Search

Your search keyword '"Friedman, Scott"' showing total 2,543 results
Start Over Author "Friedman, Scott"
2,543 results on '"Friedman, Scott"'

1. Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in Corpora and LLMs

Author

Friedman, Scott E., Benkler, Noam, Mosaphir, Drisana, Rye, Jeffrey, Schmer-Galunder, Sonja M., Goldwater, Micah, McLure, Matthew, Wheelock, Ruta, Gottlieb, Jeremy, Goldman, Robert P., and Miller, Christopher

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

Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accuracy, inclusion, and cultural fidelity. We present a validated approach to automatically (1) extracting heterogeneous latent value propositions from texts, (2) assessing resonance and conflict of values with texts, and (3) combining these operations to characterize the pluralistic value alignment of human-sourced and LLM-sourced textual data.

Published
2024

2. Soft Measures for Extracting Causal Collective Intelligence

Author

Berijanian, Maryam, Dork, Spencer, Singh, Kuldeep, Millikan, Michael Riley, Riggs, Ashlin, Swaminathan, Aadarsh, Gibbs, Sarah L., Friedman, Scott E., and Brugnone, Nathan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Social and Information Networks
Abstract

Understanding and modeling collective intelligence is essential for addressing complex social systems. Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models, but extracting high-integrity FCMs from text is challenging. This study presents an approach using large language models (LLMs) to automate FCM extraction. We introduce novel graph-based similarity measures and evaluate them by correlating their outputs with human judgments through the Elo rating system. Results show positive correlations with human evaluations, but even the best-performing measure exhibits limitations in capturing FCM nuances. Fine-tuning LLMs improves performance, but existing measures still fall short. This study highlights the need for soft similarity measures tailored to FCM extraction, advancing collective intelligence modeling with NLP., Comment: Camera-ready version accepted for publication in the EMNLP 2024 Workshop NLP4Science

Published
2024

3. Annotator in the Loop: A Case Study of In-Depth Rater Engagement to Create a Bridging Benchmark Dataset

Author

Schmer-Galunder, Sonja, Wheelock, Ruta, Friedman, Scott, Chvasta, Alyssa, Jalan, Zaria, and Saltz, Emily

Subjects
Computer Science - Computers and Society
Abstract

With the growing prevalence of large language models, it is increasingly common to annotate datasets for machine learning using pools of crowd raters. However, these raters often work in isolation as individual crowdworkers. In this work, we regard annotation not merely as inexpensive, scalable labor, but rather as a nuanced interpretative effort to discern the meaning of what is being said in a text. We describe a novel, collaborative, and iterative annotator-in-the-loop methodology for annotation, resulting in a 'Bridging Benchmark Dataset' of comments relevant to bridging divides, annotated from 11,973 textual posts in the Civil Comments dataset. The methodology differs from popular anonymous crowd-rating annotation processes due to its use of an in-depth, iterative engagement with seven US-based raters to (1) collaboratively refine the definitions of the to-be-annotated concepts and then (2) iteratively annotate complex social concepts, with check-in meetings and discussions. This approach addresses some shortcomings of current anonymous crowd-based annotation work, and we present empirical evidence of the performance of our annotation process in the form of inter-rater reliability. Our findings indicate that collaborative engagement with annotators can enhance annotation methods, as opposed to relying solely on isolated work conducted remotely. We provide an overview of the input texts, attributes, and annotation process, along with the empirical results and the resulting benchmark dataset, categorized according to the following attributes: Alienation, Compassion, Reasoning, Curiosity, Moral Outrage, and Respect., Comment: Accepted Paper at AAAI/ACM Artificial Intelligence, Ethics, and Society (AIES)

Published
2024

6. Assessing LLMs for Moral Value Pluralism

Author

Benkler, Noam, Mosaphir, Drisana, Friedman, Scott, Smart, Andrew, and Schmer-Galunder, Sonja

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

The fields of AI current lacks methods to quantitatively assess and potentially alter the moral values inherent in the output of large language models (LLMs). However, decades of social science research has developed and refined widely-accepted moral value surveys, such as the World Values Survey (WVS), eliciting value judgments from direct questions in various geographies. We have turned those questions into value statements and use NLP to compute to how well popular LLMs are aligned with moral values for various demographics and cultures. While the WVS is accepted as an explicit assessment of values, we lack methods for assessing implicit moral and cultural values in media, e.g., encountered in social media, political rhetoric, narratives, and generated by AI systems such as LLMs that are increasingly present in our daily lives. As we consume online content and utilize LLM outputs, we might ask, which moral values are being implicitly promoted or undercut, or -- in the case of LLMs -- if they are intending to represent a cultural identity, are they doing so consistently? In this paper we utilize a Recognizing Value Resonance (RVR) NLP model to identify WVS values that resonate and conflict with a given passage of output text. We apply RVR to the text generated by LLMs to characterize implicit moral values, allowing us to quantify the moral/cultural distance between LLMs and various demographics that have been surveyed using the WVS. In line with other work we find that LLMs exhibit several Western-centric value biases; they overestimate how conservative people in non-Western countries are, they are less accurate in representing gender for non-Western countries, and portray older populations as having more traditional values. Our results highlight value misalignment and age groups, and a need for social science informed technological solutions addressing value plurality in LLMs., Comment: Accepted Paper to workshop on "AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics" at NeurIPS 2023

Published
2023

8. An unbiased ranking of murine dietary models based on their proximity to human metabolic dysfunction-associated steatotic liver disease (MASLD)

Author

Vacca, Michele, Kamzolas, Ioannis, Harder, Lea Mørch, Oakley, Fiona, Trautwein, Christian, Hatting, Maximilian, Ross, Trenton, Bernardo, Barbara, Oldenburger, Anouk, Hjuler, Sara Toftegaard, Ksiazek, Iwona, Lindén, Daniel, Schuppan, Detlef, Rodriguez-Cuenca, Sergio, Tonini, Maria Manuela, Castañeda, Tamara R., Kannt, Aimo, Rodrigues, Cecília M. P., Cockell, Simon, Govaere, Olivier, Daly, Ann K., Allison, Michael, Honnens de Lichtenberg, Kristian, Kim, Yong Ook, Lindblom, Anna, Oldham, Stephanie, Andréasson, Anne-Christine, Schlerman, Franklin, Marioneaux, Jonathon, Sanyal, Arun, Afonso, Marta B., Younes, Ramy, Amano, Yuichiro, Friedman, Scott L., Wang, Shuang, Bhattacharya, Dipankar, Simon, Eric, Paradis, Valérie, Burt, Alastair, Grypari, Ioanna Maria, Davies, Susan, Driessen, Ann, Yashiro, Hiroaki, Pors, Susanne, Worm Andersen, Maja, Feigh, Michael, Yunis, Carla, Bedossa, Pierre, Stewart, Michelle, Cater, Heather L., Wells, Sara, Schattenberg, Jörn M., Anstee, Quentin M., Tiniakos, Dina, Perfield, James W., Petsalaki, Evangelia, Davidsen, Peter, and Vidal-Puig, Antonio

