Search

Your search keyword '"KIM, JOSEPH"' showing total 3,260 results
Start Over Author "KIM, JOSEPH"
3,260 results on '"KIM, JOSEPH"'

2. Scalable Normalizing Flows Enable Boltzmann Generators for Macromolecules

Author

Kim, Joseph C., Bloore, David, Kapoor, Karan, Feng, Jun, Hao, Ming-Hong, and Wang, Mengdi

Subjects
Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract

The Boltzmann distribution of a protein provides a roadmap to all of its functional states. Normalizing flows are a promising tool for modeling this distribution, but current methods are intractable for typical pharmacological targets; they become computationally intractable due to the size of the system, heterogeneity of intra-molecular potential energy, and long-range interactions. To remedy these issues, we present a novel flow architecture that utilizes split channels and gated attention to efficiently learn the conformational distribution of proteins defined by internal coordinates. We show that by utilizing a 2-Wasserstein loss, one can smooth the transition from maximum likelihood training to energy-based training, enabling the training of Boltzmann Generators for macromolecules. We evaluate our model and training strategy on villin headpiece HP35(nle-nle), a 35-residue subdomain, and protein G, a 56-residue protein. We demonstrate that standard architectures and training strategies, such as maximum likelihood alone, fail while our novel architecture and multi-stage training strategy are able to model the conformational distributions of protein G and HP35.

Published
2024

3. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

4. Phase I Safety and Feasibility Pilot of Hepatic Artery Infusion Chemotherapy in a Rural Catchment Area Using The Codman Vascular Catheter with The Medtronic SynchroMed II Pump for Intrahepatic Cancers

Author

McDonald, Hannah G., Zaki, Omar A., Wright, Matt J., Jayswal, Rani, Weiss, Heidi, Nair, Rashmi T., Ganesh, Halemane, Ellis, Scott, Kolesar, Jill M., Moss, Jessica, Barry-Hundeyin, Mautin, Pandalai, Prakash K., Kim, Joseph, Patel, Reema A., and Cavnar, Michael J.

Published
2024
Full Text
View/download PDF

5. Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS

Author

Sharma, Prashin, Kraske, Benjamin, Kim, Joseph, Laouar, Zakariya, Sunberg, Zachary, and Atkins, Ella

Subjects
Computer Science - Robotics
Abstract

Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model.

Published
2023

6. Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

Author

Yu, Zheng, Li, Yikuan, Kim, Joseph, Huang, Kaixuan, Luo, Yuan, and Wang, Mengdi

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalanced; therefore, we aim to maximize the $F_1$ score instead of the error rate. However, optimizing the non-concave $F_1$ score is not a classic RL problem, thus invalidates standard RL methods. To remedy this issue, we develop a reward shaping approach, leveraging properties of the $F_1$ score and duality of policy optimization, to provably find the set of all Pareto-optimal policies for budget-constrained $F_1$ score maximization. To handle the combinatorially complex state space, we propose a Semi-Model-based Deep Diagnosis Policy Optimization (SM-DDPO) framework that is compatible with end-to-end training and online learning. SM-DDPO is tested on diverse clinical tasks: ferritin abnormality detection, sepsis mortality prediction, and acute kidney injury diagnosis. Experiments with real-world data validate that SM-DDPO trains efficiently and identifies all Pareto-front solutions. Across all tasks, SM-DDPO is able to achieve state-of-the-art diagnosis accuracy (in some cases higher than conventional methods) with up to $85\%$ reduction in testing cost. The code is available at [https://github.com/Zheng321/Deep-Reinforcement-Learning-for-Cost-Effective-Medical-Diagnosis]., Comment: Accepted to ICRL 2023

Published
2023

7. Remote Identification Trajectory Coverage in Urban Air Mobility Applications

Author

Huang, Hejun, Mazotti, Billy, Kim, Joseph, and Li, Max Z.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

As Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) continue to mature, a safety-critical system that will need to be implemented in tandem is Remote Identification (Remote ID) for uncrewed aircraft systems (UAS). To ensure successful and efficient deployment (e.g., maximal surveillance of UAS trajectories), as well as to better understand secondary impacts (e.g., consumer privacy risks in collecting real-time UAS trajectory information), the coverage of broadcast-receive Remote ID architectures needs to be characterized. Motivated by this need, we examine theoretical and empirical trajectory coverage of several common Remote ID technologies (e.g., Bluetooth, Wi-Fi) deployed for urban package delivery missions, a commonly-cited use case for UAM and AAM. We derive methods to explicitly compute expected coverage proportions under idealized geometries, as well as conduct case studies with realistic city geographies and UAS path planning algorithms. An example of results include approximate magnitudes of Remote ID receivers needed (approximately 500-5000 receivers needed to achieve 50-95\% coverage for Bluetooth Legacy, and approximately 10-40 receivers needed for the same coverage range for Wi-Fi NAN/Beacon, assuming a cruise altitude of 200 feet) to achieve specific trajectory coverage proportions for San Francisco, California. Our analyses, combined with complementary works related to Remote ID bandwidth and deployment topologies, can help guide municipal authorities and AAM stakeholders in future Remote ID system deployments and upkeep., Comment: 10 pages, 10 figures, conference paper for 2023-ATM-Seminar

