Search

Your search keyword '"Palmer A"' showing total 367,733 results
Start Over Author "Palmer A"
367,733 results on '"Palmer A"'

114. Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation

Author

Carey, Caitlin E., Shafee, Rebecca, Wedow, Robbee, Elliott, Amanda, Palmer, Duncan S., Compitello, John, Kanai, Masahiro, Abbott, Liam, Schultz, Patrick, Karczewski, Konrad J., Bryant, Samuel C., Cusick, Caroline M., Churchhouse, Claire, Howrigan, Daniel P., King, Daniel, Davey Smith, George, Neale, Benjamin M., Walters, Raymond K., and Robinson, Elise B.

Published
2024
Full Text
View/download PDF

115. Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression

Author

Abedini, Amin, Levinsohn, Jonathan, Klötzer, Konstantin A., Dumoulin, Bernhard, Ma, Ziyuan, Frederick, Julia, Dhillon, Poonam, Balzer, Michael S., Shrestha, Rojesh, Liu, Hongbo, Vitale, Steven, Bergeson, Andi M., Devalaraja-Narashimha, Kishor, Grandi, Paola, Bhattacharyya, Tanmoy, Hu, Erding, Pullen, Steven S., Boustany-Kari, Carine M., Guarnieri, Paolo, Karihaloo, Anil, Traum, Daniel, Yan, Hanying, Coleman, Kyle, Palmer, Matthew, Sarov-Blat, Lea, Morton, Lori, Hunter, Christopher A., Kaestner, Klaus H., Li, Mingyao, and Susztak, Katalin

Published
2024
Full Text
View/download PDF

123. Co-Constructing Sustainable Collaborations in Early Childhood Settings through the Arts

Author

Palmer, Katherine, Yu, Geralyn Schroeder, and Aprill, Arnold

Abstract

This article reflects the inquiries and discoveries experienced by early childhood educators and teaching artists involved in participatory action research designed to explore how to co-construct sustainable collaborations in early childhood settings. Drawing inspiration from the Reggio Emilia Approach and other international examples, the authors detail how professional development through collaborative research leads to co-constructed experiences that significantly impact artistic encounters for children. This article highlights a two-year research project, named the Leading Learning Council (LLC), and focuses on overarching pedagogical practices, the role of documentation and reflection in the co-construction of arts experiences for educators and children, an example of the classroom as a laboratory, and considerations for applying the model within diverse early childhood settings. A significant outcome of the project was participant recognition of leadership roles in sustaining collaborative arts-based approaches in early childhood education.

Published
2023

124. How to Use Data to Improve Non-Degree Workforce Programs at Community Colleges. The Third in a Three-Part Series from New America's New Models for Career Preparation Project

Author

New America, Iris Palmer, and Shalin Jyotishi

Abstract

Despite the growing demand and availability of non-degree workforce training, outcomes for these programs are mixed. For some, non-degree programs are a faster, more affordable pathway to a good job, and, more importantly, a career that offers economic security--they represent the future of education. But for others, non-degree programs are a hyped-up distraction from degree attainment that leads to unemployment, underemployment, or employment in poverty-wage jobs with limited advancement opportunities--particularly for Black and Brown learners. The New Models for Career Preparation Project aims to help unlock the full potential of non-degree workforce training, especially at public community colleges where these programs are commonly found. This brief is the third in a three-part series that focuses on creating non-degree workforce programs. It describes data collection and data-driven continuous improvement strategies colleges should use regularly.

Published
2023

125. Pediatric Obesity Weight Evaluation Registry (POWER) Study (POWER)

Author

Helen DeVos Children's Hospital, Mayo Clinic, Duke University, National Association of Children's Hospitals and Related Institutions (NACHRI), Milton S. Hershey Medical Center, University of New Mexico, University of Illinois College of Medicine at Peoria, Children's Hospital Los Angeles, Connecticut Children's Medical Center, The Children's Hospital of San Antonio, Ann & Robert H Lurie Children's Hospital of Chicago, Children's Mercy Hospital Kansas City, Arkansas Children's Hospital Research Institute, Florida Hospital for Children, UCSF Benioff Children's Hospital Oakland, University of Oklahoma, MaineHealth, Seattle Children's Hospital, Eastern Maine Medical Center, University of Florida, University of California, Los Angeles, Gramercy Pediatrics, St. Louis Children's Hospital, Dartmouth-Hitchcock Medical Center, University of Alabama at Birmingham, University of Tulsa, Northern Arizona Healthcare, University of Kentucky, Arnold Palmer Hospital for Children, and The Cleveland Clinic

Published
2024

126. Direct $N$-body simulations of satellite formation around small asteroids: insights from DART's encounter with the Didymos system

Author

Agrusa, Harrison F., Zhang, Yun, Richardson, Derek C., Pravec, Petr, Ćuk, Matija, Michel, Patrick, Ballouz, Ronald-Louis, Jacobson, Seth A., Scheeres, Daniel J., Walsh, Kevin, Barnouin, Olivier, Daly, R. Terik, Palmer, Eric, Pajola, Maurizio, Lucchetti, Alice, Tusberti, Filippo, DeMartini, Joseph V., Ferrari, Fabio, Meyer, Alex J., Raducan, Sabina D., and Sánchez, Paul

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

We explore binary asteroid formation by spin-up and rotational disruption considering the NASA DART mission's encounter with the Didymos-Dimorphos binary, which was the first small binary visited by a spacecraft. Using a suite of $N$-body simulations, we follow the gravitational accumulation of a satellite from meter-sized particles following a mass-shedding event from a rapidly rotating primary. The satellite's formation is chaotic, as it undergoes a series of collisions, mergers, and close gravitational encounters with other moonlets, leading to a wide range of outcomes in terms of the satellite's mass, shape, orbit, and rotation state. We find that a Dimorphos-like satellite can form rapidly, in a matter of days, following a realistic mass-shedding event in which only ${\sim}2-3\%$ of the primary's mass is shed. Satellites can form in synchronous rotation due to their formation near the Roche limit. There is a strong preference for forming prolate (elongated) satellites, although some simulations result in oblate spheroids like Dimorphos. The distribution of simulated secondary shapes is broadly consistent with other binary systems, measured through radar or lightcurves. Unless Dimorphos's shape is an outlier, and considering the observational bias against lightcurve-based determination of secondary elongations for oblate bodies, we suggest there could be a significant population of oblate secondaries. If these satellites initially form with elongated shapes, a yet-unidentified pathway is needed to explain how they become oblate. Finally, we show that this chaotic formation pathway occasionally forms asteroid pairs and stable triples, including co-orbital satellites and satellites in mean motion resonances., Comment: 41 pages, 23 figures, accepted to PSJ, movies available at https://zenodo.org/doi/10.5281/zenodo.8387043

