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51. Withdrawal during outpatient low dose buprenorphine initiation in people who use fentanyl: a retrospective cohort study.

Author

Jones, Benjamin, Geier, Michelle, Neuhaus, John, Coffin, Phillip, Snyder, Hannah, Soran, Christine, Knight, Kelly, and Suen, Leslie

Subjects
Buprenorphine, Fentanyl, Low dose buprenorphine initiation, Opioid agonist therapy, Opioid use disorder, Opioid withdrawal, Opioids, Precipitated withdrawal, Male, Humans, Female, Adult, Buprenorphine, Fentanyl, Retrospective Studies, Outpatients, Opioid-Related Disorders, Substance Withdrawal Syndrome, Analgesics, Opioid
Abstract

BACKGROUND: Buprenorphine is an effective treatment for opioid use disorder (OUD); however, buprenorphine initiation can be complicated by withdrawal symptoms including precipitated withdrawal. There has been increasing interest in using low dose initiation (LDI) strategies to reduce this withdrawal risk. As there are limited data on withdrawal symptoms during LDI, we characterize withdrawal symptoms in people with daily fentanyl use who underwent initiation using these strategies as outpatients. METHODS: We conducted a retrospective chart review of patients with OUD using daily fentanyl who were prescribed 7-day or 4-day LDI at 2 substance use disorder treatment clinics in San Francisco. Two addiction medicine experts assessed extracted chart documentation for withdrawal severity and precipitated withdrawal, defined as acute worsening of withdrawal symptoms immediately after taking buprenorphine. A third expert adjudicated disagreements. Data were analyzed using descriptive statistics. RESULTS: There were 175 initiations in 126 patients. The mean age was 37 (SD 10 years). 71% were men, 26% women, and 2% non-binary. 21% identified as Black, 16% Latine, and 52% white. 60% were unstably housed and 75% had Medicaid insurance. Substance co-use included 74% who used amphetamines, 29% cocaine, 22% benzodiazepines, and 19% alcohol. Follow up was available for 118 (67%) initiations. There was deviation from protocol instructions in 22% of these initiations with follow up. 31% had any withdrawal, including 21% with mild symptoms, 8% moderate and 2% severe. Precipitated withdrawal occurred in 10 cases, or 8% of initiations with follow up. Of these, 7 had deviation from protocol instructions; thus, there were 3 cases with follow up (3%) in which precipitated withdrawal occurred without protocol deviation. CONCLUSIONS: Withdrawal was relatively common in our cohort but was mostly mild, and precipitated withdrawal was rare. Deviation from instructions, structural barriers, and varying fentanyl use characteristics may contribute to withdrawal. Clinicians should counsel patients who use fentanyl that mild withdrawal symptoms are likely during LDI, and there is still a low risk for precipitated withdrawal. Future studies should compare withdrawal across initiation types, seek ways to support patients in initiating buprenorphine, and qualitatively elicit patients withdrawal experiences.

Published
2024

52. Dermal injury drives a skin to gut axis that disrupts the intestinal microbiome and intestinal immune homeostasis in mice.

Author

Dokoshi, Tatsuya, Chen, Yang, Cavagnero, Kellen, Rahman, Gibraan, Hakim, Daniel, Brinton, Samantha, Schwarz, Hana, Brown, Elizabeth, ONeill, Alan, Nakamura, Yoshiyuki, Li, Fengwu, Salzman, Nita, Knight, Rob, and Gallo, Richard

Subjects
Mice, Animals, Gastrointestinal Microbiome, Hyaluronic Acid, Colitis, Intestinal Mucosa, Fecal Microbiota Transplantation, Dextran Sulfate, Mice, Inbred C57BL, Disease Models, Animal, Colon
Abstract