Published
2023

8. Targeting isoforms of RON kinase (MST1R) drives antitumor efficacy

Author

Kim, Joseph, Koh, Dong-In, Lee, Minki, Park, Yoon Sun, Hong, Seung-Woo, Shin, Jae-Sik, Lee, Mi So, Kim, Min-Hwa, Lee, Jun Hyung, Jeong, Joonyee, Bae, Seunggeon, Hong, Jun Ki, Jeong, Hong-Rae, Ryu, Yea Seong, Kim, Seung-Mi, Choi, Mingee, Kim, Hyojin, Ryu, Hyun, Hur, Sun-Chul, Park, Junho, Hur, Dae Young, and Jin, Dong-Hoon

Published
2023
Full Text
View/download PDF

10. PHEE: A Dataset for Pharmacovigilance Event Extraction from Text

Author

Sun, Zhaoyue, Li, Jiazheng, Pergola, Gabriele, Wallace, Byron C., John, Bino, Greene, Nigel, Kim, Joseph, and He, Yulan

Subjects
Computer Science - Computation and Language
Abstract

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resources for developing natural language models for this task are scant. We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date. We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects. Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area., Comment: 17 pages, 3 figures, EMNLP2022 accepted

Published
2022

11. ASO Visual Abstract: Phase I Safety and Feasibility Pilot of Hepatic Artery Infusion Chemotherapy in a Rural Catchment Area Using the Codman Vascular Catheter with the Medtronic Synchromed II Pump for Intrahepatic Cancers

Author

McDonald, Hannah G., Zaki, Omar A., Wright, Matt J., Jayswal, Rani, Weiss, Heidi, Nair, Rashmi T., Ganesh, Halemane, Ellis, Scott, Kolesar, Jill M., Moss, Jessica, Barry-Hundeyin, Mautin, Pandalai, Prakash K., Kim, Joseph, Patel, Reema A., and Cavnar, Michael J.

Published
2024
Full Text
View/download PDF

14. Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation

Author

Kim, Ginam, Kim, Hyunsung, Kim, Joseph Kihoon, Cho, Sung-Sik, Park, Yeong-Hun, and Kang, Suk-Ju

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, along with interest in autonomous vehicles, the importance of monitoring systems for both drivers and passengers inside vehicles has been increasing. This paper proposes a novel in-vehicle monitoring system the combines 3D pose estimation, seat-belt segmentation, and seat-belt status classification networks. Our system outputs various information necessary for monitoring by accurately considering the data characteristics of the in-vehicle environment. Specifically, the proposed 3D pose estimation directly estimates the absolute coordinates of keypoints for a driver and passengers, and the proposed seat-belt segmentation is implemented by applying a structure based on the feature pyramid. In addition, we propose a classification task to distinguish between normal and abnormal states of wearing a seat belt using results that combine 3D pose estimation with seat-belt segmentation. These tasks can be learned simultaneously and operate in real-time. Our method was evaluated on a private dataset we newly created and annotated. The experimental results show that our method has significantly high performance that can be applied directly to real in-vehicle monitoring systems., Comment: AAAI 2022 workshop AI for Transportation accepted

Published
2022

15. Association of serum globulin with all-cause mortality in incident hemodialysis patients.

Author

Pai, Alex Y, Sy, John, Kim, Joseph, Kleine, Carola-Ellen, Edward, Jessica, Hsiung, Jui-Ting, Kovesdy, Csaba P, Kalantar-Zadeh, Kamyar, and Streja, Elani

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Assistive Technology, Kidney Disease, Cardiovascular, Genetics, Bioengineering, Zero Hunger, Good Health and Well Being, Albumins, Biomarkers, Humans, Inflammation, Kidney Failure, Chronic, Malnutrition, Proportional Hazards Models, Renal Dialysis, Retrospective Studies, Serum Globulins, albumin-to-globulin ratio, globulin, hemodialysis, mortality, protein, Urology & Nephrology, Clinical sciences
Abstract