Published
2024
Full Text
View/download PDF

127. Reaction Pathway and Rovibrational Analysis of Aluminum Nitride Species as Potential Dust Grain Nucleation Agents

Author

Palmer, C. Zachary and Fortenberry, Ryan C.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract

A dust nucleating agent may be present in interstellar or circumstellar media that has gone seemingly undetected and unstudied for decades. Some analyses of the Murchison CM2 meteorite suggest that at least some of the aluminum present within condensed as aluminum nitrides instead of the long studied, but heretofore undetected suite of aluminum oxides. The present theoretical study utilizes explicitly correlated coupled cluster theory and density functional theory to provide a pathway of formation from alane (AlH$_3$) and ammonia to the cyclic structure, Al$_2$N$_2$H$_4$ which has the proper Al/N ratio expected of bulk aluminum nitrides. Novel rovibrational spectroscopic constants are computed for alane and the first two formed structures, AlNH$_6$ and AlNH$_4$, along the reaction pathway for use as reference in possible laboratory or observational studies. The $\nu_8$ bending frequency for AlNH$_6$ at 755.7 cm$^{-1}$ (13.23 $\mu$m) presents a vibrational transition intensity of 515 km mol$^{-1}$, slightly more intense than the anti-symmetric C$-$O stretch of carbon dioxide, and contains a dipole moment of 5.40 D, which is $\sim 3 \times$ larger than that of water. Thus, the present reaction pathway and rovibrational spectroscopic analysis may potentially assist in the astrophysical detection of novel, inorganic species which may be indicative of larger dust grain nucleation., Comment: 13 pages, 2 figures, 7 tables

Published
2024

128. Stability of Membranes

Author

Palmer, Bennett and Pampano, Alvaro

Subjects
Mathematics - Differential Geometry
Abstract

In [12], the authors studied a particular class of equilibrium solutions of the Helfrich energy which satisfy a second order condition called the reduced membrane equation. In this paper we develop and apply a second variation formula for the Helfrich energy for this class of surfaces. The reduced membrane equation also arises as the Euler-Lagrange equation for the area of surfaces under the action of gravity in the three dimensional hyperbolic space. We study the second variation of this functional for a particular example.

Published
2024

129. FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning

Author

Martínez, Francisco Ardévol, Min, Michiel, Huppenkothen, Daniela, Kamp, Inga, and Palmer, Paul I.

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract

Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between model complexity and run time. Reaching this compromise leads to the simplification of many physical and chemical processes (e.g. parameterised temperature structure). Here we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm, for exoplanet atmospheric retrievals. The goal is to speed up retrievals so they can be run with more computationally expensive atmospheric models, such as those computing the temperature structure using radiative transfer. We generate 100 synthetic observations using ARCiS (ARtful Modeling Code for exoplanet Science, an atmospheric modelling code with the flexibility to compute models in varying degrees of complexity) and perform retrievals on them to test the faithfulness of the SNPE posteriors. The faithfulness quantifies whether the posteriors contain the ground truth as often as we expect. We also generate a synthetic observation of a cool brown dwarf using the self-consistent capabilities of ARCiS and run a retrieval with self-consistent models to showcase the possibilities that SNPE opens. We find that SNPE provides faithful posteriors and is therefore a reliable tool for exoplanet atmospheric retrievals. We are able to run a self-consistent retrieval of a synthetic brown dwarf spectrum using only 50,000 forward model evaluations. We find that SNPE can speed up retrievals between $\sim2\times$ and $\geq10\times$ depending on the computational load of the forward model, the dimensionality of the observation, and the signal-to-noise ratio of the observation. We make the code publicly available for the community on Github., Comment: Accepted for publication at A&A

Published
2024

130. A Rapid Review of Interventions to Improve Care for People Who Are Medically Underserved with Multiple Sclerosis, Diabetic Retinopathy, and Lung Cancer.

Author

Mossburg, Sarah, Kilany, Mona, Nguyen, Charlene, Soles, Elena, Wood-Palmer, Drew, Aly, Marwa, and Jinnett, Kimberly

Subjects
interventions, medically underserved, rapid review, Humans, Multiple Sclerosis, Lung Neoplasms, Diabetic Retinopathy, United States, Healthcare Disparities, Health Services Accessibility
Abstract

In the United States, patients with chronic conditions experience disparities in health outcomes across the care continuum. Among patients with multiple sclerosis, diabetic retinopathy, and lung cancer, there is a lack of evidence summarizing interventions to improve care and decrease these disparities. The aim of this rapid literature review was to identify interventions among patients with these chronic conditions to improve health and reduce disparities in screening, diagnosis, access to treatment and specialists, adherence, and retention in care. Using structured search terms in PubMed and Web of Science, we completed a rapid review of studies published in the prior five years conducted in the United States on our subject of focus. We screened the retrieved articles for inclusion and extracted data using a standard spreadsheet. The data were synthesized across clinical conditions and summarized. Screening was the most common point in the care continuum with documented interventions. Most studies we identified addressed interventions for patients with lung cancer, with half as many studies identified for patients with diabetic retinopathy, and few studies identified for patients with multiple sclerosis. Almost two-thirds of the studies focused on patients who identify as Black, Indigenous, or people of color. Interventions with evidence evaluating implementation in multiple conditions included telemedicine, mobile clinics, and insurance subsidies, or expansion. Despite documented disparities and a focus on health equity, a paucity of evidence exists on interventions that improve health outcomes among patients who are medically underserved with multiple sclerosis, diabetic retinopathy, and lung cancer.