Published
2024
Full Text
View/download PDF

9. The James Webb Space Telescope Mission

Author

Gardner, Jonathan P., Mather, John C., Abbott, Randy, Abell, James S., Abernathy, Mark, Abney, Faith E., Abraham, John G., Abraham, Roberto, Abul-Huda, Yasin M., Acton, Scott, Adams, Cynthia K., Adams, Evan, Adler, David S., Adriaensen, Maarten, Aguilar, Jonathan Albert, Ahmed, Mansoor, Ahmed, Nasif S., Ahmed, Tanjira, Albat, Rüdeger, Albert, Loïc, Alberts, Stacey, Aldridge, David, Allen, Mary Marsha, Allen, Shaune S., Altenburg, Martin, Altunc, Serhat, Alvarez, Jose Lorenzo, Álvarez-Márquez, Javier, de Oliveira, Catarina Alves, Ambrose, Leslie L., Anandakrishnan, Satya M., Andersen, Gregory C., Anderson, Harry James, Anderson, Jay, Anderson, Kristen, Anderson, Sara M., Aprea, Julio, Archer, Benita J., Arenberg, Jonathan W., Argyriou, Ioannis, Arribas, Santiago, Artigau, Étienne, Arvai, Amanda Rose, Atcheson, Paul, Atkinson, Charles B., Averbukh, Jesse, Aymergen, Cagatay, Bacinski, John J., Baggett, Wayne E., Bagnasco, Giorgio, Baker, Lynn L., Balzano, Vicki Ann, Banks, Kimberly A., Baran, David A., Barker, Elizabeth A., Barrett, Larry K., Barringer, Bruce O., Barto, Allison, Bast, William, Baudoz, Pierre, Baum, Stefi, Beatty, Thomas G., Beaulieu, Mathilde, Bechtold, Kathryn, Beck, Tracy, Beddard, Megan M., Beichman, Charles, Bellagama, Larry, Bely, Pierre, Berger, Timothy W., Bergeron, Louis E., Darveau-Bernier, Antoine, Bertch, Maria D., Beskow, Charlotte, Betz, Laura E., Biagetti, Carl P., Birkmann, Stephan, Bjorklund, Kurt F., Blackwood, James D., Blazek, Ronald Paul, Blossfeld, Stephen, Bluth, Marcel, Boccaletti, Anthony, Boegner Jr., Martin E., Bohlin, Ralph C., Boia, John Joseph, Böker, Torsten, Bonaventura, N., Bond, Nicholas A., Bosley, Kari Ann, Boucarut, Rene A., Bouchet, Patrice, Bouwman, Jeroen, Bower, Gary, Bowers, Ariel S., Bowers, Charles W., Boyce, Leslye A., Boyer, Christine T., Boyer, Martha L., Boyer, Michael, Boyer, Robert, Bradley, Larry D., Brady, Gregory R., Brandl, Bernhard R., Brannen, Judith L., Breda, David, Bremmer, Harold G., Brennan, David, Bresnahan, Pamela A., Bright, Stacey N., Broiles, Brian J., Bromenschenkel, Asa, Brooks, Brian H., Brooks, Keira J., Brown, Bob, Brown, Bruce, Brown, Thomas M., Bruce, Barry W., Bryson, Jonathan G., Bujanda, Edwin D., Bullock, Blake M., Bunker, A. J., Bureo, Rafael, Burt, Irving J., Bush, James Aaron, Bushouse, Howard A., Bussman, Marie C., Cabaud, Olivier, Cale, Steven, Calhoon, Charles D., Calvani, Humberto, Canipe, Alicia M., Caputo, Francis M., Cara, Mihai, Carey, Larkin, Case, Michael Eli, Cesari, Thaddeus, Cetorelli, Lee D., Chance, Don R., Chandler, Lynn, Chaney, Dave, Chapman, George N., Charlot, S., Chayer, Pierre, Cheezum, Jeffrey I., Chen, Bin, Chen, Christine H., Cherinka, Brian, Chichester, Sarah C., Chilton, Zachary S., Chittiraibalan, Dharini, Clampin, Mark, Clark, Charles R., Clark, Kerry W., Clark, Stephanie M., Claybrooks, Edward E., Cleveland, Keith A., Cohen, Andrew L., Cohen, Lester M., Colón, Knicole D., Coleman, Benee L., Colina, Luis, Comber, Brian J., Comeau, Thomas M., Comer, Thomas, Reis, Alain Conde, Connolly, Dennis C., Conroy, Kyle E., Contos, Adam R., Contreras, James, Cook, Neil J., Cooper, James L., Cooper, Rachel Aviva, Correia, Michael F., Correnti, Matteo, Cossou, Christophe, Costanza, Brian F., Coulais, Alain, Cox, Colin R., Coyle, Ray T., Cracraft, Misty M., Noriega-Crespo, Alberto, Crew, Keith A., Curtis, Gary J., Cusveller, Bianca, Maciel, Cleyciane Da Costa, Dailey, Christopher T., Daugeron, Frédéric, Davidson, Greg S., Davies, James E., Davis, Katherine Anne, Davis, Michael S., Day, Ratna, de Chambure, Daniel, de Jong, Pauline, De Marchi, Guido, Dean, Bruce H., Decker, John E., Delisa, Amy S., Dell, Lawrence C., Dellagatta, Gail, Dembinska, Franciszka, Demosthenes, Sandor, Dencheva, Nadezhda M., Deneu, Philippe, DePriest, William W., Deschenes, Jeremy, Dethienne, Nathalie, Detre, Örs Hunor, Diaz, Rosa Izela, Dicken, Daniel, DiFelice, Audrey S., Dillman, Matthew, Disharoon, Maureen O., van Dishoeck, Ewine F., Dixon, William V., Doggett, Jesse B., Dominguez, Keisha L., Donaldson, Thomas S., Doria-Warner, Cristina M., Santos, Tony Dos, Doty, Heather, Douglas Jr., Robert E., Doyon, René, Dressler, Alan, Driggers, Jennifer, Driggers, Phillip A., Dunn, Jamie L., DuPrie, Kimberly C., Dupuis, Jean, Durning, John, Dutta, Sanghamitra B., Earl, Nicholas M., Eccleston, Paul, Ecobichon, Pascal, Egami, Eiichi, Ehrenwinkler, Ralf, Eisenhamer, Jonathan D., Eisenhower, Michael, Eisenstein, Daniel J., Hamel, Zaky El, Elie, Michelle L., Elliott, James, Elliott, Kyle Wesley, Engesser, Michael, Espinoza, Néstor, Etienne, Odessa, Etxaluze, Mireya, Evans, Leah, Fabreguettes, Luce, Falcolini, Massimo, Falini, Patrick R., Fatig, Curtis, Feeney, Matthew, Feinberg, Lee D., Fels, Raymond, Ferdous, Nazma, Ferguson, Henry C., Ferrarese, Laura, Ferreira, Marie-Héléne, Ferruit, Pierre, Ferry, Malcolm, Filippazzo, Joseph Charles, Firre, Daniel, Fix, Mees, Flagey, Nicolas, Flanagan, Kathryn A., Fleming, Scott W., Florian, Michael, Flynn, James R., Foiadelli, Luca, Fontaine, Mark R., Fontanella, Erin Marie, Forshay, Peter Randolph, Fortner, Elizabeth A., Fox, Ori D., Framarini, Alexandro P., Francisco, John I., Franck, Randy, Franx, Marijn, Franz, David E., Friedman, Scott D., Friend, Katheryn E., Frost, James R., Fu, Henry, Fullerton, Alexander W., Gaillard, Lionel, Galkin, Sergey, Gallagher, Ben, Galyer, Anthony D., Marín, Macarena García, Gardner, Lisa E., Garland, Dennis, Garrett, Bruce Albert, Gasman, Danny, Gáspár, András, Gastaud, René, Gaudreau, Daniel, Gauthier, Peter Timothy, Geers, Vincent, Geithner, Paul H., Gennaro, Mario, Gerber, John, Gereau, John C., Giampaoli, Robert, Giardino, Giovanna, Gibbons, Paul C., Gilbert, Karolina, Gilman, Larry, Girard, Julien H., Giuliano, Mark E., Gkountis, Konstantinos, Glasse, Alistair, Glassmire, Kirk Zachary, Glauser, Adrian Michael, Glazer, Stuart D., Goldberg, Joshua, Golimowski, David A., Gonzaga, Shireen P., Gordon, Karl D., Gordon, Shawn J., Goudfrooij, Paul, Gough, Michael J., Graham, Adrian J., Grau, Christopher M., Green, Joel David, Greene, Gretchen R., Greene, Thomas P., Greenfield, Perry E., Greenhouse, Matthew A., Greve, Thomas R., Greville, Edgar M., Grimaldi, Stefano, Groe, Frank E., Groebner, Andrew, Grumm, David M., Grundy, Timothy, Güdel, Manuel, Guillard, Pierre, Guldalian, John, Gunn, Christopher A., Gurule, Anthony, Gutman, Irvin Meyer, Guy, Paul D., Guyot, Benjamin, Hack, Warren J., Haderlein, Peter, Hagan, James B., Hagedorn, Andria, Hainline, Kevin, Haley, Craig, Hami, Maryam, Hamilton, Forrest Clifford, Hammann, Jeffrey, Hammel, Heidi B., Hanley, Christopher J., Hansen, Carl August, Hardy, Bruce, Harnisch, Bernd, Harr, Michael Hunter, Harris, Pamela, Hart, Jessica Ann, Hartig, George F., Hasan, Hashima, Hashim, Kathleen Marie, Hashimoto, Ryan, Haskins, Sujee J., Hawkins, Robert Edward, Hayden, Brian, Hayden, William L., Healy, Mike, Hecht, Karen, Heeg, Vince J., Hejal, Reem, Helm, Kristopher A., Hengemihle, Nicholas J., Henning, Thomas, Henry, Alaina, Henry, Ronald L., Henshaw, Katherine, Hernandez, Scarlin, Herrington, Donald C., Heske, Astrid, Hesman, Brigette Emily, Hickey, David L., Hilbert, Bryan N., Hines, Dean C., Hinz, Michael R., Hirsch, Michael, Hitcho, Robert S., Hodapp, Klaus, Hodge, Philip E., Hoffman, Melissa, Holfeltz, Sherie T., Holler, Bryan Jason, Hoppa, Jennifer Rose, Horner, Scott, Howard, Joseph M., Howard, Richard J., Huber, Jean M., Hunkeler, Joseph S., Hunter, Alexander, Hunter, David Gavin, Hurd, Spencer W., Hurst, Brendan J., Hutchings, John B., Hylan, Jason E., Ignat, Luminita Ilinca, Illingworth, Garth, Irish, Sandra M., Isaacs III, John C., Jackson Jr., Wallace C., Jaffe, Daniel T., Jahic, Jasmin, Jahromi, Amir, Jakobsen, Peter, James, Bryan, James, John C., James, LeAndrea Rae, Jamieson, William Brian, Jandra, Raymond D., Jayawardhana, Ray, Jedrzejewski, Robert, Jeffers, Basil S., Jensen, Peter, Joanne, Egges, Johns, Alan T., Johnson, Carl A., Johnson, Eric L., Johnson, Patricia, Johnson, Phillip Stephen, Johnson, Thomas K., Johnson, Timothy W., Johnstone, Doug, Jollet, Delphine, Jones, Danny P., Jones, Gregory S., Jones, Olivia C., Jones, Ronald A., Jones, Vicki, Jordan, Ian J., Jordan, Margaret E., Jue, Reginald, Jurkowski, Mark H., Justis, Grant, Justtanont, Kay, Kaleida, Catherine C., Kalirai, Jason S., Kalmanson, Phillip Cabrales, Kaltenegger, Lisa, Kammerer, Jens, Kan, Samuel K., Kanarek, Graham Childs, Kao, Shaw-Hong, Karakla, Diane M., Karl, Hermann, Kassin, Susan A., Kauffman, David D., Kavanagh, Patrick, Kelley, Leigh L., Kelly, Douglas M., Kendrew, Sarah, Kennedy, Herbert V., Kenny, Deborah A., Keski-Kuha, Ritva A., Keyes, Charles D., Khan, Ali, Kidwell, Richard C., Kimble, Randy A., King, James S., King, Richard C., Kinzel, Wayne M., Kirk, Jeffrey R., Kirkpatrick, Marc E., Klaassen, Pamela, Klingemann, Lana, Klintworth, Paul U., Knapp, Bryan Adam, Knight, Scott, Knollenberg, Perry J., Knutsen, Daniel Mark, Koehler, Robert, Koekemoer, Anton M., Kofler, Earl T., Kontson, Vicki L., Kovacs, Aiden Rose, Kozhurina-Platais, Vera, Krause, Oliver, Kriss, Gerard A., Krist, John, Kristoffersen, Monica R., Krogel, Claudia, Krueger, Anthony P., Kulp, Bernard A., Kumari, Nimisha, Kwan, Sandy W., Kyprianou, Mark, Labador, Aurora Gadiano, Labiano, Álvaro, Lafrenière, David, Lagage, Pierre-Olivier, Laidler, Victoria