BackgroundSerum globulin is a major component of total protein and can be elevated in inflammatory disease states. While inflammation is common in hemodialysis patients and associated with mortality and morbidity, the association between serum globulin and mortality has never been examined in hemodialysis patients.MethodsIn a retrospective cohort of 104 164 incident hemodialysis patients treated by a large dialysis organization from 2007 to 2011, we explored the association between baseline serum globulin, albumin: globulin (A:G) ratio and serum protein levels and all-cause, cardiovascular and infection-related mortality with adjustments for demographic variables and laboratory markers of malnutrition and inflammation using Cox proportional hazards models.ResultsPatients with a globulin concentration >3.8 g/dL had a higher all-cause and infection-related mortality risk {hazard ratio [HR] 1.11 [95% confidence interval (CI) 1.06-1.16] and HR 1.28 [95% CI 1.09-1.51], respectively} in the fully adjusted model when compared with the reference group of 3.0-

Published
2022

16. Implementation strategies for hospital-based probiotic administration in a stepped-wedge cluster randomized trial design for preventing hospital-acquired Clostridioides difficile infection

Author

Bresee, Lauren C., Lamont, Nicole, Ocampo, Wrechelle, Holroyd-Leduc, Jayna, Sabuda, Deana, Leal, Jenine, Dalton, Bruce, Kaufman, Jaime, Missaghi, Bayan, Kim, Joseph, Larios, Oscar E., Henderson, Elizabeth, Raman, Maitreyi, Fletcher, Jared R., Faris, Peter, Kraft, Scott, Shen, Ye, Louie, Thomas, and Conly, John M.

Published
2023
Full Text
View/download PDF

22. Primary Care Physician Adherence to Telepsychiatry Recommendations: Intermediate Outcomes from a Randomized Clinical Trial

Author

Lieng, Monica K, Aurora, Magi S, Kang, Young, Kim, Joseph M, Marcin, James P, Chan, Steven R, Mouzoon, Jamie L, Tancredi, Daniel J, Parish, Michelle, Gonzalez, Alvaro D, Scher, Lorin, Xiong, Glen, McCarron, Robert M, and Yellowlees, Peter

Subjects
Health Services and Systems, Health Sciences, Clinical Research, Good Health and Well Being, Adenosine Triphosphate, Adult, Humans, Physicians, Primary Care, Psychiatry, Referral and Consultation, Telemedicine, telemedicine, telepsychiatry, collaborative care, remote consultation, community psychiatry, mental health services, Library and Information Studies, Biomedical Engineering, Public Health and Health Services, Medical Informatics, Health services and systems, Public health
Abstract

Objective:To compare clinical recommendations given by psychiatrists and the adherence to these recommendations by primary care physicians (PCP) following consultations conducted by asynchronous telepsychiatry (ATP) and synchronous telepsychiatry (STP).Materials and Methods:ATP and STP consultations were compared using intermediate data from a randomized clinical trial with adult participant enrollment between April 2014 and December 2017. In both study arms, PCPs received written recommendations from the psychiatrist after each encounter. Independent clinicians reviewed PCP documentation to measure adherence to those recommendations in the 6 months following the baseline consultation.Results:Medical records were reviewed for 645 psychiatrists' consult recommendations; 344 from 61 ATP consultations and 301 from 62 STP consultations. Of those recommendations, 191 (56%) and 173 (58%) were rated fully adherent by two independent raters for ATP and STP, respectively. In a multilevel ordinal logistic regression model adjusted for recommendation type and recommended implementation timing, there was no statistically significant difference in adherence to recommendations for ATP compared with STP (adjusted odds ratio = 0.91, 95% confidence interval = 0.51-1.62). The profiles of recommendation type were comparable between ATP and STP.Conclusions:This is the first PCP adherence study comparing two forms of telemedicine. Although we did not find evidence of a difference between ATP and STP; this study supports the feasibility and acceptability of ATP and STP for the provision of collaborative psychiatric care. Clinical Trial Identifier NCT02084979.