The composition of the microbial community in the intestine may influence the functions of distant organs such as the brain, lung, and skin. These microbes can promote disease or have beneficial functions, leading to the hypothesis that microbes in the gut explain the co-occurrence of intestinal and skin diseases. Here, we show that the reverse can occur, and that skin directly alters the gut microbiome. Disruption of the dermis by skin wounding or the digestion of dermal hyaluronan results in increased expression in the colon of the host defense genes Reg3 and Muc2, and skin wounding changes the composition and behavior of intestinal bacteria. Enhanced expression Reg3 and Muc2 is induced in vitro by exposure to hyaluronan released by these skin interventions. The change in the colon microbiome after skin wounding is functionally important as these bacteria penetrate the intestinal epithelium and enhance colitis from dextran sodium sulfate (DSS) as seen by the ability to rescue skin associated DSS colitis with oral antibiotics, in germ-free mice, and fecal microbiome transplantation to unwounded mice from mice with skin wounds. These observations provide direct evidence of a skin-gut axis by demonstrating that damage to the skin disrupts homeostasis in intestinal host defense and alters the gut microbiome.

Published
2024

53. Harnessing the power within: engineering the microbiome for enhanced gynecologic health

Author

Brennan, Caitriona, Chan, Kristina, Kumar, Tanya, Maissy, Erica, Brubaker, Linda, Dothard, Marisol I, Gilbert, Jack A, Gilbert, Katharine E, Lewis, Amanda L, Thackray, Varykina G, Zarrinpar, Amir, and Knight, Rob

Subjects
Microbiology, Biological Sciences, Biomedical and Clinical Sciences, Nutrition, Genetics, Prevention, Complementary and Integrative Health, Human Genome, Underpinning research, 1.1 Normal biological development and functioning, Infection, Oral and gastrointestinal, Good Health and Well Being, Female, Humans, Animals, Microbiota, Gastrointestinal Microbiome, Probiotics, Prebiotics, Reproduction, microbiome manipulation, gynecologic health, polycystic ovary syndrome, bacterial vaginosis, endometriosis
Abstract

Graphical abstractAbstractAlthough numerous studies have demonstrated the impact of microbiome manipulation on human health, research on the microbiome's influence on female health remains relatively limited despite substantial disease burden. In light of this, we present a selected review of clinical trials and preclinical studies targeting both the vaginal and gut microbiomes for the prevention or treatment of various gynecologic conditions. Specifically, we explore studies that leverage microbiota transplants, probiotics, prebiotics, diet modifications, and engineered microbial strains. A healthy vaginal microbiome for females of reproductive age consists of lactic acid-producing bacteria predominantly of the Lactobacillus genus, which serves as a protective barrier against pathogens and maintains a balanced ecosystem. The gut microbiota's production of short-chain fatty acids, metabolism of primary bile acids, and modulation of sex steroid levels have significant implications for the interplay between host and microbes throughout the body, ultimately impacting reproductive health. By harnessing interventions that modulate both the vaginal and gut microbiomes, it becomes possible to not only maintain homeostasis but also mitigate pathological conditions. While the field is still working toward making broad clinical recommendations, the current studies demonstrate that manipulating the microbiome holds great potential for addressing diverse gynecologic conditions.Lay summaryManipulating the microbiome has recently entered popular culture, with various diets thought to aid the microbes that live within us. These microbes live in different locations of our body and accordingly help us digest food, modulate our immune system, and influence reproductive health. The role of the microbes living in and influencing the female reproductive tract remains understudied despite known roles in common conditions such as vulvovaginal candidiasis (affecting 75% of females in their lifetime), bacterial vaginosis (25% of females in their lifetime), cervical HPV infection (80% of females in their lifetime), endometriosis (6-10% of females of reproductive age), and polycystic ovary syndrome (10-12% of females of reproductive age). Here, we review four different approaches used to manipulate the female reproductive tract and gastrointestinal system microbiomes: microbiota transplants, probiotics, prebiotics, and dietary interventions, and the use of engineered microbial strains. In doing so, we aim to stimulate discussion on new ways to understand and treat female reproductive health conditions.