G., Laine, Benoit, Laird, Simon, Lajoie, Charles-Philippe, Lallo, Matthew D., Lam, May Yen, LaMassa, Stephanie Marie, Lambros, Scott D., Lampenfield, Richard Joseph, Lander, Matthew Ed, Langston, James Hutton, Larson, Kirsten, Larson, Melora, LaVerghetta, Robert Joseph, Law, David R., Lawrence, Jon F., Lee, David W., Lee, Janice, Lee, Yat-Ning Paul, Leisenring, Jarron, Leveille, Michael Dunlap, Levenson, Nancy A., Levi, Joshua S., Levine, Marie B., Lewis, Dan, Lewis, Jake, Lewis, Nikole, Libralato, Mattia, Lidon, Norbert, Liebrecht, Paula Louisa, Lightsey, Paul, Lilly, Simon, Lim, Frederick C., Lim, Pey Lian, Ling, Sai-Kwong, Link, Lisa J., Link, Miranda Nicole, Lipinski, Jamie L., Liu, XiaoLi, Lo, Amy S., Lobmeyer, Lynette, Logue, Ryan M., Long, Chris A., Long, Douglas R., Long, Ilana D., Long, Knox S., López-Caniego, Marcos, Lotz, Jennifer M., Love-Pruitt, Jennifer M., Lubskiy, Michael, Luers, Edward B., Luetgens, Robert A., Luevano, Annetta J., Lui, Sarah Marie G. Flores, Lund III, James M., Lundquist, Ray A., Lunine, Jonathan, Lützgendorf, Nora, Lynch, Richard J., MacDonald, Alex J., MacDonald, Kenneth, Macias, Matthew J., Macklis, Keith I., Maghami, Peiman, Maharaja, Rishabh Y., Maiolino, Roberto, Makrygiannis, Konstantinos G., Malla, Sunita Giri, Malumuth, Eliot M., Manjavacas, Elena, Marini, Andrea, Marrione, Amanda, Marston, Anthony, Martel, André R, Martin, Didier, Martin, Peter G., Martinez, Kristin L., Maschmann, Marc, Masci, Gregory L., Masetti, Margaret E., Maszkiewicz, Michael, Matthews, Gary, Matuskey, Jacob E., McBrayer, Glen A., McCarthy, Donald W., McCaughrean, Mark J., McClare, Leslie A., McClare, Michael D., McCloskey, John C., McClurg, Taylore D., McCoy, Martin, McElwain, Michael W., McGregor, Roy D., McGuffey, Douglas B., McKay, Andrew G., McKenzie, William K., McLean, Brian, McMaster, Matthew, McNeil, Warren, De Meester, Wim, Mehalick, Kimberly L., Meixner, Margaret, Meléndez, Marcio, Menzel, Michael P., Menzel, Michael T., Merz, Matthew, Mesterharm, David D., Meyer, Michael R., Meyett, Michele L., Meza, Luis E., Midwinter, Calvin, Milam, Stefanie N., Miller, Jay Todd, Miller, William C., Miskey, Cherie L., Misselt, Karl, Mitchell, Eileen P., Mohan, Martin, Montoya, Emily E., Moran, Michael J., Morishita, Takahiro, Moro-Martín, Amaya, Morrison, Debra L., Morrison, Jane, Morse, Ernie C., Moschos, Michael, Moseley, S. H., Mosier, Gary E., Mosner, Peter, Mountain, Matt, Muckenthaler, Jason S., Mueller, Donald G., Mueller, Migo, Muhiem, Daniella, Mühlmann, Prisca, Mullally, Susan Elizabeth, Mullen, Stephanie M., Munger, Alan J, Murphy, Jess, Murray, Katherine T., Muzerolle, James C., Mycroft, Matthew, Myers, Andrew, Myers, Carey R., Myers, Fred Richard R., Myers, Richard, Myrick, Kaila, Nagle IV, Adrian F., Nayak, Omnarayani, Naylor, Bret, Neff, Susan G., Nelan, Edmund P., Nella, John, Nguyen, Duy Tuong, Nguyen, Michael N., Nickson, Bryony, Nidhiry, John Joseph, Niedner, Malcolm B., Nieto-Santisteban, Maria, Nikolov, Nikolay K., Nishisaka, Mary Ann, Nota, Antonella, O'Mara, Robyn C., Oboryshko, Michael, O'Brien, Marcus B., Ochs, William R., Offenberg, Joel D., Ogle, Patrick Michael, Ohl, Raymond G., Olmsted, Joseph Hamden, Osborne, Shannon Barbara, O'Shaughnessy, Brian Patrick, Östlin, Göran, O'Sullivan, Brian, Otor, O. Justin, Ottens, Richard, Ouellette, Nathalie N. -Q., Outlaw, Daria J., Owens, Beverly A., Pacifici, Camilla, Page, James Christophe, Paranilam, James G., Park, Sang, Parrish, Keith A., Paschal, Laura, Patapis, Polychronis, Patel, Jignasha, Patrick, Keith, Pattishall Jr., Robert A., Paul, Douglas William, Paul, Shirley J., Pauly, Tyler Andrew, Pavlovsky, Cheryl M., Peña-Guerrero, Maria, Pedder, Andrew H., Peek, Matthew Weldon, Pelham, Patricia A., Penanen, Konstantin, Perriello, Beth A., Perrin, Marshall D., Perrine, Richard F., Perrygo, Chuck, Peslier, Muriel, Petach, Michael, Peterson, Karla A., Pfarr, Tom, Pierson, James M., Pietraszkiewicz, Martin, Pilchen, Guy, Pipher, Judy L., Pirzkal, Norbert, Pitman, Joseph T., Player, Danielle M., Plesha, Rachel, Plitzke, Anja, Pohner, John A., Poletis, Karyn Konstantin, Pollizzi, Joseph A., Polster, Ethan, Pontius, James T., Pontoppidan, Klaus, Porges, Susana C., Potter, Gregg D., Prescott, Stephen, Proffitt, Charles R., Pueyo, Laurent, Neira, Irma Aracely Quispe, Radich, Armando, Rager, Reiko T., Rameau, Julien, Ramey, Deborah D., Alarcon, Rafael Ramos, Rampini, Riccardo, Rapp, Robert, Rashford, Robert A., Rauscher, Bernard J., Ravindranath, Swara, Rawle, Timothy, Rawlings, Tynika N., Ray, Tom, Regan, Michael W., Rehm, Brian, Rehm, Kenneth D., Reid, Neill, Reis, Carl A., Renk, Florian, Reoch, Tom B., Ressler, Michael, Rest, Armin W., Reynolds, Paul J., Richon, Joel G., Richon, Karen V., Ridgaway, Michael, Riedel, Adric Richard, Rieke, George H., Rieke, Marcia, Rifelli, Richard E., Rigby, Jane R., Riggs, Catherine S., Ringel, Nancy J., Ritchie, Christine E., Rix, Hans-Walter, Robberto, Massimo, Robinson, Michael S., Robinson, Orion, Rock, Frank W., Rodriguez, David R., del Pino, Bruno Rodríguez, Roellig, Thomas, Rohrbach, Scott O., Roman, Anthony J., Romelfanger, Frederick J., Romo Jr., Felipe P., Rosales, Jose J., Rose, Perry, Roteliuk, Anthony F., Roth, Marc N., Rothwell, Braden Quinn, Rouzaud, Sylvain, Rowe, Jason, Rowlands, Neil, Roy, Arpita, Royer, Pierre, Rui, Chunlei, Rumler, Peter, Rumpl, William, Russ, Melissa L., Ryan, Michael B., Ryan, Richard M., Saad, Karl, Sabata, Modhumita, Sabatino, Rick, Sabbi, Elena, Sabelhaus, Phillip A., Sabia, Stephen, Sahu, Kailash C., Saif, Babak N., Salvignol, Jean-Christophe, Samara-Ratna, Piyal, Samuelson, Bridget S., Sanders, Felicia A., Sappington, Bradley, Sargent, B. A., Sauer, Arne, Savadkin, Bruce J., Sawicki, Marcin, Schappell, Tina M., Scheffer, Caroline, Scheithauer, Silvia, Scherer, Ron, Schiff, Conrad, Schlawin, Everett, Schmeitzky, Olivier, Schmitz, Tyler S., Schmude, Donald J., Schneider, Analyn, Schreiber, Jürgen, Schroeven-Deceuninck, Hilde, Schultz, John J., Schwab, Ryan, Schwartz, Curtis H., Scoccimarro, Dario, Scott, John F., Scott, Michelle B., Seaton, Bonita L., Seely, Bruce S., Seery, Bernard, Seidleck, Mark, Sembach, Kenneth, Shanahan, Clare Elizabeth, Shaughnessy, Bryan, Shaw, Richard A., Shay, Christopher Michael, Sheehan, Even, Sheth, Kartik, Shih, Hsin-Yi, Shivaei, Irene, Siegel, Noah, Sienkiewicz, Matthew G., Simmons, Debra D., Simon, Bernard P., Sirianni, Marco, Sivaramakrishnan, Anand, Slade, Jeffrey E., Sloan, G. C., Slocum, Christine E., Slowinski, Steven E., Smith, Corbett T., Smith, Eric P., Smith, Erin C., Smith, Koby, Smith, Robert, Smith, Stephanie J., Smolik, John L., Soderblom, David R., Sohn, Sangmo Tony, Sokol, Jeff, Sonneborn, George, Sontag, Christopher D., Sooy, Peter R., Soummer, Remi, Southwood, Dana M., Spain, Kay, Sparmo, Joseph, Speer, David T., Spencer, Richard, Sprofera, Joseph D., Stallcup, Scott S., Stanley, Marcia K., Stansberry, John A., Stark, Christopher C., Starr, Carl W., Stassi, Diane Y., Steck, Jane A., Steeley, Christine D., Stephens, Matthew A., Stephenson, Ralph J., Stewart, Alphonso C., Stiavelli, Massimo, Stockman Jr., Hervey, Strada, Paolo, Straughn, Amber N., Streetman, Scott, Strickland, David Kendal, Strobele, Jingping F., Stuhlinger, Martin, Stys, Jeffrey Edward, Such, Miguel, Sukhatme, Kalyani, Sullivan, Joseph F., Sullivan, Pamela C., Sumner, Sandra M., Sun, Fengwu, Sunnquist, Benjamin Dale, Swade, Daryl Allen, Swam, Michael S., Swenton, Diane F., Swoish, Robby A., Litten, Oi In Tam, Tamas, Laszlo, Tao, Andrew, Taylor, David K., Taylor, Joanna M., Plate, Maurice te, Van Tea, Mason, Teague, Kelly K., Telfer, Randal C., Temim, Tea, Texter, Scott C., Thatte, Deepashri G., Thompson, Christopher Lee, Thompson, Linda M., Thomson, Shaun R., Thronson, Harley, Tierney, C. M., Tikkanen, Tuomo, Tinnin, Lee, Tippet, William Thomas, Todd, Connor William, Tran, Hien D., Trauger, John, Trejo, Edwin Gregorio, Truong, Justin Hoang Vinh, Tsukamoto, Christine L., Tufail, Yasir, Tumlinson, Jason, Tustain, Samuel, Tyra, Harrison, Ubeda, Leonardo, Underwood, Kelli, Uzzo, Michael A., Vaclavik, Steven, Valenduc, Frida, Valenti, Jeff A., Van Campen, Julie, van de Wetering, Inge, Van Der Marel, Roeland P., van Haarlem, Remy, Vandenbussche, Bart, Vanterpool, Dona D., Vernoy, Michael R., Costas, Maria Begoña Vila, Volk, Kevin, Voorzaat, Piet, Voyton, Mark F., Vydra, Ekaterina, Waddy, Darryl J., Waelkens, Christoffel, Wahlgren, Glenn Michael, Walker Jr., Frederick E., Wander, Michel, Warfield, Christine K., Warner, Gerald, Wasiak, Francis C., Wasiak, Matthew F., Wehner, James, Weiler, Kevin R., Weilert, Mark, Weiss, Stanley B., Wells, Martyn, Welty, Alan D., Wheate, Lauren, Wheeler, Thomas P., White, Christy L., Whitehouse, Paul, Whiteleather, Jennifer Margaret, Whitman, William Russell, Williams, Christina C., Willmer, Christopher N. A., Willott, Chris J., Willoughby, Scott P., Wilson, Andrew, Wilson, Debra, Wilson, Donna V., Windhorst, Rogier, Wislowski, Emily Christine, Wolfe, David J., Wolfe, Michael A., Wolff, Schuyler, Wondel, Amancio, Woo, Cindy, Woods, Robert T., Worden, Elaine, Workman, William, Wright, Gillian S., Wu, Carl, Wu, Chi-Rai, Wun, Dakin D., Wymer, Kristen B., Yadetie, Thomas, Yan, Isabelle C., Yang, Keith C., Yates, Kayla L., Yeager, Christopher R., Yerger, Ethan John, Young, Erick T., Young, Gary, Yu, Gene, Yu, Susan, Zak, Dean S., Zeidler, Peter, Zepp, Robert, Zhou, Julia, Zincke, Christian A., Zonak, Stephanie, and Zondag, Elisabeth