Published
2024

131. ALOX15B controls macrophage cholesterol homeostasis via lipid peroxidation, ERK1/2 and SREBP2.

Author

Benatzy, Yvonne, Palmer, Megan, Lütjohann, Dieter, Ohno, Rei-Ichi, Kampschulte, Nadja, Schebb, Nils, Fuhrmann, Dominik, Snodgrass, Ryan, and Brüne, Bernhard

Subjects
15-LO2, Arachidonate 15-lipoxygenase type B, Lipid peroxidation, MAPK, Reactive oxygen species, Sterol regulatory element-binding protein 2
Abstract

Macrophage cholesterol homeostasis is crucial for health and disease and has been linked to the lipid-peroxidizing enzyme arachidonate 15-lipoxygenase type B (ALOX15B), albeit molecular mechanisms remain obscure. We performed global transcriptome and immunofluorescence analysis in ALOX15B-silenced primary human macrophages and observed a reduction of nuclear sterol regulatory element-binding protein (SREBP) 2, the master transcription factor of cellular cholesterol biosynthesis. Consequently, SREBP2-target gene expression was reduced as were the sterol biosynthetic intermediates desmosterol and lathosterol as well as 25- and 27-hydroxycholesterol. Mechanistically, suppression of ALOX15B reduced lipid peroxidation in primary human macrophages and thereby attenuated activation of mitogen-activated protein kinase ERK1/2, which lowered SREBP2 abundance and activity. Low nuclear SREBP2 rendered both, ALOX15B-silenced and ERK1/2-inhibited macrophages refractory to SREBP2 activation upon blocking the NPC intracellular cholesterol transporter 1. These studies suggest a regulatory mechanism controlling macrophage cholesterol homeostasis based on ALOX15B-mediated lipid peroxidation and concomitant ERK1/2 activation.

Published
2024

132. A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

Author

de Jong, Tristan V, Pan, Yanchao, Rastas, Pasi, Munro, Daniel, Tutaj, Monika, Akil, Huda, Benner, Chris, Chen, Denghui, Chitre, Apurva S, Chow, William, Colonna, Vincenza, Dalgard, Clifton L, Demos, Wendy M, Doris, Peter A, Garrison, Erik, Geurts, Aron M, Gunturkun, Hakan M, Guryev, Victor, Hourlier, Thibaut, Howe, Kerstin, Huang, Jun, Kalbfleisch, Ted, Kim, Panjun, Li, Ling, Mahaffey, Spencer, Martin, Fergal J, Mohammadi, Pejman, Ozel, Ayse Bilge, Polesskaya, Oksana, Pravenec, Michal, Prins, Pjotr, Sebat, Jonathan, Smith, Jennifer R, Woods, Leah C Solberg, Tabakoff, Boris, Tracey, Alan, Uliano-Silva, Marcela, Villani, Flavia, Wang, Hongyang, Sharp, Burt M, Telese, Francesca, Jiang, Zhihua, Saba, Laura, Wang, Xusheng, Murphy, Terence D, Palmer, Abraham A, Kwitek, Anne E, Dwinell, Melinda R, Williams, Robert W, Li, Jun Z, and Chen, Hao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Biotechnology, Good Health and Well Being, Rats, Animals, Genome, Molecular Sequence Annotation, Genomics, Whole Genome Sequencing, Genetic Variation, Rnor_6.0, genetic map, heterogeneous stock, hybrid rat diversity panel, inbred strains, mRatBN7.2, phylogenetic tree, rat, recombinant inbred, reference genome
Abstract

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Published
2024

133. Phase 2 study of add-on parsaclisib for patients with myelofibrosis and suboptimal response to ruxolitinib: final results.

Author

Yacoub, Abdulraheem, Borate, Uma, Rampal, Raajit, Ali, Haris, Wang, Eunice, Gerds, Aaron, Hobbs, Gabriela, Kremyanskaya, Marina, Winton, Elliott, OConnell, Casey, Goel, Swati, Oh, Stephen, Schiller, Gary, McCloskey, James, Palmer, Jeanne, Holmes, Houston, Hager, Steven, Assad, Albert, Erickson-Viitanen, Susan, Zhou, Feng, and Daver, Naval

Subjects
Humans, Primary Myelofibrosis, Phosphatidylinositol 3-Kinases, Pyrazoles, Nitriles, Pyrimidines, Pyrrolidines
Abstract

Ruxolitinib reduces spleen volume, improves symptoms, and increases survival in patients with intermediate- or high-risk myelofibrosis. However, suboptimal response may occur, potentially because of signaling via the phosphoinositide 3-kinase (PI3K)/protein kinase B pathway. This phase 2 study evaluated dosing, efficacy, and safety of add-on PI3Kδ inhibitor parsaclisib for patients with primary or secondary myelofibrosis with suboptimal response to ruxolitinib. Eligible patients remained on a stable ruxolitinib dose and received add-on parsaclisib 10 or 20 mg, once daily for 8 weeks, and once weekly thereafter (daily-to-weekly dosing; n = 32); or parsaclisib 5 or 20 mg, once daily for 8 weeks, then 5 mg once daily thereafter (all-daily dosing; n = 42). Proportion of patients achieving a ≥10% decrease in spleen volume at 12 weeks was 28% for daily-to-weekly dosing and 59.5% for all-daily dosing. Proportions of patients achieving ≥50% decrease at week 12 in Myelofibrosis Symptom Assessment Form and Myeloproliferative Neoplasms Symptom Assessment Form symptom scores were 14% and 18% for daily-to-weekly dosing, and 28% and 32% for all-daily dosing, respectively. Most common nonhematologic treatment-emergent adverse events were nausea (23%), diarrhea (22%), abdominal pain and fatigue (each 19%), and cough and dyspnea (each 18%). New-onset grade 3 and 4 thrombocytopenia were observed in 19% of patients, each dosed daily-to-weekly, and in 26% and 7% of patients dosed all-daily, respectively, managed with dose interruptions. Hemoglobin levels remained steady. The addition of parsaclisib to stable-dose ruxolitinib can reduce splenomegaly and improve symptoms, with manageable toxicity in patients with myelofibrosis with suboptimal response to ruxolitinib. This trial was registered at www.clinicaltrials.gov as #NCT02718300.

Published
2024

134. Mapping human tissues with highly multiplexed RNA in situ hybridization.

Author

Kalhor, Kian, Chen, Chien-Ju, Lee, Ho, Cai, Matthew, Nafisi, Mahsa, Que, Richard, Palmer, Carter, Yuan, Yixu, Zhang, Yida, Li, Xuwen, Song, Jinghui, Knoten, Amanda, Lake, Blue, Gaut, Joseph, Keene, C, Lein, Ed, Kharchenko, Peter, Chun, Jerold, Jain, Sanjay, Fan, Jian-Bing, and Zhang, Kun

Subjects
Humans, RNA, In Situ Hybridization, Gene Expression Profiling, Transcriptome, Cytosol
Abstract

In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. There has been a surge of multiplexed RNA in situ mapping techniques but their application to human tissues has been limited due to their large size, general lower tissue quality and high autofluorescence. Here we report DART-FISH, a padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections. We introduce an omni-cell type cytoplasmic stain that substantially improves the segmentation of cell bodies. Our enzyme-free isothermal decoding procedure allows us to image 121 genes in large sections from the human neocortex in 20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.