Published
2022

25. Long-term Outcomes with Spinal versus General Anesthesia for Hip Fracture Surgery: A Randomized Trial

Author

Vail, Emily A., Feng, Rui, Sieber, Frederick, Carson, Jeffrey L., Ellenberg, Susan S., Magaziner, Jay, Dillane, Derek, Marcantonio, Edward R., Sessler, Daniel I., Ayad, Sabry, Stone, Trevor, Papp, Steven, Donegan, Derek, Mehta, Samir, Schwenk, Eric S., Marshall, Mitchell, Jaffe, J. Douglas, Luke, Charles, Sharma, Balram, Azim, Syed, Hymes, Robert, Chin, Ki-Jinn, Sheppard, Richard, Perlman, Barry, Sappenfield, Joshua, Hauck, Ellen, Tierney, Ann, Horan, Annamarie D., Neuman, Mark D., Looke, Thomas, Bent, Sandra, Franco-Mora, Ariana, Hedrick, Pamela, Newbern, Matthew, Tadros, Rafik, Pealer, Karen, Vlassakov, Kamen, Buckley, Carolyn, Gavin, Lauren, Gorbatov, Svetlana, Gosnell, James, Steen, Talora, Vafai, Avery, Zeballos, Jose, Hruslinski, Jennifer, Cardenas, Louis, Berry, Ashley, Getchell, John, Quercetti, Nicholas, Hassan, Manal, Bajracharya, Gauasan, Billow, Damien, Bloomfield, Michael, Cuko, Evis, Elyaderani, Mehrun K., Hampton, Robert, Honar, Hooman, Khoshknabi, Dilara, Kim, Daniel, Krahe, David, Lew, Michael M., Maheshwer, Conjeevram B., Niazi, Azfar, Saha, Partha, Salih, Ahmed, de Swart, Robert J., Volio, Andrew, Bolkus, Kelly, DeAngelis, Matthew, Dodson, Gregory, Gerritsen, Jeffrey, McEniry, Brian, Mitrev, Ludmil, Kwofie, M. Kwesi, Belliveau, Anne, Bonazza, Flynn, Lloyd, Vera, Panek, Izabela, Dabiri, Jared, Chavez, Chris, Craig, Jason, Davidson, Todd, Dietrichs, Chad, Fleetwood, Cheryl, Foley, Mike, Getto, Chris, Hailes, Susie, Hermes, Sarah, Hooper, Andy, Koener, Greg, Kohls, Kate, Law, Leslie, Lipp, Adam, Losey, Allison, Nelson, William, Nieto, Mario, Rogers, Pam, Rutman, Steve, Scales, Garrett, Sebastian, Barbara, Stanciu, Tom, Lobel, Gregg, Giampiccolo, Michelle, Herman, Dara, Kaufman, Margit, Murphy, Bryan, Pau, Clara, Puzio, Thomas, Veselsky, Marlene, Apostle, Kelly, Boyer, Dory, Chen Fan, Brenda, Lee, Susan, Lemke, Mike, Merchant, Richard, Moola, Farhad, Payne, Kyrsten, Perey, Bertrand, Viskontas, Darius, Poler, Mark, D’Antonio, Patricia, O’Neill, Greg, Abdullah, Amer, Fish-Fuhrmann, Jamie, Giska, Mark, Fidkowski, Christina, Guthrie, Stuart Trent, Hakeos, William, Hayes, Lillian, Hoegler, Joseph, Nowak, Katherine, Beck, Jeffery, Cuff, Jaslynn, Gaski, Greg, Haaser, Sharon, Holzman, Michael, Malekzadeh, A. Stephen, Ramsey, Lolita, Schulman, Jeff, Schwartzbach, Cary, Azefor, Tangwan, Davani, Arman, Jaberi, Mahmood, Masear, Courtney, Haider, Syed Basit, Chungu, Carolyn, Ebrahimi, Ali, Fikry, Karim, Marcantonio, Andrew, Shelvan, Anitha, Sanders, David, Clarke, Collin, Lawendy, Abdel, Schwartz, Gary, Garg, Mohit, Kim, Joseph, Caruci, Juan, Commeh, Ekow, Cuevas, Randy, Cuff, Germaine, Franco, Lola, Furgiuele, David, Giuca, Matthew, Allman, Melissa, Barzideh, Omid, Cossaro, James, D’Arduini, Armando, Farhi, Anita, Gould, Jason, Kafel, John, Patel, Anuj, Peller, Abraham, Reshef, Hadas, Safur, Mohammed, Toscano, Fiore, Tedore, Tiffany, Akerman, Michael, Brumberger, Eric, Clark, Sunday, Friedlander, Rachel, Jegarl, Anita, Lane, Joseph, Lyden, John P., Mehta, Nili, Murrell, Matthew T., Painter, Nathan, Ricci, William, Sbrollini, Kaitlyn, Sharma, Rahul, Steel, Peter A.D., Steinkamp, Michele, Weinberg, Roniel, Stephenson Wellman, David, Nader, Antoun, Fitzgerald, Paul, Ritz, Michaela, Bryson, Greg, Craig, Alexandra, Farhat, Cassandra, Gammon, Braden, Gofton, Wade, Harris, Nicole, Lalonde, Karl, Liew, Allan, Meulenkamp, Bradley, Sonnenburg, Kendra, Wai, Eugene, Wilkin, Geoffrey, Troxell, Karen, Alderfer, Mary