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Published
2023
Full Text
View/download PDF

10. The Science Performance of JWST as Characterized in Commissioning

Author

Rigby, Jane, Perrin, Marshall, McElwain, Michael, Kimble, Randy, Friedman, Scott, Lallo, Matt, Doyon, René, Feinberg, Lee, Ferruit, Pierre, Glasse, Alistair, Rieke, Marcia, Rieke, George, Wright, Gillian, Willott, Chris, Colon, Knicole, Milam, Stefanie, Neff, Susan, Stark, Christopher, Valenti, Jeff, Abell, Jim, Abney, Faith, Abul-Huda, Yasin, Acton, D. Scott, Adams, Evan, Adler, David, Aguilar, Jonathan, Ahmed, Nasif, Albert, Loïc, Alberts, Stacey, Aldridge, David, Allen, Marsha, Altenburg, Martin, Marquez, Javier Alvarez, de Oliveira, Catarina Alves, Andersen, Greg, Anderson, Harry, Anderson, Sara, Argyriou, Ioannis, Armstrong, Amber, Arribas, Santiago, Artigau, Etienne, Arvai, Amanda, Atkinson, Charles, Bacon, Gregory, Bair, Thomas, Banks, Kimberly, Barrientes, Jaclyn, Barringer, Bruce, Bartosik, Peter, Bast, William, Baudoz, Pierre, Beatty, Thomas, Bechtold, Katie, Beck, Tracy, Bergeron, Eddie, Bergkoetter, Matthew, Bhatawdekar, Rachana, Birkmann, Stephan, Blazek, Ronald, Blome, Claire, Boccaletti, Anthony, Boeker, Torsten, Boia, John, Bonaventura, Nina, Bond, Nicholas, Bosley, Kari, Boucarut, Ray, Bourque, Matthew, Bouwman, Jeroen, Bower, Gary, Bowers, Charles, Boyer, Martha, Bradley, Larry, Brady, Greg, Braun, Hannah, Breda, David, Bresnahan, Pamela, Bright, Stacey, Britt, Christopher, Bromenschenkel, Asa, Brooks, Brian, Brooks, Keira, Brown, Bob, Brown, Matthew, Brown, Patricia, Bunker, Andy, Burger, Matthew, Bushouse, Howard, Cale, Steven, Cameron, Alex, Cameron, Peter, Canipe, Alicia, Caplinger, James, Caputo, Francis, Cara, Mihai, Carey, Larkin, Carniani, Stefano, Carrasquilla, Maria, Carruthers, Margaret, Case, Michael, Catherine, Riggs, Chance, Don, Chapman, George, Charlot, Stéphane, Charlow, Brian, Chayer, Pierre, Chen, Bin, Cherinka, Brian, Chichester, Sarah, Chilton, Zack, Chonis, Taylor, Clampin, Mark, Clark, Charles, Clark, Kerry, Coe, Dan, Coleman, Benee, Comber, Brian, Comeau, Tom, Connolly, Dennis, Cooper, James, Cooper, Rachel, Coppock, Eric, Correnti, Matteo, Cossou, Christophe, Coulais, Alain, Coyle, Laura, Cracraft, Misty, Curti, Mirko, Cuturic, Steven, Davis, Katherine, Davis, Michael, Dean, Bruce, DeLisa, Amy, deMeester, Wim, Dencheva, Nadia, Dencheva, Nadezhda, DePasquale, Joseph, Deschenes, Jeremy, Detre, Örs Hunor, Diaz, Rosa, Dicken, Dan, DiFelice, Audrey, Dillman, Matthew, Dixon, William, Doggett, Jesse, Donaldson, Tom, Douglas, Rob, DuPrie, Kimberly, Dupuis, Jean, Durning, John, Easmin, Nilufar, Eck, Weston, Edeani, Chinwe, Egami, Eiichi, Ehrenwinkler, Ralf, Eisenhamer, Jonathan, Eisenhower, Michael, Elie, Michelle, Elliott, James, Elliott, Kyle, Ellis, Tracy, Engesser, Michael, Espinoza, Nestor, Etienne, Odessa, Etxaluze, Mireya, Falini, Patrick, Feeney, Matthew, Ferry, Malcolm, Filippazzo, Joseph, Fincham, Brian, Fix, Mees, Flagey, Nicolas, Florian, Michael, Flynn, Jim, Fontanella, Erin, Ford, Terrance, Forshay, Peter, Fox, Ori, Franz, David, Fu, Henry, Fullerton, Alexander, Galkin, Sergey, Galyer, Anthony, Marin, Macarena Garcia, Gardner, Jonathan, Gardner, Lisa, Garland, Dennis, Garrett, Bruce, Gasman, Danny, Gaspar, Andras, Gaudreau, Daniel, Gauthier, Peter, Geers, Vincent, Geithner, Paul, Gennaro, Mario, Giardino, Giovanna, Girard, Julien, Giuliano, Mark, Glassmire, Kirk, Glauser, Adrian, Glazer, Stuart, Godfrey, John, Golimowski, David, Gollnitz, David, Gong, Fan, Gonzaga, Shireen, Gordon, Michael, Gordon, Karl, Goudfrooij, Paul, Greene, Thomas, Greenhouse, Matthew, Grimaldi, Stefano, Groebner, Andrew, Grundy, Timothy, Guillard, Pierre, Gutman, Irvin, Ha, Kong Q., Haderlein, Peter, Hagedorn, Andria, Hainline, Kevin, Haley, Craig, Hami, Maryam, Hamilton, Forrest, Hammel, Heidi, Hansen, Carl, Harkins, Tom, Harr, Michael, Hart, Jessica, Hart, Quyen, Hartig, George, Hashimoto, Ryan, Haskins, Sujee, Hathaway, William, Havey, Keith, Hayden, Brian, Hecht, Karen, Heller-Boyer, Chris, Henriques, Caroline, Henry, Alaina, Hermann, Karl, Hernandez, Scarlin, Hesman, Brigette, Hicks, Brian, Hilbert, Bryan, Hines, Dean, Hoffman, Melissa, Holfeltz, Sherie, Holler, Bryan J., Hoppa, Jennifer, Hott, Kyle, Howard, Joseph, Howard, Rick, Hunter, Alexander, Hunter, David, Hurst, Brendan, Husemann, Bernd, Hustak, Leah, Ignat, Luminita Ilinca, Illingworth, Garth, Irish, Sandra, Jackson, Wallace, Jahromi, Amir, Jakobsen, Peter, James, LeAndrea, James, Bryan, Januszewski, William, Jenkins, Ann, Jirdeh, Hussein, Johnson, Phillip, Johnson, Timothy, Jones, Vicki, Jones, Ron, Jones, Danny, Jones, Olivia, Jordan, Ian, Jordan, Margaret, Jurczyk, Sarah, Jurling, Alden, Kaleida, Catherine, Kalmanson, Phillip, Kammerer, Jens, Kang, Huijo, Kao, Shaw-Hong, Karakla, Diane, Kavanagh, Patrick, Kelly, Doug, Kendrew, Sarah, Kennedy, Herbert, Kenny, Deborah, Keski-kuha, Ritva, Keyes, Charles, Kidwell, Richard, Kinzel, Wayne, Kirk, Jeff, Kirkpatrick, Mark, Kirshenblat, Danielle, Klaassen, Pamela, Knapp, Bryan, Knight, J. Scott, Knollenberg, Perry, Koehler, Robert, Koekemoer, Anton, Kovacs, Aiden, Kulp, Trey, Kumari, Nimisha, Kyprianou, Mark, La Massa, Stephanie, Labador, Aurora, Ortega, Alvaro Labiano, Lagage, Pierre-Olivier, Lajoie, Charles-Phillipe, Lallo, Matthew, Lam, May, Lamb, Tracy, Lambros, Scott, Lampenfield, Richard, Langston, James, Larson, Kirsten, Law, David, Lawrence, Jon, Lee, David, Leisenring, Jarron, Lepo, Kelly, Leveille, Michael, Levenson, Nancy, Levine, Marie, Levy, Zena, Lewis, Dan, Lewis, Hannah, Libralato, Mattia, Lightsey, Paul, Link, Miranda, Liu, Lily, Lo, Amy, Lockwood, Alexandra, Logue, Ryan, Long, Chris, Long, Douglas, Loomis, Charles, Lopez-Caniego, Marcos, Alvarez, Jose Lorenzo, Love-Pruitt, Jennifer, Lucy, Adrian, Luetzgendorf, Nora, Maghami, Peiman, Maiolino, Roberto, Major, Melissa, Malla, Sunita, Malumuth, Eliot, Manjavacas, Elena, Mannfolk, Crystal, Marrione, Amanda, Marston, Anthony, Martel, André, Maschmann, Marc, Masci, Gregory, Masciarelli, Michaela, Maszkiewicz, Michael, Mather, John, McKenzie, Kenny, McLean, Brian, McMaster, Matthew, Melbourne, Katie, Meléndez, Marcio, Menzel, Michael, Merz, Kaiya, Meyett, Michele, Meza, Luis, Miskey, Cherie, Misselt, Karl, Moller, Christopher, Morrison, Jane, Morse, Ernie, Moseley, Harvey, Mosier, Gary, Mountain, Matt, Mueckay, Julio, Mueller, Michael, Mullally, Susan, Murphy, Jess, Murray, Katherine, Murray, Claire, Mustelier, David, Muzerolle, James, Mycroft, Matthew, Myers, Richard, Myrick, Kaila, Nanavati, Shashvat, Nance, Elizabeth, Nayak, Omnarayani, Naylor, Bret, Nelan, Edmund, Nickson, Bryony, Nielson, Alethea, Nieto-Santisteban, Maria, Nikolov, Nikolay, Noriega-Crespo, Alberto, O'Shaughnessy, Brian, O'Sullivan, Brian, Ochs, William, Ogle, Patrick, Oleszczuk, Brenda, Olmsted, Joseph, Osborne, Shannon, Ottens, Richard, Owens, Beverly, Pacifici, Camilla, Pagan, Alyssa, Page, James, Park, Sang, Parrish, Keith, Patapis, Polychronis, Paul, Lee, Pauly, Tyler, Pavlovsky, Cheryl, Pedder, Andrew, Peek, Matthew, Pena-Guerrero, Maria, Pennanen, Konstantin, Perez, Yesenia, Perna, Michele, Perriello, Beth, Phillips, Kevin, Pietraszkiewicz, Martin, Pinaud, Jean-Paul, Pirzkal, Norbert, Pitman, Joseph, Piwowar, Aidan, Platais, Vera, Player, Danielle, Plesha, Rachel, Pollizi, Joe, Polster, Ethan, Pontoppidan, Klaus, Porterfield, Blair, Proffitt, Charles, Pueyo, Laurent, Pulliam, Christine, Quirt, Brian, Neira, Irma Quispe, Alarcon, Rafael Ramos, Ramsay, Leah, Rapp, Greg, Rapp, Robert, Rauscher, Bernard, Ravindranath, Swara, Rawle, Timothy, Regan, Michael, Reichard, Timothy A., Reis, Carl, Ressler, Michael E., Rest, Armin, Reynolds, Paul, Rhue, Timothy, Richon, Karen, Rickman, Emily, Ridgaway, Michael, Ritchie, Christine, Rix, Hans-Walter, Robberto, Massimo, Robinson, Gregory, Robinson, Michael, Robinson, Orion, Rock, Frank, Rodriguez, David, Del Pino, Bruno Rodriguez, Roellig, Thomas, Rohrbach, Scott, Roman, Anthony, Romelfanger, Fred, Rose, Perry, Roteliuk, Anthony, Roth, Marc, Rothwell, Braden, Rowlands, Neil, Roy, Arpita, Royer, Pierre, Royle, Patricia, Rui, Chunlei, Rumler, Peter, Runnels, Joel, Russ, Melissa, Rustamkulov, Zafar, Ryden, Grant, Ryer, Holly, Sabata, Modhumita, Sabatke, Derek, Sabbi, Elena, Samuelson, Bridget, Sapp, Benjamin, Sappington, Bradley, Sargent, B., Sauer, Arne, Scheithauer, Silvia, Schlawin, Everett, Schlitz, Joseph, Schmitz, Tyler, Schneider, Analyn, Schreiber, Jürgen, Schulze, Vonessa, Schwab, Ryan, Scott, John, Sembach, Kenneth, Shanahan, Clare, Shaughnessy, Bryan, Shaw, Richard, Shawger, Nanci, Shay, Christopher, Sheehan, Evan, Shen, Sharon, Sherman, Allan, Shiao, Bernard, Shih, Hsin-Yi, Shivaei, Irene, Sienkiewicz, Matthew, Sing, David, Sirianni, Marco, Sivaramakrishnan, Anand, Skipper, Joy, Sloan, Gregory, Slocum, Christine, Slowinski, Steven, Smith, Erin, Smith, Eric, Smith, Denise, Smith, Corbett, Snyder, Gregory, Soh, Warren, Sohn, Tony, Soto, Christian, Spencer, Richard, Stallcup, Scott, Stansberry, John, Starr, Carl, Starr, Elysia, Stewart, Alphonso, Stiavelli, Massimo, Straughn, Amber, Strickland, David, Stys, Jeff, Summers, Francis, Sun, Fengwu, Sunnquist, Ben, Swade, Daryl, Swam, Michael, Swaters, Robert, Swoish, Robby, Taylor, Joanna M., Taylor, Rolanda, Plate, Maurice Te, Tea, Mason, Teague, Kelly, Telfer, Randal, Temim, Tea, Thatte, Deepashri, Thompson, Christopher, Thompson, Linda, Thomson, Shaun, Tikkanen, Tuomo, Tippet, William, Todd, Connor, Toolan, Sharon, Tran, Hien, Trejo, Edwin, Truong, Justin, Tsukamoto, Chris, Tustain, Samuel, Tyra, Harrison, Ubeda, Leonardo, Underwood, Kelli, Uzzo, Michael, Van Campen, Julie, Vandal, Thomas, Vandenbussche, Bart, Vila, Begoña, Volk, Kevin, Wahlgren, Glenn, Waldman, Mark, Walker, Chanda, Wander, Michel, Warfield, Christine, Warner, Gerald, Wasiak, Matthew, Watkins, Mitchell, Weaver, Andrew, Weilert, Mark, Weiser, Nick, Weiss, Ben, Weissman, Sarah, Welty, Alan, West, Garrett, Wheate, Lauren, Wheatley, Elizabeth, Wheeler, Thomas, White, Rick, Whiteaker, Kevin, Whitehouse, Paul, Whiteleather, Jennifer, Whitman, William, Williams, Christina, Willmer, Christopher, Willoughby, Scott, Wilson, Andrew, Wirth, Gregory, Wislowski, Emily, Wolf, Erin, Wolfe, David, Wolff, Schuyler, Workman, Bill, Wright, Ray, Wu, Carl, Wu, Rai, Wymer, Kristen, Yates, Kayla, Yeager, Christopher, Yeates, Jared, Yerger, Ethan, Yoon, Jinmi, Young, Alice, Yu, Susan, Zak, Dean, Zeidler, Peter, Zhou, Julia, Zielinski, Thomas, Zincke, Cristian, and Zonak, Stephanie