Published
2024

54. Robustness of cancer microbiome signals over a broad range of methodological variation.

Author

Sepich-Poore, Gregory, McDonald, Daniel, Kopylova, Evguenia, Guccione, Caitlin, Zhu, Qiyun, Austin, George, Carpenter, Carolina, Fraraccio, Serena, Wandro, Stephen, Kosciolek, Tomasz, Janssen, Stefan, Metcalf, Jessica, Song, Se, Kanbar, Jad, Miller-Montgomery, Sandrine, Heaton, Robert, Mckay, Rana, Patel, Sandip, Swafford, Austin, Korem, Tal, and Knight, Rob

Subjects
Humans, Neoplasms, Microbiota
Abstract

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Published
2024

55. Integration of polygenic and gut metagenomic risk prediction for common diseases.

Author

Liu, Yang, Ritchie, Scott, Teo, Shu, Ruuskanen, Matti, Kambur, Oleg, Zhu, Qiyun, Sanders, Jon, Vázquez-Baeza, Yoshiki, Verspoor, Karin, Jousilahti, Pekka, Lahti, Leo, Niiranen, Teemu, Salomaa, Veikko, Havulinna, Aki, Méric, Guillaume, Inouye, Michael, and Knight, Rob

Subjects
Male, Humans, Diabetes Mellitus, Type 2, Prospective Studies, Risk Factors, Coronary Artery Disease, Prostatic Neoplasms, Genetic Risk Score
Abstract

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Published
2024

56. A Design Space for Intelligent and Interactive Writing Assistants

Author

Lee, Mina, Gero, Katy Ilonka, Chung, John Joon Young, Shum, Simon Buckingham, Raheja, Vipul, Shen, Hua, Venugopalan, Subhashini, Wambsganss, Thiemo, Zhou, David, Alghamdi, Emad A., August, Tal, Bhat, Avinash, Choksi, Madiha Zahrah, Dutta, Senjuti, Guo, Jin L. C., Hoque, Md Naimul, Kim, Yewon, Knight, Simon, Neshaei, Seyed Parsa, Sergeyuk, Agnia, Shibani, Antonette, Shrivastava, Disha, Shroff, Lila, Stark, Jessi, Sterman, Sarah, Wang, Sitong, Bosselut, Antoine, Buschek, Daniel, Chang, Joseph Chee, Chen, Sherol, Kreminski, Max, Park, Joonsuk, Pea, Roy, Rho, Eugenia H., Shen, Shannon Zejiang, and Siangliulue, Pao

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computation and Language
Abstract

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through a large community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension) by systematically reviewing 115 papers. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the envisioning and design of new writing assistants., Comment: Published as a conference paper at CHI 2024

Published
2024
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57. Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach

Author

Mahbub, Maria, Dams, Gregory M., Srinivasan, Sudarshan, Rizy, Caitlin, Danciu, Ioana, Trafton, Jodie, and Knight, Kathryn

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

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by American insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large Language Models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients., Comment: 10 pages, 4 figures, 2 tables

Published
2024

58. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol

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

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

59. A Deep Learning Method for Classification of Biophilic Artworks

Author

Kar, Purna, Bird, Jordan J., Xing, Yangang, Sumich, Alexander, Knight, Andrew, Lotfi, Ahmad, and van Barthold, Benedict Carpenter

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Biophilia is an innate love for living things and nature itself that has been associated with a positive impact on mental health and well-being. This study explores the application of deep learning methods for the classification of Biophilic artwork, in order to learn and explain the different Biophilic characteristics present in a visual representation of a painting. Using the concept of Biophilia that postulates the deep connection of human beings with nature, we use an artificially intelligent algorithm to recognise the different patterns underlying the Biophilic features in an artwork. Our proposed method uses a lower-dimensional representation of an image and a decoder model to extract salient features of the image of each Biophilic trait, such as plants, water bodies, seasons, animals, etc., based on learnt factors such as shape, texture, and illumination. The proposed classification model is capable of extracting Biophilic artwork that not only helps artists, collectors, and researchers studying to interpret and exploit the effects of mental well-being on exposure to nature-inspired visual aesthetics but also enables a methodical exploration of the study of Biophilia and Biophilic artwork for aesthetic preferences. Using the proposed algorithms, we have also created a gallery of Biophilic collections comprising famous artworks from different European and American art galleries, which will soon be published on the Vieunite@ online community.