Published
2024

135. Genomic malaria surveillance of antenatal care users detects reduced transmission following elimination interventions in Mozambique.

Author

Brokhattingen, Nanna, Matambisso, Glória, da Silva, Clemente, Neubauer Vickers, Eric, Pujol, Arnau, Mbeve, Henriques, Cisteró, Pau, Maculuve, Sónia, Cuna, Boaventura, Melembe, Cardoso, Ndimande, Nelo, Palmer, Brian, García-Ulloa, Manuel, Munguambe, Humberto, Montaña-Lopez, Júlia, Nhamussua, Lidia, Simone, Wilson, Chidimatembue, Arlindo, Galatas, Beatriz, Guinovart, Caterina, Rovira-Vallbona, Eduard, Saúte, Francisco, Aide, Pedro, Aranda-Díaz, Andrés, Macete, Eusébio, Mayor, Alfredo, and Greenhouse, Bryan

Subjects
Child, Animals, Female, Pregnancy, Humans, Prenatal Care, Mozambique, Malaria, Plasmodium falciparum, Parasites, Genomics, Malaria, Falciparum
Abstract

Routine sampling of pregnant women at first antenatal care (ANC) visits could make Plasmodium falciparum genomic surveillance more cost-efficient and convenient in sub-Saharan Africa. We compare the genetic structure of parasite populations sampled from 289 first ANC users and 93 children from the community in Mozambique between 2015 and 2019. Samples are amplicon sequenced targeting 165 microhaplotypes and 15 drug resistance genes. Metrics of genetic diversity and relatedness, as well as the prevalence of drug resistance markers, are consistent between the two populations. In an area targeted for elimination, intra-host genetic diversity declines in both populations (p = 0.002-0.007), while for the ANC population, population genetic diversity is also lower (p = 0.0004), and genetic relatedness between infections is higher (p = 0.002) than control areas, indicating a recent reduction in the parasite population size. These results highlight the added value of genomic surveillance at ANC clinics to inform about changes in transmission beyond epidemiological data.

Published
2024

136. Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.

Author

Batista, Sandra, Madar, Vered, Freda, Philip, Bhandary, Priyanka, Ghosh, Attri, Matsumoto, Nicholas, Chitre, Apurva, Palmer, Abraham, and Moore, Jason

Subjects
Epistasis, GWAS, Interaction model, Linear regression, Partial correlation, XOR
Abstract

PURPOSE: Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable. METHODS: We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index. RESULTS: This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways. CONCLUSION: Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Published
2024

137. Phase 1/2 study of monalizumab plus durvalumab in patients with advanced solid tumors.

Author

Patel, Sandip, Alonso-Gordoa, Teresa, Banerjee, Susana, Wang, Ding, Naidoo, Jarushka, Standifer, Nathan, Palmer, Doug, Cheng, Lin-Yang, Kourtesis, Panagiotis, Ascierto, Maria, Das, Mayukh, Diamond, Jennifer, Hellmann, Matthew, and Carneiro, Benedito

Subjects
Adaptive Immunity, Immunity, Innate, Natural Killer T-Cells, Programmed Cell Death 1 Receptor, Tumor Microenvironment, Female, Humans, Adolescent, Adult, Carcinoma, Non-Small-Cell Lung, Ligands, Lung Neoplasms, Tumor Microenvironment, Antibodies, Monoclonal, Antibodies, Monoclonal, Humanized
Abstract

BACKGROUND: The combination of monalizumab (anti-NKG2A/CD94) and durvalumab (anti-programmed death ligand-1) may promote antitumor immunity by targeting innate and adaptive immunity. This phase 1/2 study of monalizumab and durvalumab evaluated safety, antitumor activity, and pharmacodynamics in patients with advanced solid tumors. MAIN BODY: Immunotherapy-naïve patients aged ≥18 years with advanced disease, Eastern Cooperative Oncology Group performance status of 0-1, and 1-3 prior lines of systemic therapy in the recurrent/metastatic setting were enrolled. In part 1 (dose escalation), patients received durvalumab 1500 mg every 4 weeks (Q4W) with increasing doses of monalizumab Q2W/Q4W (n=15). Dose expansion in part 1 included patients with cervical cancer (n=15; durvalumab 1500 mg Q4W and monalizumab 750 mg Q2W) or metastatic microsatellite stable (MSS)-colorectal cancer (CRC) (n=15; durvalumab 1500 mg Q4W and monalizumab 750 mg Q4W). In part 2 (dose expansion), patients with MSS-CRC (n=40), non-small cell lung cancer (NSCLC; n=20), MSS-endometrial cancer (n=40), or ovarian cancer (n=40) received durvalumab 1500 mg Q4W and monalizumab 750 mg Q2W. The primary endpoint was safety. Secondary endpoints included antitumor activity per Response Evaluation Criteria In Solid Tumors version 1.1 (RECIST v1.1). Exploratory analyses included assessment of T-cell and natural killer (NK) cell activation and proliferation in peripheral blood and the tumor microenvironment (TME). The study enrolled 185 patients (part 1, 45; part 2, 140). No dose-limiting toxicities were observed and the maximum tolerated dose was not reached. In part 2, the most common treatment-related adverse events were fatigue (12.1%), asthenia (9.3%), diarrhea (9.3%), pruritus (7.9%), and pyrexia (7.1%). In the expansion cohorts, response rates were 0% (cervical), 7.7% (MSS-CRC), 10% (NSCLC), 5.4% (ovarian), and 0% (MSS-endometrial). Sustained NK cell activation, CD8+ T-cell proliferation, increased serum levels of CXCL10 (C-X-C motif chemokine ligand 10) and CXCL11, and increased tumor infiltration of CD8+ and granzyme B+ cells were observed. CONCLUSIONS: Although efficacy was modest, monalizumab plus durvalumab was well tolerated and encouraging immune activation was observed in the peripheral blood and TME. TRIAL REGISTRATION NUMBER: NCT02671435.