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies., Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb293

Published
2022
Full Text
View/download PDF

11. Mapping a Plurality of Explanations with NLP: A Case Study of Mothers and Health Workers in India

Author

Friedman, Scott E, Schmer-Galunder, Sonja, Sarathy, Vasanth, Wheelock, Ruta, McLure, Matthew, Mosaphir, Drisana M, Goldman, Robert P., Benkler, Noam, Kantharaju, Pavan, Goldwater, Micah, and Legare, Cristine

Subjects
Anthropology, Artificial Intelligence, Linguistics, Causal reasoning, Knowledge representation
Abstract

Understanding the values, norms, behaviors, and causal beliefs of communities is a central goal of cognitive science, with practical benefits of grasping and improving community factors such as healthcare delivery. These cultural causal beliefs are evident, in part, within narratives, interview transcripts, ethnography, and other textual sources, but analyzing these texts presently involves tedious expert hand-coding or relatively shallow qualitative text analysis or classification. We present a novel approach for extracting graphical causal models from text via NLP, including qualitative causality, intentions, teleology, sentiment, welfare, social influence, and other rationale. The factors (i.e., nodes) of these causal models are tagged with ethnographic attributes and word-senses, allowing aggregation of causal models over thousands of passages to identify correlations and recurring themes. We apply this approach to a corpus of narrative interviews about maternal and child health and healthcare delivery in Bihar, India, corroborating the hand-coded results of human experts and also identifying novel insights about explanatory structure.

Published
2023

12. Substance and Behavioral Addiction in Family Law Cases.

Author

Friedman, Scott and Tilley, John L.

Subjects
Family law -- Practice -- Evaluation, Psychiatric counseling -- Laws, regulations and rules, Compulsive gambling -- Care and treatment -- Laws, regulations and rules, Addiction -- Care and treatment -- Laws, regulations and rules, Substance abuse -- Care and treatment, Government regulation
Abstract

Addiction is a condition demarcated by repetitive, compulsive behavior that results in either substantial distress to the person or significant impairment in some area of the persons i functioning, such [...]

Published
2024

13. Conditional deletion of CEACAM1 in hepatic stellate cells causes their activation

Author

Muturi, Harrison T., Ghadieh, Hilda E., Asalla, Suman, Lester, Sumona G., Belew, Getachew D., Zaidi, Sobia, Abdolahipour, Raziyeh, Shrestha, Abhishek P., Portuphy, Agnes O., Stankus, Hannah L., Helal, Raghd Abu, Verhulst, Stefaan, Duarte, Sergio, Zarrinpar, Ali, van Grunsven, Leo A., Friedman, Scott L., Schwabe, Robert F., Hinds, Terry D., Jr., Kumarasamy, Sivarajan, and Najjar, Sonia M.