Published
2024

60. Torsion-free abelian groups of finite rank and fields of finite transcendence degree

Author

Ho, Meng-Che "Turbo", Knight, Julia, and Miller, Russell

Subjects
Mathematics - Logic
Abstract

Let $\operatorname{TFAb}_r$ be the class of torsion-free abelian groups of rank $r$, and let $\operatorname{FD}_r$ be the class of fields of characteristic $0$ and transcendence degree~$r$. We compare these classes using various notions. Considering Scott complexity of the structures in the classes and the complexity of the isomorphism relations on the classes, the classes seem very similar. Hjorth and Thomas showed that the $\operatorname{TFAb}_r$ are strictly increasing under Borel reducibility. This is not so for the classes $\operatorname{FD}_r$. Thomas and Velickovic showed that for sufficiently large $r$, the classes $\operatorname{FD}_r$ are equivalent under Borel reducibility. We try to compare the groups with the fields, using Borel reducibility, and also using some effective variants. We give functorial Turing computable embeddings of $\operatorname{TFAb}_r$ in $\operatorname{FD}_r$, and of $\operatorname{FD}_r$ in $\operatorname{FD}_{r+1}$. We show that under computable countable reducibility, $\operatorname{TFAb}_1$ lies on top among the classes we are considering. In fact, under computable countable reducibility, isomorphism on $\operatorname{TFAb}_1$ lies on top among equivalence relations that are effective $\Sigma_3$, along with the Vitali equivalence relation on $2^\omega$., Comment: 27 pages

Published
2024

61. A far-ultraviolet-driven photoevaporation flow observed in a protoplanetary disk

Author

Berné, Olivier, Habart, Emilie, Peeters, Els, Schroetter, Ilane, Canin, Amélie, Sidhu, Ameek, Chown, Ryan, Bron, Emeric, Haworth, Thomas J., Klaassen, Pamela, Trahin, Boris, Van De Putte, Dries, Alarcón, Felipe, Zannese, Marion, Abergel, Alain, Bergin, Edwin A., Bernard-Salas, Jeronimo, Boersma, Christiaan, Cami, Jan, Cuadrado, Sara, Dartois, Emmanuel, Dicken, Daniel, Elyajouri, Meriem, Fuente, Asunción, Goicoechea, Javier R., Gordon, Karl D., Issa, Lina, Joblin, Christine, Kannavou, Olga, Khan, Baria, Lacinbala, Ozan, Languignon, David, Gal, Romane Le, Maragkoudakis, Alexandros, Meshaka, Raphael, Okada, Yoko, Onaka, Takashi, Pasquini, Sofia, Pound, Marc W., Robberto, Massimo, Röllig, Markus, Schefter, Bethany, Schirmer, Thiébaut, Simmer, Thomas, Tabone, Benoit, Tielens, Alexander G. G. M., Vicente, Sílvia, Wolfire, Mark G., Aleman, Isabel, Allamandola, Louis, Auchettl, Rebecca, Baratta, Giuseppe Antonio, Baruteau, Clément, Bejaoui, Salma, Bera, Partha P., Black, John H., Boulanger, Francois, Bouwman, Jordy, Brandl, Bernhard, Brechignac, Philippe, Brünken, Sandra, Buragohain, Mridusmita, Burkhardt, Andrew, Candian, Alessandra, Cazaux, Stéphanie, Cernicharo, Jose, Chabot, Marin, Chakraborty, Shubhadip, Champion, Jason, Colgan, Sean W. J., Cooke, Ilsa R., Coutens, Audrey, Cox, Nick L. J., Demyk, Karine, Meyer, Jennifer Donovan, Engrand, Cécile, Foschino, Sacha, García-Lario, Pedro, Gavilan, Lisseth, Gerin, Maryvonne, Godard, Marie, Gottlieb, Carl A., Guillard, Pierre, Gusdorf, Antoine, Hartigan, Patrick, He, Jinhua, Herbst, Eric, Hornekaer, Liv, Jäger, Cornelia, Janot-Pacheco, Eduardo, Kaufman, Michael, Kemper, Francisca, Kendrew, Sarah, Kirsanova, Maria S., Knight, Collin, Kwok, Sun, Labiano, Álvaro, Lai, Thomas S. -Y., Lee, Timothy J., Lefloch, Bertrand, Petit, Franck Le, Li, Aigen, Linz, Hendrik, Mackie, Cameron J., Madden, Suzanne C., Mascetti, Joëlle, McGuire, Brett A., Merino, Pablo, Micelotta, Elisabetta R., Morse, Jon A., Mulas, Giacomo, Neelamkodan, Naslim, Ohsawa, Ryou, Paladini, Roberta, Palumbo, Maria Elisabetta, Pathak, Amit, Pendleton, Yvonne J., Petrignani, Annemieke, Pino, Thomas, Puga, Elena, Rangwala, Naseem, Rapacioli, Mathias, Ricca, Alessandra, Roman-Duval, Julia, Roueff, Evelyne, Rouillé, Gaël, Salama, Farid, Sales, Dinalva A., Sandstrom, Karin, Sarre, Peter, Sciamma-O'Brien, Ella, Sellgren, Kris, Shannon, Matthew J., Simonnin, Adrien, Shenoy, Sachindev S., Teyssier, David, Thomas, Richard D., Togi, Aditya, Verstraete, Laurent, Witt, Adolf N., Wootten, Alwyn, Ysard, Nathalie, Zettergren, Henning, Zhang, Yong, Zhang, Ziwei E., and Zhen, Junfeng

