thurer, Richard, additional, Villamizar, Nestor, additional, Khan, Arfa, additional, Shah, Rakesh, additional, Liu, Xueguo, additional, Herzog, Gary, additional, Yeh, Diana, additional, Wu, Ning, additional, Lowry, Joseph, additional, Salvatore, Mary, additional, Frumiento, Carmine, additional, Mendelson, David S., additional, Smith, Michael V., additional, Korst, Robert, additional, Taylor, Jana, additional, Ginsberg, Michelle S., additional, Straznicka, Michaela, additional, Widmann, Mark, additional, Cecchi, Gary, additional, Matalon, Terence A.S., additional, Scheinberg, Paul, additional, Odzer, Shari-Lynn, additional, Olsen, David, additional, Grannis, Fred, additional, Rotter, Arnold, additional, Ray, Daniel, additional, Mullen, David, additional, Wiernik, Peter H., additional, Cheung, Edson H., additional, Lim, Melissa, additional, DeCunzo, Louis, additional, Glassberg, Robert, additional, Endress, Carmen, additional, Yoder, Mark, additional, Shah, Palmi, additional, Welch, Laura, additional, Kalafer, Michael, additional, Green, Jeremy, additional, Walsh, James, additional, Bertsch, David, additional, Camacho, Elmer, additional, Chin, Cynthia, additional, O'Brien, James, additional, and Willey, James C., additional

Published
2022
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29. A systematic review of technology-based interventions for unintentional injury prevention education and behaviour change.

Author

Omaki, Elise, Rizzutti, Nicholas, Shields, Wendy, Zhu, Jeffrey, McDonald, Eileen, Stevens, Martha W., and Gielen, Andrea

Subjects
ACCIDENT prevention, BEHAVIOR modification, COMPUTER software, COMPUTERS, EDUCATION, INTELLECT, INTERNET, ONLINE information services, PUBLIC health, QUALITY assurance, TECHNOLOGY, HUMAN services programs
Published
2017
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30. Still Too Hot

Author

Shields, Wendy C., primary, McDonald, Eileen, additional, Frattaroli, Shannon, additional, Perry, Elise C., additional, Zhu, Jeffrey, additional, and Gielen, Andrea C., additional

Published
2013
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32. A systematic review of technology-based interventions for unintentional injury prevention education and behaviour change.

Author

Omaki E, Rizzutti N, Shields W, Zhu J, McDonald E, Stevens MW, and Gielen A

Subjects
Health Behavior, Health Knowledge, Attitudes, Practice, Humans, Program Evaluation, United States, Accident Prevention instrumentation, Accident Prevention methods, Health Promotion methods, Internet statistics & numerical data, Public Health Informatics instrumentation, Wounds and Injuries prevention & control
Abstract

Objective: The aims of this literature review are to (1) summarise how computer and mobile technology-based health behaviour change applications have been evaluated in unintentional injury prevention, (2) describe how these successes can be applied to injury-prevention programmes in the future and (3) identify research gaps., Methods: Studies included in this systematic review were education and behaviour change intervention trials and programme evaluations in which the intervention was delivered by either a computer or mobile technology and addressed an unintentional injury prevention topic. Articles were limited to those published in English and after 1990., Results: Among the 44 technology-based injury-prevention studies included in this review, 16 studies evaluated locally hosted software programmes, 4 studies offered kiosk-based programmes, 11 evaluated remotely hosted internet programmes, 2 studies used mobile technology or portable devices and 11 studies evaluated virtual-reality interventions. Locally hosted software programmes and remotely hosted internet programmes consistently increased knowledge and behaviours. Kiosk programmes showed evidence of modest knowledge and behaviour gains. Both programmes using mobile technology improved behaviours. Virtual-reality programmes consistently improved behaviours, but there were little gains in knowledge. No studies evaluated text-messaging programmes dedicated to injury prevention., Conclusions: There is much potential for computer-based programmes to be used for injury-prevention behaviour change. The reviewed studies provide evidence that computer-based communication is effective in conveying information and influencing how participants think about an injury topic and adopt safety behaviours., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Published
2017
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