Published
2024
Full Text
View/download PDF

14. From Unstructured Text to Causal Knowledge Graphs: A Transformer-Based Approach

Author

Friedman, Scott, Magnusson, Ian, Sarathy, Vasanth, and Schmer-Galunder, Sonja

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

Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express interactions between quantities (e.g., sleep decreases stress), between discrete events or entities (e.g., a protein inhibits another protein's transcription), or between intentional or functional factors (e.g., hospital patients pray to relieve their pain). Extracting and representing these diverse causal relations are critical for cognitive systems that operate in domains spanning from scientific discovery to social science. This paper presents a transformer-based NLP architecture that jointly extracts knowledge graphs including (1) variables or factors described in language, (2) qualitative causal relationships over these variables, (3) qualifiers and magnitudes that constrain these causal relationships, and (4) word senses to localize each extracted node within a large ontology. We do not claim that our transformer-based architecture is itself a cognitive system; however, we provide evidence of its accurate knowledge graph extraction in real-world domains and the practicality of its resulting knowledge graphs for cognitive systems that perform graph-based reasoning. We demonstrate this approach and include promising results in two use cases, processing textual inputs from academic publications, news articles, and social media., Comment: arXiv admin note: substantial text overlap with arXiv:2108.13304

Published
2022

17. Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results

Author

Magnusson, Ian H. and Friedman, Scott E.

Subjects
Computer Science - Computation and Language
Abstract

Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus. By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence. We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond., Comment: 8 pages, 4 tables, 4 figures, accepted at EMNLP 2021

Published
2021

19. Extracting Qualitative Causal Structure with Transformer-Based NLP

Author

Friedman, Scott E., Magnusson, Ian H., and Schmer-Galunder, Sonja M.

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract

Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express interactions between quantities (e.g., sleep decreases stress), between discrete events or entities (e.g., a protein inhibits another protein's transcription), or between intentional or functional factors (e.g., hospital patients pray to relieve their pain). This paper presents a transformer-based NLP architecture that jointly identifies and extracts (1) variables or factors described in language, (2) qualitative causal relationships over these variables, and (3) qualifiers and magnitudes that constrain these causal relationships. We demonstrate this approach and include promising results from in two use cases, processing textual inputs from academic publications, news articles, and social media., Comment: 7 pages, 7 figures, IJCAI Workshop on Qualitative Reasoning

Published
2021

22. Intravitreal aflibercept 8 mg in diabetic macular oedema (PHOTON): 48-week results from a randomised, double-masked, non-inferiority, phase 2/3 trial

Author

Abraham, Prema, Aderman, Christopher, Akiyama, Kunihiko, Alfaro, Daniel V., Ali, Fareed A., Amini, Payam, Anzalotta, Andres Emanuelli, Bátor, György, Batlle, Ivan, Berger, Adam, Bhandari, Ramanath, Bridges, William, Brinkmann, Christian, Brown, Jamin, Burgess, Stuart, Calzada, Jorge, Capone Jr., Antonio, Cervena, Dana, Charles, Steven, Chaudhry, Nauman, Chow, David, Clark, W. Lloyd, Conrad III, Paul, Cunningham, Matthew, Dadgostar, Hajir, Dessouki, Amr, Deupree, Dana, Devine, Christopher, Eichenbaum, David, Ernest, Jan, Feltgen, Nicolas, Fenberg, Moss, Ferrone, Philip, Frenkel, Ronald, Friedman, Scott, Gasperini, Julie, Gerstenblith, Adam, Ghorayeb, Ghassan, Giunta, Michel, Goff, Mitchell, Golas, Liliya, Googe Jr., Joseph M., Goren Fein, Jordana, Hagedorn, Curtis, Hagiwara, Akira, Hahn, Paul, Hairston, Richard, Handza, Jason, Hau, Vivienne, Hayashi, Ken, Heier, Jeffrey, Hershberger, Vrinda, Higgins, Patrick, Hirano, Yoshio, Honda, Shigeru, Ikegami, Yasuko, Ishida, Yuichiro, Ishikawa, Isao, Ishii, Kiyoshi, Jablon, Eric P., Jain, Atul, Kaji, Yuichi, Kapoor, Kapil, Kerényi, Ágnes, Kimura, Kazuhiro, Kishino, Genichiro, Kiss, Katalin, Kitaoka, Takashi, Klancnik, James M., Kobayashi, Namie, Kogo, Jiro, Korda, Vladimir, Kruger, Erik, Kusuhara, Sentaro, Lara, Wilfredo, Laud, Ketan, Lee, Seong, Luu, James, Marcus, Dennis, Mein, Calvin, Meleth, Annal, Milibák, Tibor, Mitamura, Yoshinori, Murata, Toshinori, Noge, Sumiyo, Onoe, Hajime, Osher, James, Papp, András, Parschauer, Justin, Patel, Sugat, Patel, Sunil, Pezda, Matthew, Pirouz, Ashkan, Prasad, Pradeep, Punjabi, Omar, Rao, Llewelyn, Roe, Richard, Schadlu, Ramin, Schneider, Eric, Shah, Ankur, Shah, Milan, Shah, Sandeep, Shah, Sumit, Sharma, Ashish, Sheth, Veeral, Shimura, Masahiko, Singerman, Lawrence, Spital, Georg, Stoltz, Robert, Suan, Eric, Suzuma, Kiyoshi, Takahashi, Hidenori, Takamura, Yoshihiro, Takeuchi, Masaru, Tan, Jeffrey, Thomas, Benjamin, Tóth,-Molnár, Edit, Ueda, Tetsuo, Ushida, Hiroaki, Vajas, Attila, Varma, Deepali, Varsányi, Balázs, Veith, Miroslav, Weber, Pamela, Wee, Raymond, Williams, Geoff, Yamada, Haruhiko, Yonekawa, Yoshihiro, Yoshida, Shigeo, Brown, David M, Boyer, David S, Do, Diana V, Wykoff, Charles C, Sakamoto, Taiji, Win, Peter, Joshi, Sunir, Salehi-Had, Hani, Seres, András, Berliner, Alyson J, Leal, Sergio, Vitti, Robert, Chu, Karen W, Reed, Kimberly, Rao, Rohini, Cheng, Yenchieh, Sun, Wei, Voronca, Delia, Bhore, Rafia, Schmidt-Ott, Ursula, Schmelter, Thomas, Schulze, Andrea, Zhang, Xin, Hirshberg, Boaz, Yancopoulos, George D, and Sivaprasad, Sobha

Published
2024
Full Text
View/download PDF

27. Provenance-Based Interpretation of Multi-Agent Information Analysis

Author

Friedman, Scott, Rye, Jeff, LaVergne, David, Thomsen, Dan, Allen, Matthew, and Tunis, Kyle

Subjects
Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' appraisals and evidence links (expressed in our novel DIVE ontology). Together, PROV-O and DIVE enable dynamic propagation of confidence and counter-factual refutation to improve human-machine trust and analytic integrity. We demonstrate representative software developed for user interaction with that provenance, and discuss key needs for organizations adopting such approaches. We demonstrate all of these assessments in a multi-agent analysis scenario, using an interactive web-based information validation UI., Comment: 6 pages, 5 figures, appears in Proceedings of TaPP 2020

Published
2020

28. Provenance-Based Assessment of Plans in Context

Author

Friedman, Scott E., Goldman, Robert P., Freedman, Richard G., Kuter, Ugur, Geib, Christopher, and Rye, Jeffrey

Subjects
Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

Many real-world planning domains involve diverse information sources, external entities, and variable-reliability agents, all of which may impact the confidence, risk, and sensitivity of plans. Humans reviewing a plan may lack context about these factors; however, this information is available during the domain generation, which means it can also be interwoven into the planner and its resulting plans. This paper presents a provenance-based approach to explaining automated plans. Our approach (1) extends the SHOP3 HTN planner to generate dependency information, (2) transforms the dependency information into an established PROV-O representation, and (3) uses graph propagation and TMS-inspired algorithms to support dynamic and counter-factual assessment of information flow, confidence, and support. We qualified our approach's explanatory scope with respect to explanation targets from the automated planning literature and the information analysis literature, and we demonstrate its ability to assess a plan's pertinence, sensitivity, risk, assumption support, diversity, and relative confidence., Comment: 9 pages, 7 figures, including in Proceedings of the 2020 ICAPS Workshop on Explainable AI Planning (XAIP)

Published
2020

29. Mechanisms and disease consequences of nonalcoholic fatty liver disease

Author

Loomba, Rohit, Friedman, Scott L, and Shulman, Gerald I

Subjects
Hepatitis, Liver Disease, Rare Diseases, Liver Cancer, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Cancer, 2.1 Biological and endogenous factors, Aetiology, Oral and gastrointestinal, Good Health and Well Being, Carcinoma, Hepatocellular, Humans, Liver, Liver Cirrhosis, Liver Neoplasms, Non-alcoholic Fatty Liver Disease, fibrosis, insulin resistance, lipotoxicity, liver cancer, metabolism-associated fatty liver disease, nonalcoholic steatohepatitis, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Nonalcoholic fatty liver disease (NAFLD) is the leading chronic liver disease worldwide. Its more advanced subtype, nonalcoholic steatohepatitis (NASH), connotes progressive liver injury that can lead to cirrhosis and hepatocellular carcinoma. Here we provide an in-depth discussion of the underlying pathogenetic mechanisms that lead to progressive liver injury, including the metabolic origins of NAFLD, the effect of NAFLD on hepatic glucose and lipid metabolism, bile acid toxicity, macrophage dysfunction, and hepatic stellate cell activation, and consider the role of genetic, epigenetic, and environmental factors that promote fibrosis progression and risk of hepatocellular carcinoma in NASH.

Published
2021

31. Toward Transformer-Based NLP for Extracting Psychosocial Indicators of Moral Disengagement

Author

Friedman, Scott E, Magnusson, Ian, Schmer-Galunder, Sonja, Wheelock, Ruta, Gottlieb, Jeremy, patel, pooja, and Miller, Christopher

Subjects
cognitive science
Abstract

Moral disengagement is a mechanism whereby people distance or disconnect their actions from their moral evaluation. This work presents a novel knowledge graph schema, dataset, and transformer-based NLP model to identify and represent indicators of moral disengagement in text. Our graph schema is informed by Albert Bandura’s psychosocial mechanisms of moral disengagement, including dehumanization, victimization, moral condemnation and justification, and attribution (or displacement) of responsibility. Our preliminary dataset is comprised of online posts from five different communities. We present initial evidence that (1) our theory-based schema can represent moral disengagement indicators across these communities and (2) our transformer-based NLP model can identify indicators of moral disengagement in text. As it matures, this thread of computational social science research can help us understand the spread of morally-disengaged language and its effect on online communities.