Published
2024

138. Evaluation of the Xpert MTB Host Response assay for the triage of patients with presumed pulmonary tuberculosis: a prospective diagnostic accuracy study in Viet Nam, India, the Philippines, Uganda, and South Africa

Author

Gupta-Wright, Ankur, Ha, Huy, Abdulgadar, Shima, Crowder, Rebecca, Emmanuel, Jerusha, Mukwatamundu, Job, Marcelo, Danaida, Phillips, Patrick PJ, Christopher, Devasahayam Jesudas, Nhung, Nguyen Viet, Theron, Grant, Yu, Charles, Nahid, Payam, Cattamanchi, Adithya, Worodria, William, Denkinger, Claudia M, Network, R2D2 TB, Thangakunam, Balamugesh, Shankar, Deepa, Ernest, Vinita, John, Flavita, Karthikeyan, Bharath, Sekar, Reena, Mangal, Divya, Vijayasree, Sai, Sankar, Swetha, Shibiya, Mary, Gajendran, Priyadarshini, Elango, Shanmugasundaram, Sekar, Rajasekar, Almonte, Jared, Basilio, Ramon, Cariaga, Asella Ruvijean, Destura, Raul, Dalay, Victoria, Dayawon, Karlo, Gelina, Darecil, Goleña, Joseph Aldwin, Golla, Maria Marissa, Ilagan, Gidalthi Jonathan, Lim, Dodge, Pabruada, Angelita, Reyes, Annalyn, Reyes, Roeus Vincent Arjay G, Tonquin, Maricef, Derendinger, Brigitta, Hendrikse, Megan, Okunola, Anna, Palmer, Zaida, Andama, Alfred, Kisakye, Esther, Mwebe, Sandra, Nakaye, Martha, Nyawere, Justine, Bukirwa, Alice, Mangeni, Wilson, Ssonko, John Baptist, Nakaweesa, Annet, Nassuna, Irene, Nekesa, Irene, Katumba, David, Asege, Lucy, Nalugwa, Talemwa, Dang, Hai, Dinh, Luong, Doan, Thien, Do, Hang, Do, Tam, Le, Hien, Le, Nguyet, Nguyen, Anh, Nguyen, Dong, Nguyen, Hanh, Nguyen, Hoang, Nguyen, Thanh, Pham, Nam, Pham, Thuong, Phan, Ha, Trinh, Trang, Castro, Robert, Cook, Catherine, Mochizuki, Tessa, Kato-Maeda, Midori, Nathavitharana, Ruvandhi, Nolan, Kevin, Olaru, Ioana-Diana, del Mar Castro Noriega, Maria, and Yerlikaya, Seda

Subjects
Epidemiology, Health Services and Systems, Public Health, Health Sciences, Tuberculosis, Infectious Diseases, Clinical Research, HIV/AIDS, Rare Diseases, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Infection, Good Health and Well Being, Adult, Female, Humans, Male, Cough, HIV Seropositivity, India, Mycobacterium tuberculosis, Philippines, Prospective Studies, Sensitivity and Specificity, South Africa, Sputum, Triage, Tuberculosis, Pulmonary, Uganda, Vietnam, R2D2 TB Network, Microbiology, Public Health and Health Services, Health services and systems, Public health
Abstract

BackgroundNon-sputum-based triage tests for tuberculosis are a priority for ending tuberculosis. We aimed to evaluate the diagnostic accuracy of the late-prototype Xpert MTB Host Response (Xpert HR) blood-based assay.MethodsWe conducted a prospective diagnostic accuracy study among outpatients with presumed tuberculosis in outpatient clinics in Viet Nam, India, the Philippines, Uganda, and South Africa. Eligible participants were aged 18 years or older and reported cough lasting at least 2 weeks. We excluded those receiving tuberculosis treatment in the preceding 12 months and those who were unwilling to consent. Xpert HR was performed on capillary or venous blood. Reference standard testing included sputum Xpert MTB/RIF Ultra and mycobacterial culture. We performed receiver operating characteristic (ROC) analysis to identify the optimal cutoff value for the Xpert HR to achieve the target sensitivity of 90% or more while maximising specificity, then calculated diagnostic accuracy using this cutoff value. This study was prospectively registered with ClinicalTrials.gov, NCT04923958.FindingsBetween July 13, 2021, and Aug 15, 2022, 2046 adults with at least 2 weeks of cough were identified, of whom 1499 adults (686 [45·8%] females and 813 [54·2%] males) had valid Xpert HR and reference standard results. 329 (21·9%) had microbiologically confirmed tuberculosis. Xpert HR had an area under the ROC curve of 0·89 (95% CI 0·86-0·91). The optimal cutoff value was less than or equal to -1·25, giving a sensitivity of 90·3% (95% CI 86·5-93·3; 297 of 329) and a specificity of 62·6% (95% CI 59·7-65·3; 732 of 1170). Sensitivity was similar across countries, by sex, and by subgroups, although specificity was lower in people living with HIV (45·1%, 95% CI 37·8-52·6) than in those not living with HIV (65·9%, 62·8-68·8; difference of 20·8%, 95% CI 13·0-28·6; p

Published
2024

139. FEMA: Fast and efficient mixed‐effects algorithm for large sample whole‐brain imaging data

Author

Parekh, Pravesh, Fan, Chun Chieh, Frei, Oleksandr, Palmer, Clare E, Smith, Diana M, Makowski, Carolina, Iversen, John R, Pecheva, Diliana, Holland, Dominic, Loughnan, Robert, Nedelec, Pierre, Thompson, Wesley K, Hagler, Donald J, Andreassen, Ole A, Jernigan, Terry L, Nichols, Thomas E, and Dale, Anders M

Subjects
Biological Psychology, Psychology, Pediatric, Bioengineering, Neurosciences, Biomedical Imaging, Underpinning research, 1.1 Normal biological development and functioning, Mental health, Neurological, Adolescent, Humans, Magnetic Resonance Imaging, Cross-Sectional Studies, Brain, Neuroimaging, Connectome, Algorithms, ABCD, longitudinal analysis, mixed models, vertex-wise, voxel-wise, whole brain, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Published
2024

140. Clinicopathologic Features of Antibrush Border Antibody Disease.

Author

Murphy, Joel, Caza, Tiffany, Cassol, Clarissa, Storey, Aaron, Ambruzs, Josephine, Boils, Christie, Walker, Patrick, Sharma, Shree, Messias, Nidia, Hennigar, Randolph, Andeen, Nicole, VanBeek, Christine, Palmer, Matthew, Sankar, Lakshna, Sanghi, Pooja, Dinesh, Kumar, Dicker, Lance, Urisman, Anatoly, and Larsen, Christopher

Subjects
ABBA, AMN, CUBN, LRP2, antibrush border antibody disease, kidney biopsy
Abstract