Ellen, Brannen, Jason, Cupitt, Christopher, Gerhart, Stacy, McLin, Renee, Sheidy, Julie, Yurick, Katherine, Chen, Fei, Dragert, Karen, Kiss, Geza, Malveaux, Halina, McCloskey, Deborah, Mellender, Scott, Mungekar, Sagar S., Noveck, Helaine, Sagebien, Carlos, Biby, Luat, McKelvy, Gail, Richards, Anna, Abola, Ramon, Ayala, Brittney, Halper, Darcy, Mavarez, Ana, Rizwan, Sabeen, Choi, Stephen, Awad, Imad, Flynn, Brendan, Henry, Patrick, Jenkinson, Richard, Kaustov, Lilia, Lappin, Elizabeth, McHardy, Paul, Singh, Amara, Donnelly, Joanne, Gonzalez, Meera, Haydel, Christopher, Livelsberger, Jon, Pazionis, Theresa, Slattery, Bridget, Vazquez-Trejo, Maritza, Baratta, Jaime, Cirullo, Michael, Deiling, Brittany, Deschamps, Laura, Glick, Michael, Katz, Daniel, Krieg, James, Lessin, Jennifer, Mojica, Jeffrey, Torjman, Marc, Jin, Rongyu, Salpeter, Mary Jane, Powell, Mark, Simmons, Jeffrey, Lawson, Prentiss, Kukreja, Promil, Graves, Shanna, Sturdivant, Adam, Bryant, Ayesha, Crump, Sandra Joyce, Verrier, Michelle, Green, James, Menon, Matthew, Applegate, Richard, Arias, Ana, Pineiro, Natasha, Uppington, Jeffrey, Wolinsky, Phillip, Gunnett, Amy, Hagen, Jennifer, Harris, Sara, Hollen, Kevin, Holloway, Brian, Horodyski, Mary Beth, Pogue, Trevor, Ramani, Ramachandran, Smith, Cameron, Woods, Anna, Warrick, Matthew, Flynn, Kelly, Mongan, Paul, Ranganath, Yatish, Fernholz, Sean, Ingersoll-Weng, Esperanza, Marian, Anil, Seering, Melinda, Sibenaller, Zita, Stout, Lori, Wagner, Allison, Walter, Alicia, Wong, Cynthia, Orwig, Denise, Goud, Maithri, Helker, Chris, Mezenghie, Lydia, Montgomery, Brittany, Preston, Peter, Schwartz, J. Sanford, Weber, Ramona, Fleisher, Lee A., Mehta, Samir, Stephens-Shields, Alisa J., Dinh, Cassandra, Schwartz, Aron, Chelly, Jacques E., Goel, Shiv, Goncz, Wende, Kawabe, Touichi, Khetarpal, Sharad, Monroe, Amy, Shick, Vladislav, Breidenstein, Max, Dominick, Timothy, Friend, Alexander, Mathews, Donald, Lennertz, Richard, Sanders, Robert, Akere, Helen, Balweg, Tyler, Bo, Amber, Doro, Christopher, Goodspeed, David, Lang, Gerald, Parker, Maggie, Rettammel, Amy, Roth, Mary, White, Marissa, Whiting, Paul, Allen, Brian F.S., Baker, Tracie, Craven, Debra, McEvoy, Matt, Turnbo, Teresa, Kates, Stephen, Morgan, Melanie, Willoughby, Teresa, Weigel, Wade, Auyong, David, Fox, Ellie, Welsh, Tina, Cusson, Bruce, Dobson, Sean, Edwards, Christopher, Harris, Lynette, Henshaw, Daryl, Johnson, Kathleen, McKinney, Glen, Miller, Scott, Reynolds, Jon, Segal, B. Scott, Turner, Jimmy, VanEenenaam, David, Weller, Robert, Lei, Jineli, Treggiari, Miriam, Akhtar, Shamsuddin, Blessing, Marcelle, Johnson, Chanel, Kampp, Michael, Kunze, Kimberly, OʼConnor, Mary, Gaskins, Lakisha J., Looke, Thomas, Tadros, Rafik, Vlassakov, Kamen, Cardenas, Louis, Hassan, Manal, Bolkus, Kelly, Mitrev, Ludmil, Kwofie, M. Kwesi, Dabiri, Jared, Lobel, Gregg, Poler, Mark, Giska, Mark, Sanders, David, Schwartz, Gary, Giuca, Matthew, Tedore, Tiffany, Nader, Antoun, Papp, Stephen, Bryson, Greg, Troxell, Karen, Kiss, Geza, Choi, Stephen, Powell, Mark, Applegate, Richard, Warrick, Matthew, Ranganath, Yatish, Elkassabany, Nabil, Chelly, Jacques E., Hoeft, Mark A., Lennertz, Richard, Sanders, Robert, Allen, Brian F.S., Kates, Stephen, Weigel, Wade, Li, Jinlei, Wijeysundera, Duminda N., Kheterpal, Sachin, Moore, Reneé H., Smith, Alexander K., Tosi, Laura L., Elkassabany, Nabil, Looke, Thomas, Menio, Diane, Mehta, Samir, Fleisher, Lee, Menio, Diane, Hruslinski, Jennifer, Ramsey, Lolita, Gaskins, Lakisha J., Langlois, Christine, Gaskins, Lakisha J., Mezenghie, Lydia, Montgomery, Brittany, Oduwole, Samuel, and Rose, Thomas