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Most low-mass stars form in stellar clusters that also contain massive stars, which are sources of far-ultraviolet (FUV) radiation. Theoretical models predict that this FUV radiation produces photo-dissociation regions (PDRs) on the surfaces of protoplanetary disks around low-mass stars, impacting planet formation within the disks. We report JWST and Atacama Large Millimetere Array observations of a FUV-irradiated protoplanetary disk in the Orion Nebula. Emission lines are detected from the PDR; modelling their kinematics and excitation allows us to constrain the physical conditions within the gas. We quantify the mass-loss rate induced by the FUV irradiation, finding it is sufficient to remove gas from the disk in less than a million years. This is rapid enough to affect giant planet formation in the disk.

Published
2024

62. A Multi-Agent Model for Opinion Evolution under Cognitive Biases

Author

Alvim, Mário S., da Silva, Artur Gaspar, Knight, Sophia, and Valencia, Frank

Subjects
Computer Science - Multiagent Systems, Computer Science - Social and Information Networks
Abstract

We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region $[-1,1]^2$ and categorized into four sub-regions based on the potential reactions they may elicit in an agent during instances of opinion disagreement. Under the assumption that each bias of every agent is a continuous function within the region of receptive but resistant reactions ($\mathbf{R}$), we show that the society converges to a consensus if the graph is strongly connected. Under the same assumption, we also establish that the entire society converges to a unanimous opinion if and only if the source components of the graph-namely, strongly connected components with no external influence-converge to that opinion. We illustrate that convergence is not guaranteed for strongly connected graphs when biases are either discontinuous functions in $\mathbf{R}$ or not included in $\mathbf{R}$. We showcase our model through a series of examples and simulations, offering insights into how opinions form in social networks under cognitive biases.

Published
2024

63. A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation

Author

Zhou, Yuyue, Felfeliyan, Banafshe, Ghosh, Shrimanti, Knight, Jessica, Alves-Pereira, Fatima, Keen, Christopher, Küpper, Jessica, Hareendranathan, Abhilash Rakkunedeth, and Jaremko, Jacob L.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, and domain-specific restriction limits model generalizability. Visual in-context learning (ICL) is a new and exciting area of research in computer vision. Unlike conventional deep learning, ICL emphasizes the model's ability to adapt to new tasks based on given examples quickly. Inspired by MAE-VQGAN, we proposed a new simple visual ICL method called SimICL, combining visual ICL pairing images with masked image modeling (MIM) designed for self-supervised learning. We validated our method on bony structures segmentation in a wrist ultrasound (US) dataset with limited annotations, where the clinical objective was to segment bony structures to help with further fracture detection. We used a test set containing 3822 images from 18 patients for bony region segmentation. SimICL achieved an remarkably high Dice coeffient (DC) of 0.96 and Jaccard Index (IoU) of 0.92, surpassing state-of-the-art segmentation and visual ICL models (a maximum DC 0.86 and IoU 0.76), with SimICL DC and IoU increasing up to 0.10 and 0.16. This remarkably high agreement with limited manual annotations indicates SimICL could be used for training AI models even on small US datasets. This could dramatically decrease the human expert time required for image labeling compared to conventional approaches, and enhance the real-world use of AI assistance in US image analysis.