Published
2021

34. Gender Gaps Correlate with Gender Bias in Social Media Word Embeddings

Author

Friedman, Scott, Schmer-Galunder, Sonja, Chen, Anthony, Goldman, Robert, and Ausman, Michelle

Subjects
gender bias, gender gaps, word embeddings, NLP
Abstract

Gender status, gender roles, and gender values vary widelyacross cultures. Anthropology has provided qualitative ac-counts of economic, cultural, and biological factors that im-pact social groups, and international organizations have gath-ered indices and surveys to help quantify gender inequalitiesin states. Concurrently, machine learning research has recentlycharacterized pervasive gender biases in AI language models,rooting from biases in their textual training data. While thesemachine biases produce sub-optimal inferences, they may helpus characterize and predict statistical gender gaps and gendervalues in the culture(s) that produced the training text, therebyhelping us understand cultural context through big data. Thispaper presents an approach to (1) construct word embeddings(i.e., vector-based lexical semantics) from a region’s social me-dia, (2) quantify gender bias in word embeddings, and (3)correlate biases with survey responses and statistical gendergaps in education, politics, economics, and health. We validatethis approach using 2018 Twitter data spanning 143 countriesand 51 U.S. territories, 23 international and 7 U.S. gender gapstatistics, and seven international survey results from the WorldValue Survey. Integrating these heterogeneous data across cul-tures is an important step toward understanding (1) how biasesin culture might manifest in machine learning models and (2)how to estimate gender inequality from big data.

Published
2020

37. Cultural Value Resonance in Folktales: A Transformer-Based Analysis with the World Value Corpus

Author

Benkler, Noam, Friedman, Scott, Schmer-Galunder, Sonja, Mosaphir, Drisana, Sarathy, Vasanth, Kantharaju, Pavan, McLure, Matthew D., Goldman, Robert P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Thomson, Robert, editor, Dancy, Christopher, editor, and Pyke, Aryn, editor

Published
2022
Full Text
View/download PDF

39. Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

Author

Krishnaswamy, Nikhil, Friedman, Scott, and Pustejovsky, James

Subjects
Computer Science - Artificial Intelligence
Abstract

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples--sometimes only one--from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants' ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.

Published
2018

40. Hepatic Autophagy Deficiency Compromises Farnesoid X Receptor Functionality and Causes Cholestatic Injury.

Author

Khambu, Bilon, Li, Tiangang, Yan, Shengmin, Yu, Changshun, Chen, Xiaoyun, Goheen, Michael, Li, Yong, Lin, Jingmei, Cummings, Oscar, Lee, Youngmin, Friedman, Scott, Dong, Zheng, Feng, Gen-Sheng, Wu, Shangwei, and Yin, Xiao-Ming

Subjects
Animals, Autophagy, Bile Acids and Salts, Cholestasis, Intrahepatic, Female, Food Deprivation, Liver, Male, Mice, Transgenic, NF-E2-Related Factor 2, Receptors, Cytoplasmic and Nuclear
Abstract

Autophagy is important for hepatic homeostasis, nutrient regeneration, and organelle quality control. We investigated the mechanisms by which liver injury occurred in the absence of autophagy function. We found that mice deficient in autophagy because of the lack of autophagy-related gene 7 or autophagy-related gene 5, key autophagy-related genes, manifested intracellular cholestasis with increased levels of serum bile acids, a higher ratio of tauromuricholic acid/taurocholic acid in the bile, increased hepatic bile acid load, abnormal bile canaliculi, and altered expression of hepatic transporters. In determining the underlying mechanism, we found that autophagy sustained and promoted the basal and up-regulated expression of farnesoid X receptor (Fxr) in the fed and starved conditions, respectively. Consequently, expression of Fxr and its downstream genes, particularly bile salt export pump, and the binding of FXR to the promoter regions of these genes, were suppressed in autophagy-deficient livers. In addition, codeletion of nuclear factor erythroid 2-related factor 2 (Nrf2) in autophagy deficiency status reversed the FXR suppression. Furthermore, the cholestatic injury of autophagy-deficient livers was reversed by enhancement of FXR activity or expression, or by Nrf2 deletion. Conclusion: Together with earlier reports that FXR can suppress autophagy, our findings indicate that autophagy and FXR form a regulatory loop and deficiency of autophagy causes abnormal FXR functionality, leading to the development of intracellular cholestasis and liver injury.

Published
2019

41. Simulating Explanatory Coexistence:Integrated, Synthetic, and Target-Dependent Reasoning

Author

Friedman, Scott E. and Goldwater, Micah B.

Subjects
cognitive modeling, explanatory coexistence, AI, abductive reasoning, explanation
Abstract

Understanding the cognitive structure of explanations— andthe cognitive processes that assemble them— is a milestonefor understanding how people learn and communicate. Re-cent research on explanatory coexistence suggests that peo-ple’s causal beliefs are less globally coherent than previouslythought: people use seemingly-competing supernatural and bi-ological causes to explain different aspects of the same phe-nomenon, or they assemble supernatural and biological causesinto single, coherent explanations (Legare & Gelman, 2008;Legare & Shtulman, 2018; Shtulman & Lombrozo, 2016).This coexistence— and unexpected coherence— of diversecausal mechanisms poses interesting questions about the roleof coherence and fragmentation in people’s mental models andexplanations. This paper presents a computational model ofexplanatory coherence in the well-characterized domain of dis-ease transmission, extending a previous cognitive model ofexplanation-based conceptual change (Friedman, Forbus, &Sherin, 2018). Our approach (1) retrieves diverse causal modelfragments based on the phenomenon to explain, (2) assem-bles coherent causal models using relevance-directed abduc-tive reasoning, and (3) selects explanatory paths that supportwithin-explanation and within-scenario coherence. Our modelsimulates the three different types of explanatory coexistencedetailed in the literature.

Published
2019

43. The Behavior of Selected Diffuse Interstellar Bands with Molecular Fraction in Diffuse Atomic and Molecular Clouds

Author

Fan, Haoyu, Welty, Daniel E., York, Donald G., Sonnentrucker, Paule, Dahlstrom, Julie A., Baskes, Noah, Friedman, Scott D., Hobbs, Lewis M., Jiang, Zihao, Rachford, Brian, Snow, Theodore P., Sherman, Reid, and Zhao, Gang

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We study the behavior of eight diffuse interstellar bands (DIBs) in different interstellar environments, as characterized by the fraction of hydrogen in molecular form [$f$(H$_2$)], with comparisons to the corresponding behavior of various known atomic and molecular species. The equivalent widths of the five "normal" DIBs ($\lambda\lambda$5780.5, 5797.1, 6196.0, 6283.8, and 6613.6), normalized to $E(B-V)$, show a "Lambda-shaped" behavior: they increase at low $f$(H$_2$), peak at $f$(H$_2$) ~ 0.3, and then decrease. The similarly normalized column densities of Ca, Ca$^+$, Ti$^+$, and CH$^+$ also decline for $f$(H$_2$) > 0.3. In contrast, the normalized column densities of Na, K, CH, CN, and CO increase monotonically with $f$(H$_2$), and the trends exhibited by the three C$_2$ DIBs ($\lambda\lambda$4726.8, 4963.9, and 4984.8) lie between those two general behaviors. These trends with $f$(H$_2$) are accompanied by cosmic scatter, the dispersion at any given $f$(H$_2$) being significantly larger than the individual errors of measurement. The Lambda-shaped trends suggest the balance between creation and destruction of the DIB carriers differs dramatically between diffuse atomic and diffuse molecular clouds; additional processes besides ionization and shielding are needed to explain those observed trends. Except for several special cases, the highest $W$(5780)/$W$(5797) ratios, characterizing the so-called "sigma-zeta effect", occur only at $f$(H$_2$) < 0.2. We propose a sequence of DIBs based on trends in their pair-wise strength ratios with increasing $f$(H$_2$). In order of increasing environmental density, we find the $\lambda$6283.8 and $\lambda$5780.5 DIBs, the $\lambda$6196.0 DIB, the $\lambda$6613.6 DIB, the $\lambda$5797.1 DIB, and the C$_2$ DIBs., Comment: accepted to ApJ; 53 pages; 12 figures; 3 tables

Published
2017
Full Text
View/download PDF

44. Geographic Atrophy Natural History Versus Treatment: Time to Fovea.

Author

Zhang, Casey, Kahan, Elias, Begaj, Tedi, Friedman, Scott M., Deobhakta, Avnish, Heyang, Michael, Shen, Liangbo Linus, Moshfeghi, Darius, Wai, Karen, and Parikh, Ravi

Subjects
ATROPHY, THERAPEUTICS, RETINAL diseases, AGE factors in retinal degeneration
Abstract

Background and Objective: The Food and Drug Administration recently approved treatments of geographic atrophy (GA). Our study aims to quantify the time for a lesion to reach the central fovea based on reduction of GA growth rates from therapeutics compared to the natural history. Patients and Methods: A previously published study calculates local border expansion rate of GA lesions at varying retinal eccentricities. In this study, we use these rates to model GA expansion toward the fovea and the effects of treatments that reduce growth in GA area by 15% to 45% on lesions of varying sizes with posterior margin 250, 500, 750, 1000, 1250, 1500, and 3000 µm from the fovea. Results: Lesions with an area 8 mm2 and posterior edge 500 µm from the fovea will reach the fovea in 5.08 years with no treatment, but the same lesions will reach the fovea in 5.85, 6.52, 7.36, and 8.46 years with a treatment that reduces growth in GA area by 15%, 25%, 35%, and 45%, respectively. Conclusions: Distance of the posterior edge of the lesion was the primary factor in GA growth toward the fovea, and lesion size only minimally affects growth rates of GA. Based on the efficacy of current and future therapeutics and distance of GA to the fovea, our study provides the marginal time benefit of treatment to guide patients and clinicians, placing both the natural history of GA and the effects of current and future treatments into clinical context. [Ophthalmic Surg Lasers Imaging Retina 2024;55:576–585.] [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

45. Kisspeptin Alleviates Human Hepatic Fibrogenesis by Inhibiting TGFβ Signaling in Hepatic Stellate Cells.

Author

Prasad, Kavita, Bhattacharya, Dipankar, Shams, Shams Gamal Eldin, Izarraras, Kimberly, Hart, Tia, Mayfield, Brent, Blaszczyk, Maryjka B., Zhou, Zhongren, Pajvani, Utpal B., Friedman, Scott L., and Bhattacharya, Moshmi