Published
2024
Full Text
View/download PDF

29. Longitudinal Analysis of the T-cell Receptor Repertoire in Graft-infiltrating Lymphocytes Following Hand Transplantation

Author

Kim, Joseph Y, Lei, Zhengdeng, Maienschein-Cline, Mark, Chlipala, George E, Balamurugan, Arumugam, McDiarmid, Sue V, Azari, Kodi, and Yang, Otto O

Subjects
Biomedical and Clinical Sciences, Immunology, Transplantation, Organ Transplantation, Rare Diseases, Genes, T-Cell Receptor, Graft Rejection, Graft Survival, Hand, Hand Transplantation, Humans, Immunity, Cellular, Immunogenetic Phenomena, Male, Middle Aged, Skin Transplantation, T-Lymphocytes, Time Factors, Treatment Outcome, Medical and Health Sciences, Surgery, Clinical sciences
Abstract

BackgroundT lymphocyte-mediated acute rejection is a significant complication following solid organ transplantation. Standard methods of monitoring for acute rejection rely on assessing histological tissue damage but do not define the immunopathogenesis. Additionally, current therapies for rejection broadly blunt cellular immunity, creating a high risk for opportunistic infections. There is, therefore, a need to better understand the process of acute cellular rejection to help develop improved prognostic tests and narrowly targeted therapies.MethodsThrough next-generation sequencing, we characterized and compared the clonal T-cell receptor (TCR) repertoires of graft-infiltrating lymphocytes (GILs) and blood-derived lymphocytes from a hand transplant recipient over 420 days following transplantation. We also tracked the TCR clonal persistence and V beta (BV) gene usage, evaluating overlap between these 2 compartments.ResultsTCR repertoires of blood and GIL populations remained distinct throughout the sampling period, and differential BV usage was consistently seen between these compartments. GIL TCR clones persisted over time and were seen in only limited frequency in the blood T-lymphocyte populations.ConclusionsWe demonstrate that blood monitoring of TCR clones does not reveal the pathogenic process of acute cellular rejection in transplanted tissue. GILs show clonal persistence with biased BV usage, suggesting that tissue TCR clonal monitoring could be useful, although a deeper understanding is necessary to prognosticate rejection based on TCR clonal repertoires. Finally, the distinct TCR BV usage bias in GILs raises the possibility for prevention and therapy of acute cellular rejection based on targeting of specific TCR clones.

Published
2021

30. Designing a Bonus-Malus system reflecting the claim size under the dependent frequency-severity model

Author

Oh, Rosy, Kim, Joseph H. T., and Ahn, Jae Youn

Subjects
Statistics - Applications
Abstract

In auto insurance, a Bonus-Malus System (BMS) is commonly used as a posteriori risk classification mechanism to set the premium for the next contract period based on a policyholder's claim history. Even though recent literature reports evidence of a significant dependence between frequency and severity, the current BMS practice is to use a frequency-based transition rule while ignoring severity information. Although Oh et al. (2019) claim that the frequency-driven BMS transition rule can accommodate the dependence between frequency and severity, their proposal is only a partial solution, as the transition rule still completely ignores the claim severity and is unable to penalize large claims. In this study, we propose to use the BMS with a transition rule based on both frequency and size of claim, based on the bivariate random effect model, which conveniently allows dependence between frequency and severity. We analytically derive the optimal relativities under the proposed BMS framework and show that the proposed BMS outperforms the existing frequency-driven BMS. Later numerical experiments are also provided using both hypothetical and actual datasets in order to assess the effect of various dependencies on the BMS risk classification and confirm our theoretical findings.

Published
2020

36. Integrating electronic health records (EHRs) to facilitate cancer biomarker testing: Real-world implementation barriers and solutions.

Author

Huelsman, Karen M., primary, Offit, Caroline, additional, Waugh, Wendi, additional, McNair, Christopher, additional, Kurtin, Sandra E., additional, Enstad, Crystal, additional, Rice, Courtney, additional, Osterman, Travis, additional, Yu, Peter Paul, additional, Shepard, Gregg Christian, additional, Martin, Nikki A., additional, Kim, Joseph, additional, Kisiel, Molly, additional, and Plotkin, Elana, additional

Published
2024
Full Text
View/download PDF

37. An ALK2 inhibitor, BLU-782, prevents heterotopic ossification in a mouse model of fibrodysplasia ossificans progressiva