Published
2024

64. A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation

Author

Tang, Xiangru, Dai, Howard, Knight, Elizabeth, Wu, Fang, Li, Yunyang, Li, Tianxiao, and Gerstein, Mark

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

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

Published
2024

78. One hundred years of EEG for brain and behaviour research

Author

Mushtaq, Faisal, Welke, Dominik, Gallagher, Anne, Pavlov, Yuri G., Kouara, Layla, Bosch-Bayard, Jorge, van den Bosch, Jasper J. F., Arvaneh, Mahnaz, Bland, Amy R., Chaumon, Maximilien, Borck, Cornelius, He, Xun, Luck, Steven J., Machizawa, Maro G., Pernet, Cyril, Puce, Aina, Segalowitz, Sidney J., Rogers, Christine, Awais, Muhammad, Babiloni, Claudio, Bailey, Neil W., Baillet, Sylvain, Bendall, Robert C. A., Brady, Daniel, Bringas-Vega, Maria L., Busch, Niko A., Calzada-Reyes, Ana, Chatard, Armand, Clayson, Peter E., Cohen, Michael X., Cole, Jonathan, Constant, Martin, Corneyllie, Alexandra, Coyle, Damien, Cruse, Damian, Delis, Ioannis, Delorme, Arnaud, Fair, Damien, Falk, Tiago H., Gamer, Matthias, Ganis, Giorgio, Gloy, Kilian, Gregory, Samantha, Hassall, Cameron D., Hiley, Katherine E., Ivry, Richard B., Jerbi, Karim, Jenkins, Michael, Kaiser, Jakob, Keil, Andreas, Knight, Robert T., Kochen, Silvia, Kotchoubey, Boris, Krigolson, Olave E., Langer, Nicolas, Liesefeld, Heinrich R., Lippé, Sarah, London, Raquel E., MacNamara, Annmarie, Makeig, Scott, Marinovic, Welber, Martínez-Montes, Eduardo, Marzuki, Aleya A., Mathew, Ryan K., Michel, Christoph, Millán, José d. R., Mon-Williams, Mark, Morales-Chacón, Lilia, Naar, Richard, Nilsonne, Gustav, Niso, Guiomar, Nyhus, Erika, Oostenveld, Robert, Paul, Katharina, Paulus, Walter, Pfabigan, Daniela M., Pourtois, Gilles, Rampp, Stefan, Rausch, Manuel, Robbins, Kay, Rossini, Paolo M., Ruzzoli, Manuela, Schmidt, Barbara, Senderecka, Magdalena, Srinivasan, Narayanan, Stegmann, Yannik, Thompson, Paul M., Valdes-Sosa, Mitchell, van der Molen, Melle J. W., Veniero, Domenica, Verona, Edelyn, Voytek, Bradley, Yao, Dezhong, Evans, Alan C., and Valdes-Sosa, Pedro

Published
2024
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79. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

Author

Mei, Zhendong, Wang, Fenglei, Bhosle, Amrisha, Dong, Danyue, Mehta, Raaj, Ghazi, Andrew, Zhang, Yancong, Liu, Yuxi, Rinott, Ehud, Ma, Siyuan, Rimm, Eric B., Daviglus, Martha, Willett, Walter C., Knight, Rob, Hu, Frank B., Qi, Qibin, Chan, Andrew T., Burk, Robert D., Stampfer, Meir J., Shai, Iris, Kaplan, Robert C., Huttenhower, Curtis, and Wang, Dong D.