INTRODUCTION: Antibrush border antibody disease (ABBA) is an autoimmune tubulointerstitial kidney disease that primarily affects older individuals and results in progressive kidney failure. It is rare with only 20 reported cases. Here, we describe a case series to further define the clinicopathologic spectrum and natural history, and to inform management. METHODS: We identified 67 patients with ABBA who underwent kidney biopsy, including 65 native and 2 transplants. Demographics, clinical findings, and laboratory data were obtained. Histopathologic data included light microscopy, immunofluorescence, electron microscopy and immunostaining for LRP2, CUBN, and AMN. Follow-up data, including treatment(s), laboratory values, and outcomes, were available from 51 patients. RESULTS: Patients with ABBA were predominantly male with a median age of 72 years. Median serum creatinine was 2.7 mg/dl, proteinuria was 2.8 g/day, and hematuria was present in two-thirds of the patients. Tubular injury with LRP2-positive tubular basement membrane (TBM) deposits were seen in 94.2% of patients. Thirty-eight patients (56.7%) had a second kidney disease, commonly glomerular diseases with high-grade proteinuria. These diseases included podocytopathies, membranous nephropathy (MN), IgA nephropathy, diabetic glomerulopathy, lupus nephritis (LN), crescentic glomerulonephritis (GN), tubulointerstitial nephritis, and involvement by lymphoma. The majority of patients were treated with immunosuppression. Of those patients with follow-up, 29.4% achieved remission, 70.6% had no response, and 52.8% required dialysis or were deceased. Untreated patients were at the highest risk. CONCLUSION: ABBA is a rare autoimmune kidney disease that often occurs with other kidney diseases. Although the overall prognosis of ABBA is poor, there is potential benefit from immunosuppression.

Published
2024

141. Profiles of a COVID-19 Syndemic: Anti-Asian Racism, Economic Challenges, and Mental and Physical Health

Author

McGarity-Palmer, Rebecca, Saw, Anne, Horse, Aggie J Yellow, Yi, Stella S, Tsoh, Janice, and Takeuchi, David

Subjects
Public Health, Health Sciences, Prevention, Behavioral and Social Science, Good Health and Well Being, Humans, COVID-19, Asian, Racism, Syndemic, Pandemics, Asian Americans, Health status, Financial burden, Public Health and Health Services, Public health
Abstract

BackgroundDuring the COVID-19 pandemic, Asians/Asian Americans have experienced co-occurring threats of anti-Asian racism, economic challenges, and negative mental and physical health symptoms.ObjectivesWe examined the co-occurrence of COVID-19-related anti-Asian discrimination and collective racism, economic stressors, and mental and physical health challenges for Asians/Asian Americans during the COVID-19 pandemic. We also examined Asian/Asian American subgroups associated with these threats.MethodsNationally representative data from the 2021 Asian American and Native Hawaiian/Pacific Islander COVID-19 Needs Assessment Project (unweighted N = 3,508) were used to conduct a latent profile analysis to identify unique typologies of the co-occurrence of these threats. We also conducted chi-square analyses to investigate subgroup differences by latent profile.ResultsWe identified five distinct latent profiles: multi-threat impact, low impact, collective racism, health challenges, and economic/health challenges. Forty percent of Asians/Asian Americans were in the multi-threat impact profile, indicating high levels across COVID-19-related threats. Subgroup analyses revealed significant differences in profile membership. East Asians, US-born Asians/Asian Americans, and those aged 25-44 seemed to be particularly affected by the proposed syndemic; results also differed by income.ConclusionAsians/Asian Americans have experienced co-occurring and interrelated threats during COVID-19 that suggest the presence of a syndemic. Results from our study point to vulnerable Asian/Asian American subgroups and the need for targeted public health efforts to address racism, health challenges, and economic challenges in the context of COVID-19.

Published
2024

142. Trends in Racial Discrimination Experiences for Asian Americans During the COVID-19 Pandemic

Author

McGarity-Palmer, Rebecca, Saw, Anne, Tsoh, Janice Y, and Yellow Horse, Aggie J

Subjects
Public Health, Health Sciences, Health Disparities, Coronaviruses, Clinical Research, Social Determinants of Health, Minority Health, Basic Behavioral and Social Science, Prevention, Infectious Diseases, Emerging Infectious Diseases, Brain Disorders, Behavioral and Social Science, Mental Health, Coronaviruses Disparities and At-Risk Populations, Mental health, Good Health and Well Being, Adult, Humans, Female, Racism, Asian, COVID-19, Pandemics, Asian People, Asian American, Physical health, Anti-Asian racism, Public Health and Health Services, Public health
Abstract

BackgroundAsian Americans (AAs) are experiencing increased rates of anti-Asian racism during COVID-19. Experiences of racism, whether personal or collective, constitute stress and psychosocial trauma that negatively impact mental and physical health.ObjectivesExamine subgroup differences in rates of personal experience of discrimination and COVID-related collective racism and how each is associated with mental and physical health for AAs.MethodsNationally representative data from the 2021 Asian American and Native Hawaiian/Pacific Islander COVID-19 Needs Assessment Project were used to estimate prevalence rates of discrimination and average COVID-related collective racism scores for AAs (unweighted N = 3478). We conducted logistic and linear regression models to examine subgroup differences by sociodemographic factors. We also conducted hierarchical logistic regression models to examine associations between racism and psychological distress and health decline.ResultsTwenty-four percent of AAs (95% CI: 21.6, 25.6) reported experiencing discrimination during the first year of the COVID-19 pandemic. Subgroup analyses revealed that Chinese, younger adults, and AAs who completed the survey in an Asian language were significantly more likely to experience discrimination compared to their counterparts. For COVID-related collective racism, subgroup analyses revealed that Chinese, women, and adults ages 25-44 were more likely to report experiences of collective racism compared to their counterparts. Both discrimination and collective racism were independently associated with negative mental and physical health.ConclusionDiscrimination and COVID-related collective racism are associated with negative mental and physical health outcomes for AAs. Results point to vulnerable AA subgroups and the need for targeted public health efforts to address racism in the context of COVID-19.