Author

Davis, Alison J., primary, Brooijmans, Natasja, additional, Brubaker, Jason D., additional, Stevison, Faith, additional, LaBranche, Timothy P., additional, Albayya, Faris, additional, Fleming, Paul, additional, Hodous, Brian L., additional, Kim, Joseph L., additional, Kim, Sean, additional, Lobbardi, Riadh, additional, Palmer, Michael, additional, Sheets, Michael P., additional, Vassiliadis, John, additional, Wang, Ruduan, additional, Williams, Brett D., additional, Wilson, Douglas, additional, Xu, Lan, additional, Zhu, Xing Julia, additional, Bouchard, Keith, additional, Hunter, Jeffrey W., additional, Graul, Chris, additional, Greenblatt, Elliot, additional, Hussein, Amira, additional, Lyon, Morgan, additional, Russo, Joelle, additional, Stewart, Rachel, additional, Dorsch, Marion, additional, Guzi, Timothy J., additional, Kadambi, Vivek, additional, Lengauer, Christoph, additional, and Garner, Andrew P., additional

Published
2024
Full Text
View/download PDF

38. Partial Pancreatoduodenectomy

Author

Kim, Joseph J., Sarpel, Umut, Labow, Daniel M., Scott-Conner, Carol E. H., editor, Kaiser, Andreas M., editor, Nguyen, Ninh T., editor, Sarpel, Umut, editor, and Sugg, Sonia L., editor

Published
2022
Full Text
View/download PDF

39. Wearing Your Worth at Work: The Consequences of Employees' Daily Clothing Choices.

Author

Kim, Joseph K., Holtz, Brian C., and Vogel, Ryan M.

Subjects
EMPLOYEES, CLOTHING & dress, SELF-esteem, ORGANIZATIONAL behavior, CONFORMITY, AESTHETICS, WORK environment
Abstract

Do the clothes worn to work impact employees' thoughts and behaviors? Despite the universal necessity of wearing clothes and the fact that employees make decisions about this daily, organizational scholars have not yet addressed this question. We integrate sociometer and enclothed cognition theories to propose that aspects of clothing—their aesthetics, conformity, and uniqueness—hold symbolic meanings that have implications for employees' state self-esteem and subsequent task and relational behaviors (i.e., goal progress, social avoidance). We first provide evidence for the nature of the symbolic meanings associated with these three dimensions of work clothing in a set of within-person experimental studies. The results of a 10-day field study of employees from four organizations generally supported our predictions, showing that daily clothing aesthetics and uniqueness had effects on state self-esteem and downstream behavioral consequences. The effects of daily clothing conformity emerged under the condition of greater interaction frequency with others in the workplace. Our manuscript contributes to both major theories from which we draw, and further offers theoretical and practical contributions to the literature on organizational clothing. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

42. Evaluation of Protein Kinase cAMP-Activated Catalytic Subunit Alpha as a Therapeutic Target for Fibrolamellar Carcinoma

Author

Schalm, Stefanie S., O’Hearn, Erin, Wilson, Kevin, LaBranche, Timothy P., Silva, Grace, Zhang, Zhuo, DiPietro, Lucian, Bifulco, Neil, Woessner, Richard, Stransky, Nicolas, Sappal, Darshan, Campbell, Robert, Lobbardi, Riadh, Palmer, Michael, Kim, Joseph, Ye, Chaoyang, Dorsch, Marion, Lengauer, Christoph, Guzi, Timothy, Kadambi, Vivek, Garner, Andrew, and Hoeflich, Klaus P.

Published
2023
Full Text
View/download PDF

46. A comparison between agent-based and GIS-based tsunami evacuation simulations: a case study for Tofino, BC

Author

Kim, Joseph, Takabatake, Tomoyuki, Nistor, Ioan, and Shibayama, Tomoya

Subjects
Vancouver Island -- Environmental aspects -- Case studies, Tsunamis -- Models -- Case studies -- Canada, Evacuation of civilians -- Models -- Case studies, Agent based models -- Usage -- Case studies, Geographic information systems -- Case studies -- Usage, Geographic information system, Engineering and manufacturing industries
Abstract