Published
2024
Full Text
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80. Genetic risk factors for COVID-19 and influenza are largely distinct

Author

Kosmicki, Jack A., Marcketta, Anthony, Sharma, Deepika, Di Gioia, Silvio Alessandro, Batista, Samantha, Yang, Xiao-Man, Tzoneva, Gannie, Martinez, Hector, Sidore, Carlo, Kessler, Michael D., Horowitz, Julie E., Roberts, Genevieve H. L., Justice, Anne E., Banerjee, Nilanjana, Coignet, Marie V., Leader, Joseph B., Park, Danny S., Lanche, Rouel, Maxwell, Evan, Knight, Spencer C., Bai, Xiaodong, Guturu, Harendra, Baltzell, Asher, Girshick, Ahna R., McCurdy, Shannon R., Partha, Raghavendran, Mansfield, Adam J., Turissini, David A., Zhang, Miao, Mbatchou, Joelle, Watanabe, Kyoko, Verma, Anurag, Sirugo, Giorgio, Ritchie, Marylyn D., Salerno, William J., Shuldiner, Alan R., Rader, Daniel J., Mirshahi, Tooraj, Marchini, Jonathan, Overton, John D., Carey, David J., Habegger, Lukas, Reid, Jeffrey G., Economides, Aris, Kyratsous, Christos, Karalis, Katia, Baum, Alina, Cantor, Michael N., Rand, Kristin A., Hong, Eurie L., Ball, Catherine A., Siminovitch, Katherine, Baras, Aris, Abecasis, Goncalo R., and Ferreira, Manuel A. R.

Published
2024
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82. A hot-Jupiter progenitor on a super-eccentric retrograde orbit

Author

Gupta, Arvind F., Millholland, Sarah C., Im, Haedam, Dong, Jiayin, Jackson, Jonathan M., Carleo, Ilaria, Libby-Roberts, Jessica, Delamer, Megan, Giovinazzi, Mark R., Lin, Andrea S. J., Kanodia, Shubham, Wang, Xian-Yu, Stassun, Keivan, Masseron, Thomas, Dragomir, Diana, Mahadevan, Suvrath, Wright, Jason, Alvarado-Montes, Jaime A., Bender, Chad, Blake, Cullen H., Caldwell, Douglas, Cañas, Caleb I., Cochran, William D., Dalba, Paul, Everett, Mark E., Fernandez, Pipa, Golub, Eli, Guillet, Bruno, Halverson, Samuel, Hebb, Leslie, Higuera, Jesus, Huang, Chelsea X., Klusmeyer, Jessica, Knight, Rachel, Leroux, Liouba, Logsdon, Sarah E., Loose, Margaret, McElwain, Michael W., Monson, Andrew, Ninan, Joe P., Nowak, Grzegorz, Palle, Enric, Patel, Yatrik, Pepper, Joshua, Primm, Michael, Rajagopal, Jayadev, Robertson, Paul, Roy, Arpita, Schneider, Donald P., Schwab, Christian, Schweiker, Heidi, Sgro, Lauren, Shimizu, Masao, Simard, Georges, Stefánsson, Guðmundur, Stevens, Daniel J., Villanueva, Steven, Wisniewski, John, Will, Stefan, and Ziegler, Carl

Published
2024
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84. Effects of diazepam on hippocampal blood flow in people at clinical high risk for psychosis

Author

Livingston, Nicholas R., Kiemes, Amanda, Devenyi, Gabriel A., Knight, Samuel, Lukow, Paulina B., Jelen, Luke A., Reilly, Thomas, Dima, Aikaterini, Nettis, Maria Antonietta, Casetta, Cecilia, Agyekum, Tyler, Zelaya, Fernando, Spencer, Thomas, De Micheli, Andrea, Fusar-Poli, Paolo, Grace, Anthony A., Williams, Steve C. R., McGuire, Philip, Egerton, Alice, Chakravarty, M. Mallar, and Modinos, Gemma

Published
2024
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100. Moving Together: Intra- and Intergenerational Dance Performing Citizenship

Author

Brown, Carol, Knight, Linda, Battersby, Clare, and Roche, Jenny

Abstract

In this article, we focus on dance as intra- and intergenerational learning that cultivates corporeal knowledge held in common. Three interrelated projects reveal how danced connections between life stages develops an aesthetics of complex interaction and a mutuality of learning that enacts citizenry through communities in motion in the urban-scape. The projects approach dance practice through an expanded concept of choreography that takes into account the site, situation and life stage of dancers. We propose that moving together is a social choreography, a method for developing body-place awareness and civic participation, sharing experiences and bodily practices and performing relational complexity.

Published
2023

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