Subjects
TRANSFORMING growth factors, PEPTIDE hormones, LIVER cells, PHOSPHOPROTEIN phosphatases, KISSPEPTINS
Abstract

The peptide hormone kisspeptin attenuates liver steatosis, metabolic dysfunction-associated steatohepatitis (MASH), and fibrosis in mouse models by signaling via the kisspeptin 1 receptor (KISS1R). However, whether kisspeptin impacts fibrogenesis in the human liver is not known. We investigated the impact of a potent kisspeptin analog (KPA) on fibrogenesis using human precision-cut liver slices (hPCLS) from fibrotic livers from male patients, in human hepatic stellate cells (HSCs), LX-2, and in primary mouse HSCs. In hPCLS, 48 h and 72 h of KPA (3 nM, 100 nM) treatment decreased collagen secretion and lowered the expression of fibrogenic and inflammatory markers. Immunohistochemical studies revealed that KISS1R is expressed and localized to HSCs in MASH/fibrotic livers. In HSCs, KPA treatment reduced transforming growth factor b (TGFβ)-the induced expression of fibrogenic and inflammatory markers, in addition to decreasing TGFβ-induced collagen secretion, cell migration, proliferation, and colony formation. Mechanistically, KISS1R signaling downregulated TGFβ signaling by decreasing SMAD2/3 phosphorylation via the activation of protein phosphatases, PP2A, which dephosphorylates SMAD 2/3. This study revealed for the first time that kisspeptin reverses human hepatic fibrogenesis, thus identifying it as a new therapeutic target to treat hepatic fibrosis. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

47. Diagnostic modalities for nonalcoholic fatty liver disease, nonalcoholic steatohepatitis, and associated fibrosis

Author

Younossi, Zobair M, Loomba, Rohit, Anstee, Quentin M, Rinella, Mary E, Bugianesi, Elisabetta, Marchesini, Giulio, Neuschwander‐Tetri, Brent A, Serfaty, Lawrence, Negro, Francesco, Caldwell, Stephen H, Ratziu, Vlad, Corey, Kathleen E, Friedman, Scott L, Abdelmalek, Manal F, Harrison, Stephen A, Sanyal, Arun J, Lavine, Joel E, Mathurin, Philippe, Charlton, Michael R, Goodman, Zachary D, Chalasani, Naga P, Kowdley, Kris V, George, Jacob, and Lindor, Keith

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Liver Disease, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Clinical Research, Hepatitis, Oral and gastrointestinal, Good Health and Well Being, Biomarkers, Collagen, Humans, Liver, Liver Cirrhosis, Non-alcoholic Fatty Liver Disease, Medical Biochemistry and Metabolomics, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

Nonalcoholic fatty liver disease (NAFLD) is a spectrum comprised of isolated steatosis, nonalcoholic steatohepatitis (NASH), advanced fibrosis, and cirrhosis. The majority of NAFLD subjects do not have NASH and do not carry a significant risk for liver-related adverse outcomes (cirrhosis and mortality). Globally, the prevalence of NAFLD is approximately 25%. In Asia, a gradient of high to low prevalence rates is noted from urban to rural areas. Given the prevalence of NAFLD, the clinical and economic burden of NAFLD and NASH can be substantial. With increasing recognition of NASH as an important liver disease, the diagnosis of NASH still requires a liver biopsy that is suboptimal. Although liver biopsy is the most accurate modality to diagnose and stage the severity of NASH, this method suffers from being invasive, costly, associated with potential complications, and plagued with interobserver variability of individual pathological features. A number of noninvasive modalities to diagnose NASH and stage liver fibrosis are being developed. These modalities include predictive models (NAFLD fibrosis score) and serum biomarkers such as enhanced liver fibrosis (ELF). Other tests are based on radiological techniques, such as transient elastography (TE) or magnetic resonance elastography (MRE), which are used to estimate liver stiffness as a potential surrogate of hepatic fibrosis. Although a dynamic field of research, most of these diagnostic modalities have area under the curve ranging between 0.76 and 0.90%, with MRE having the best predictive performance. In summary, developing safe and easily accessible noninvasive modalities to accurately diagnose and monitor NASH and associated fibrosis is of utmost importance in clinical practice and clinical research. These tests are not only important to risk stratify subjects at the greatest risk for progressive liver disease, but also to serve as appropriate surrogate endpoints for therapeutic clinical trials of NASH. (Hepatology 2018;68:349-360).

Published
2018

48. Current and future therapeutic regimens for nonalcoholic fatty liver disease and nonalcoholic steatohepatitis

Author

Younossi, Zobair M, Loomba, Rohit, Rinella, Mary E, Bugianesi, Elisabetta, Marchesini, Giulio, Neuschwander‐Tetri, Brent A, Serfaty, Lawrence, Negro, Francesco, Caldwell, Stephen H, Ratziu, Vlad, Corey, Kathleen E, Friedman, Scott L, Abdelmalek, Manal F, Harrison, Stephen A, Sanyal, Arun J, Lavine, Joel E, Mathurin, Philippe, Charlton, Michael R, Chalasani, Naga P, Anstee, Quentin M, Kowdley, Kris V, George, Jacob, Goodman, Zachary D, and Lindor, Keith

Subjects
Hepatitis, Liver Disease, Chronic Liver Disease and Cirrhosis, Prevention, Clinical Trials and Supportive Activities, Clinical Research, Digestive Diseases, Rare Diseases, Oral and gastrointestinal, Good Health and Well Being, Clinical Trials as Topic, Exercise, Humans, Liver Transplantation, Non-alcoholic Fatty Liver Disease, Obesity, Weight Loss, Medical Biochemistry and Metabolomics, Clinical Sciences, Immunology, Gastroenterology & Hepatology
Abstract

Nonalcoholic fatty liver disease (NAFLD) and its progressive form non-alcoholic steatohepatitis (NASH), are rapidly becoming among the top causes of cirrhosis, hepatocellular carcinoma, and indications for liver transplantation. Other than lifestyle modification through diet and exercise, there are currently no other approved treatments for NASH/NAFLD. Although weight loss can be effective, it is difficult to achieve and sustain. In contrast, bariatric surgery can improve metabolic conditions associated with NAFLD, and has been shown to improve liver histology. To have approved regimens for the treatment of NASH/NAFLD, several issues must be addressed. First, all stakeholders must agree on the most appropriate clinical trial endpoints for NASH. Currently, resolution of NASH (without worsening fibrosis) or reduction of fibrosis stage (without worsening NASH) are the accepted endpoints by the regulatory authorities. It is important to recognize the prognostic implication of histologic features of NASH. In this context, although histologic NASH has been associated with advanced fibrosis, it is not an independent predictor of long-term mortality. In contrast, there are significant data to suggest that fibrosis stage is the only robust and independent predictor of liver-related mortality. In addition to the primary endpoints, several important secondary endpoints, including noninvasive biomarkers, long-term outcomes, and patient-reported outcomes must be considered. In 2018, a few phase 3 clinical trials for the treatment of NASH have been initiated. Additionally, a number of phase 2a and 2b clinical trials targeting different pathogenic pathways in NASH are in the pipeline of emerging therapies.ConclusionOver the next 5 years, some of these regimens are expected to provide potential new treatment options for patients with NASH/NAFLD. (Hepatology 2018;68:361-371).

Published
2018

49. A randomized, placebo‐controlled trial of cenicriviroc for treatment of nonalcoholic steatohepatitis with fibrosis

Author

Friedman, Scott L, Ratziu, Vlad, Harrison, Stephen A, Abdelmalek, Manal F, Aithal, Guruprasad P, Caballeria, Juan, Francque, Sven, Farrell, Geoffrey, Kowdley, Kris V, Craxi, Antonio, Simon, Krzysztof, Fischer, Laurent, Melchor‐Khan, Liza, Vest, Jeffrey, Wiens, Brian L, Vig, Pamela, Seyedkazemi, Star, Goodman, Zachary, Wong, Vincent Wai‐Sun, Loomba, Rohit, Tacke, Frank, Sanyal, Arun, and Lefebvre, Eric

Subjects
Hepatitis, Patient Safety, Clinical Research, Chronic Liver Disease and Cirrhosis, Liver Disease, Clinical Trials and Supportive Activities, Digestive Diseases, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Oral and gastrointestinal, Adult, Aged, Biomarkers, CCR5 Receptor Antagonists, Double-Blind Method, Female, Humans, Imidazoles, Liver, Liver Cirrhosis, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Sulfoxides, Treatment Outcome, Medical Biochemistry and Metabolomics, Clinical Sciences, Immunology, Gastroenterology & Hepatology
Abstract

The aim of this study was to evaluate cenicriviroc (CVC), a dual antagonist of CC chemokine receptor types 2 and 5, for treatment of nonalcoholic steatohepatitis (NASH) with liver fibrosis (LF). A randomized, double-blind, multinational phase 2b study enrolled subjects with NASH, a nonalcoholic fatty liver disease activity score (NAS) ≥4, and LF (stages 1-3, NASH Clinical Research Network) at 81 clinical sites. Subjects (N = 289) were randomly assigned CVC 150 mg or placebo. Primary outcome was ≥2-point improvement in NAS and no worsening of fibrosis at year 1. Key secondary outcomes were: resolution of steatohepatitis (SH) and no worsening of fibrosis; improvement in fibrosis by ≥1 stage and no worsening of SH. Biomarkers of inflammation and adverse events were assessed. Full study recruitment was achieved. The primary endpoint of NAS improvement in the intent-to-treat population and resolution of SH was achieved in a similar proportion of subjects on CVC (N = 145) and placebo (N = 144; 16% vs. 19%, P = 0.52 and 8% vs. 6%, P = 0.49, respectively). However, the fibrosis endpoint was met in significantly more subjects on CVC than placebo (20% vs. 10%; P = 0.02). Treatment benefits were greater in those with higher disease activity and fibrosis stage at baseline. Biomarkers of systemic inflammation were reduced with CVC. Safety and tolerability of CVC were comparable to placebo.ConclusionAfter 1 year of CVC treatment, twice as many subjects achieved improvement in fibrosis and no worsening of SH compared with placebo. Given the urgent need to develop antifibrotic therapies in NASH, these findings warrant phase 3 evaluation. (Hepatology 2018;67:1754-1767).

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results