Published
2024

143. Tensor Networks for Explainable Machine Learning in Cybersecurity

Author

Aizpurua, Borja, Palmer, Samuel, and Orus, Roman

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

In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it in the context of a real use-case of adversary-generated threat intelligence. Our investigation proves that MPS rival traditional deep learning models such as autoencoders and GANs in terms of performance, while providing much richer model interpretability. Our approach naturally facilitates the extraction of feature-wise probabilities, Von Neumann Entropy, and mutual information, offering a compelling narrative for classification of anomalies and fostering an unprecedented level of transparency and interpretability, something fundamental to understand the rationale behind artificial intelligence decisions., Comment: 9 pages, 9 figures, 2 table, minor typos corrected

Published
2023

144. Exact minimax entropy models of large-scale neuronal activity

Author

Lynn, Christopher W., Yu, Qiwei, Pang, Rich, Palmer, Stephanie E., and Bialek, William

Subjects
Physics - Biological Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Quantitative Biology - Neurons and Cognition
Abstract

In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamental bottleneck: the number of correlations one would like to include in a model grows larger than the available data. In this undersampled regime, one must focus on a sparse subset of correlations; the optimal choice contains the maximum information about patterns of activity or, equivalently, minimizes the entropy of the inferred maximum entropy model. Applying this ``minimax entropy" principle is generally intractable, but here we present an exact and scalable solution for pairwise correlations that combine to form a tree (a network without loops). Applying our method to over one thousand neurons in the mouse hippocampus, we find that the optimal tree of correlations reduces our uncertainty about the population activity by 14% (over 50 times more than a random tree). Despite containing only 0.1% of all pairwise correlations, this minimax entropy model accurately predicts the observed large-scale synchrony in neural activity and becomes even more accurate as the population grows. The inferred Ising model is almost entirely ferromagnetic (with positive interactions) and exhibits signatures of thermodynamic criticality. These results suggest that a sparse backbone of excitatory interactions may play an important role in driving collective neuronal activity., Comment: 15 pages, 11 figures

Published
2023

145. 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

146. RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor

Author

Nguyen, Khanh Duy, Zhang, Zixuan, Suchocki, Reece, Li, Sha, Palmer, Martha, Brown, Susan, Han, Jiawei, and Ji, Heng

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

In this paper, we present RESIN-EDITOR, an interactive event graph visualizer and editor designed for analyzing complex events. Our RESIN-EDITOR system allows users to render and freely edit hierarchical event graphs extracted from multimedia and multi-document news clusters with guidance from human-curated event schemas. RESIN-EDITOR's unique features include hierarchical graph visualization, comprehensive source tracing, and interactive user editing, which is more powerful and versatile than existing Information Extraction (IE) visualization tools. In our evaluation of RESIN-EDITOR, we demonstrate ways in which our tool is effective in understanding complex events and enhancing system performance. The source code, a video demonstration, and a live website for RESIN-EDITOR have been made publicly available., Comment: The first two authors contribute equally to this paper

Published
2023

147. First Constraints on WIMP-Nucleon Effective Field Theory Couplings in an Extended Energy Region From LUX-ZEPLIN

Author

LZ Collaboration, Aalbers, J., Akerib, D. S., Musalhi, A. K. Al, Alder, F., Amarasinghe, C. S., Ames, A., Anderson, T. J., Angelides, N., Araújo, H. M., Armstrong, J. E., Arthurs, M., Baker, A., Balashov, S., Bang, J., Bargemann, J. W., Baxter, A., Beattie, K., Benson, T., Bhatti, A., Biekert, A., Biesiadzinski, T. P., Birch, H. J., Bishop, E., Blockinger, G. M., Boxer, B., Brew, C. A. J., Brás, P., Burdin, S., Buuck, M., Carmona-Benitez, M. C., Carter, M., Chawla, A., Chen, H., Cherwinka, J. J., Chott, N. I., Converse, M. V., Cottle, A., Cox, G., Curran, D., Dahl, C. E., David, A., Delgaudio, J., Dey, S., de Viveiros, L., Ding, C., Dobson, J. E. Y., Druszkiewicz, E., Eriksen, S. R., Fan, A., Fearon, N. M., Fiorucci, S., Flaecher, H., Fraser, E. D., Fruth, T. M. A., Gaitskell, R. J., Geffre, A., Genovesi, J., Ghag, C., Gibbons, R., Gokhale, S., Green, J., van der Grinten, M. G. D., Hall, C. R., Han, S., Hartigan-O'Connor, E., Haselschwardt, S. J., Hertel, S. A., Heuermann, G., Homenides, G. J., Horn, M., Huang, D. Q., Hunt, D., Ignarra, C. M., Jacquet, E., James, R. S., Johnson, J., Kaboth, A. C., Kamaha, A. C., Khaitan, D., Khazov, A., Khurana, I., Kim, J., Kingston, J., Kirk, R., Kodroff, D., Korley, L., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., Krikler, B., Kudryavtsev, V. A., Lee, J., Leonard, D. S., Lesko, K. T., Levy, C., Lin, J., Lindote, A., Linehan, R., Lippincott, W. H., Lopes, M. I., Asamar, E. Lopez, Lorenzon, W., Lu, C., Luitz, S., Majewski, P. A., Manalaysay, A., Mannino, R. L., Maupin, C., McCarthy, M. E., McDowell, G., McKinsey, D. N., McLaughlin, J., Miller, E. H., Mizrachi, E., Monte, A., Monzani, M. E., Mendoza, J. D. Morales, Morrison, E., Mount, B. J., Murdy, M., Murphy, A. St. J., Naylor, A., Nedlik, C., Nelson, H. N., Neves, F., Nguyen, A., Nikoleyczik, J. A., Olcina, I., Oliver-Mallory, K. C., Orpwood, J., Palladino, K. J., Palmer, J., Pannifer, N., Parveen, N., Patton, S. J., Penning, B., Pereira, G., Perry, E., Pershing, T., Piepke, A., Qie, Y., Reichenbacher, J., Rhyne, C. A., Riffard, Q., Rischbieter, G. R. C., Riyat, H. S., Rosero, R., Rushton, T., Rynders, D., Santone, D., Sazzad, A. B. M. R., Schnee, R. W., Shaw, S., Shutt, T., Silk, J. J., Silva, C., Sinev, G., Smith, R., Solovov, V. N., Sorensen, P., Soria, J., Stancu, I., Stevens, A., Stifter, K., Suerfu, B., Sumner, T. J., Szydagis, M., Taylor, W. C., Tiedt, D. R., Timalsina, M., Tong, Z., Tovey, D. R., Tranter, J., Trask, M., Tripathi, M., Tronstad, D. R., Turner, W., Vacheret, A., Vaitkus, A. C., Velan, V., Wang, A., Wang, J. J., Wang, Y., Watson, J. R., Webb, R. C., Weeldreyer, L., Whitis, T. J., Williams, M., Wisniewski, W. J., Wolfs, F. L. H., Woodford, S., Woodward, D., Wright, C. J., Xia, Q., Xiang, X., Xu, J., Yeh, M., and Zweig, E. A.