Soft measures such as evacuation planning are recommended to mitigate the loss of life during tsunamis. Two types of evacuation models are widely used: (i) agent-based modelling (ABM) defines sets of rules that individual agents in a simulation follow during a simulated evacuation; and (ii) geographical information systems (GIS) are more accessible to city planners, but cannot incorporate the dynamic behaviours found in ABMs. The two evacuation modelling methodologies were compared through a case study by assessing the state of evacuation preparedness and investigating potential mitigation options. The two models showed different magnitudes for mortality rates and facility demand but had similar trends. Both models agreed on the best solution to reduce the loss of life for the community. GIS may serve as a useful tool for initial investigation or as a validation tool for ABMs. ABMs are recommended for use when modelling evacuation until GIS methodologies are further developed. Key words: evacuation model, agent-based model, GIS model, tsunami inundation, coastal hazards. Des mesures souples, comme la planification d'evacuation, sont recommandees pour attenuer les pertes de vie lors de tsunamis. Deux types de modeles d'evacuation sont largement utilises : (i) la modelisation fondee sur les agents (MFA) definit des ensembles de regles que les agents individuels d'une simulation suivent pendant une evacuation simulee tandis que (ii) les systemes d'information geographique (SIG) sont plus accessibles aux urbanistes, mais ne peuvent integrer les comportements dynamiques des MFA. Les deux methodes de modelisation d'evacuation ont ete comparees au moyen d'une etude de cas en evaluant l'etat de preparation a l'evacuation et en examinant les options d'attenuation possibles. Les deux modeles presentaient des ampleurs differentes pour les taux de mortalite et la demande d'installations, mais des tendances semblables. Les deux modeles concordaient quant a la meilleure solution pour reduire les pertes de vie dans la collectivite. Les SIG peuvent servir d'outil pour l'enquete preliminaire ou comme outil de validation pour les MFA. Il est recommande d'utiliser les MFA lors de la modelisation d'evacuation jusqu'a ce que les methodologies de SIG soient developpees davantage. [Traduit par la Redaction] Mots-cles : modele d'evacuation, modele fonde sur les agents, modele de systeme d'information geographique (SIG), inondation par tsunami, dangers cotiers., 1. Introduction 1.1. Evacuation planning as a tsunami mitigation measure In Japan, hard-structural tsunami mitigation measures such as the Kamaishi breakwater and other seawalls protecting the coastal areas experienced partial [...]

Published
2022
Full Text
View/download PDF

47. Supplementary Table 2 from The Immune Suppressor IGSF1 as a Potential Target for Cancer Immunotherapy

Author

Koh, Dong-In, primary, Lee, Minki, primary, Park, Yoon Sun, primary, Shin, Jae-Sik, primary, Kim, Joseph, primary, Ryu, Yea Seong, primary, Lee, Jun Hyung, primary, Bae, Seunggeon, primary, Lee, Mi So, primary, Hong, Jun Ki, primary, Jeong, Hong-Rae, primary, Choi, Mingee, primary, Hong, Seung-Woo, primary, Kim, Dong Kwan, primary, Lee, Hyun-kyung, primary, Kim, Bomi, primary, Yoon, Yoo Sang, primary, and Jin, Dong-Hoon, primary

Published
2024
Full Text
View/download PDF

48. Supplementary Figure 5 from The Immune Suppressor IGSF1 as a Potential Target for Cancer Immunotherapy

Author

Koh, Dong-In, primary, Lee, Minki, primary, Park, Yoon Sun, primary, Shin, Jae-Sik, primary, Kim, Joseph, primary, Ryu, Yea Seong, primary, Lee, Jun Hyung, primary, Bae, Seunggeon, primary, Lee, Mi So, primary, Hong, Jun Ki, primary, Jeong, Hong-Rae, primary, Choi, Mingee, primary, Hong, Seung-Woo, primary, Kim, Dong Kwan, primary, Lee, Hyun-kyung, primary, Kim, Bomi, primary, Yoon, Yoo Sang, primary, and Jin, Dong-Hoon, primary

Published
2024
Full Text
View/download PDF

49. Data from The Immune Suppressor IGSF1 as a Potential Target for Cancer Immunotherapy

Author

Koh, Dong-In, primary, Lee, Minki, primary, Park, Yoon Sun, primary, Shin, Jae-Sik, primary, Kim, Joseph, primary, Ryu, Yea Seong, primary, Lee, Jun Hyung, primary, Bae, Seunggeon, primary, Lee, Mi So, primary, Hong, Jun Ki, primary, Jeong, Hong-Rae, primary, Choi, Mingee, primary, Hong, Seung-Woo, primary, Kim, Dong Kwan, primary, Lee, Hyun-kyung, primary, Kim, Bomi, primary, Yoon, Yoo Sang, primary, and Jin, Dong-Hoon, primary

Published
2024
Full Text
View/download PDF

50. Supplementary Figure 1 from The Immune Suppressor IGSF1 as a Potential Target for Cancer Immunotherapy

Author

Koh, Dong-In, primary, Lee, Minki, primary, Park, Yoon Sun, primary, Shin, Jae-Sik, primary, Kim, Joseph, primary, Ryu, Yea Seong, primary, Lee, Jun Hyung, primary, Bae, Seunggeon, primary, Lee, Mi So, primary, Hong, Jun Ki, primary, Jeong, Hong-Rae, primary, Choi, Mingee, primary, Hong, Seung-Woo, primary, Kim, Dong Kwan, primary, Lee, Hyun-kyung, primary, Kim, Bomi, primary, Yoon, Yoo Sang, primary, and Jin, Dong-Hoon, primary

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results