Subjects
High Energy Physics - Experiment, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Following the first science results of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time projection chamber operating from the Sanford Underground Research Facility in Lead, South Dakota, USA, we report the initial limits on a model-independent non-relativistic effective field theory describing the complete set of possible interactions of a weakly interacting massive particle (WIMP) with a nucleon. These results utilize the same 5.5 t fiducial mass and 60 live days of exposure collected for the LZ spin-independent and spin-dependent analyses while extending the upper limit of the energy region of interest by a factor of 7.5 to 270 keVnr. No significant excess in this high energy region is observed. Using a profile-likelihood ratio analysis, we report 90% confidence level exclusion limits on the coupling of each individual non-relativistic WIMP-nucleon operator for both elastic and inelastic interactions in the isoscalar and isovector bases., Comment: 17 pages 11 figures

Published
2023
Full Text
View/download PDF

148. Low-rank longitudinal factor regression

Author

Palmer, Glenn, Herring, Amy H., and Dunson, David B.

Subjects
Statistics - Applications
Abstract

Developmental epidemiology commonly focuses on assessing the association between multiple early life exposures and childhood health. Statistical analyses of data from such studies focus on inferring the contributions of individual exposures, while also characterizing time-varying and interacting effects. Such inferences are made more challenging by correlations among exposures, nonlinearity, and the curse of dimensionality. Motivated by studying the effects of prenatal bisphenol A (BPA) and phthalate exposures on glucose metabolism in adolescence using data from the ELEMENT study, we propose a low-rank longitudinal factor regression (LowFR) model for tractable inference on flexible longitudinal exposure effects. LowFR handles highly-correlated exposures using a Bayesian dynamic factor model, which is fit jointly with a health outcome via a novel factor regression approach. The model collapses on simpler and intuitive submodels when appropriate, while expanding to allow considerable flexibility in time-varying and interaction effects when supported by the data. After demonstrating LowFR's effectiveness in simulations, we use it to analyze the ELEMENT data and find that diethyl and dibutyl phthalate metabolite levels in trimesters 1 and 2 are associated with altered glucose metabolism in adolescence.

Published
2023

149. Multi-indication evidence synthesis in oncology health technology assessment

Author

Singh, Janharpreet, Anwer, Sumayya, Palmer, Stephen, Saramago, Pedro, Thomas, Anne, Dias, Sofia, Soares, Marta, and Bujkiewicz, Sylwia

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Background: Cancer drugs receive licensing extensions to include additional indications as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences supporting Health Technology Assessment (HTA). Methods: We applied meta-analytic methods to randomised trial data on bevacizumab to share information across cancer indications on the treatment effect on overall survival (OS) or progression-free survival (PFS), and on the surrogate relationship between effects on PFS and OS. Common or random parameters were used to facilitate sharing and the further flexibility of mixture models was explored. Results: OS treatment effects lacked precision when pooling data available at present-day within each indication, particularly for indications with few trials. There was no suggestion of heterogeneity across indications. Sharing information across indications provided more precise inferences on treatment effects, and on surrogacy parameters, with the strength of sharing depending on the model. When a surrogate relationship was used to predict OS effects, uncertainty was only reduced with sharing imposed on PFS effects in addition to surrogacy parameters. Corresponding analyses using the earlier, sparser evidence available for particular HTAs showed that sharing on both surrogacy and PFS effects did not notably reduce uncertainty in OS predictions. Limited heterogeneity across indications meant that the added flexibility of mixture models was unnecessary. Conclusions: Meta-analysis methods can be usefully applied to share information on treatment effectiveness across indications to increase the precision of target indication estimates in HTA. Sharing on surrogate relationships requires caution, as meaningful precision gains require larger bodies of evidence and clear support for surrogacy from other indications., Comment: 24 pages, 5 figures, 1 table

Published
2023

150. Accessing new physics with an undoped, cryogenic CsI CEvNS detector for COHERENT at the SNS

Author

Barbeau, P. S., Belov, V., Bernardi, I., Bock, C., Bolozdynya, A., Bouabid, R., Browning, J., Cabrera-Palmer, B., Conley, E., da Silva, V., Daughhetee, J., Detwiler, J., Ding, K., Durand, M. R., Efremenko, Y., Elliott, S. R., Erlandson, A., Fabris, L., Febbraro, M., Galindo-Uribarri, A., Green, M. P., Hakenmüller, J., Heath, M. R., Hedges, S., Johnson, B. A., Johnson, T., Khromov, A., Konovalov, A., Kozlova, E., Kumpan, A., Kyzylova, O., Link, J. M., Liu, J., Major, A., Mann, K., Markoff, D. M., Mattingly, J., Mueller, P. E., Newby, J., Ogoi, N., O'Reilly, J., Parno, D. S., Pérez-Loureiro, D., Penttila, S. I., Pershey, D., Prior, C. G., Queen, J., Rapp, R., Ray, H., Razuvaeva, O., Reyna, D., Rich, G. C., Rudik, D., Runge, J., Salvat, D. J., Sander, J., Scholberg, K., Shakirov, A., Simakov, G., Snow, W. M., Sosnovtsev, V., Stringer, M., Subedi, T., Suh, B., Sur, B., Tayloe, R., Tellez-Giron-Flores, K., Tsai, Y. -T., Vanderwerp, J., van Nieuwenhuizen, E. E., Varner, R. L., Virtue, C. J., Visser, G., Walkup, K., Ward, E. M., Wongjirad, T., Yang, Y., Yoo, J., Yu, C. -H., and Zaalishvili, A.

Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

We consider the potential for a 10-kg undoped cryogenic CsI detector operating at the Spallation Neutron Source to measure coherent elastic neutrino-nucleus scattering and its sensitivity to discover new physics beyond the standard model. Through a combination of increased event rate, lower threshold, and good timing resolution, such a detector would significantly improve on past measurements. We considered tests of several beyond-the-standard-model scenarios such as neutrino non-standard interactions and accelerator-produced dark matter. This detector's performance was also studied for relevant questions in nuclear physics and neutrino astronomy, namely the weak charge distribution of CsI nuclei and detection of neutrinos from a core-collapse supernova.

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results