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3. Multi-modal investigation of the bone micro- and ultrastructure, and elemental distribution in the presence of Mg-xGd screws at mid-term healing stages

Author

Kamila Iskhakova, Hanna Cwieka, Svenja Meers, Heike Helmholz, Anton Davydok, Malte Storm, Ivo Matteo Baltruschat, Silvia Galli, Daniel Pröfrock, Olga Will, Mirko Gerle, Timo Damm, Sandra Sefa, Weilue He, Keith MacRenaris, Malte Soujon, Felix Beckmann, Julian Moosmann, Thomas O'Hallaran, Roger J. Guillory, II, D.C. Florian Wieland, Berit Zeller-Plumhoff, and Regine Willumeit-Römer

Subjects
Biodegradable implants, Bone ultrastructure, Degradation, Mg-based alloys, Materials of engineering and construction. Mechanics of materials, TA401-492, Biology (General), QH301-705.5
Abstract

Magnesium (Mg) – based alloys are becoming attractive materials for medical applications as temporary bone implants for support of fracture healing, e.g. as a suture anchor. Due to their mechanical properties and biocompatibility, they may replace titanium or stainless-steel implants, commonly used in orthopedic field. Nevertheless, patient safety has to be assured by finding a long-term balance between metal degradation, osseointegration, bone ultrastructure adaptation and element distribution in organs. In order to determine the implant behavior and its influence on bone and tissues, we investigated two Mg alloys with gadolinium contents of 5 and 10 wt percent in comparison to permanent materials titanium and polyether ether ketone. The implants were present in rat tibia for 10, 20 and 32 weeks before sacrifice of the animal. Synchrotron radiation-based micro computed tomography enables the distinction of features like residual metal, degradation layer and bone structure. Additionally, X-ray diffraction and X-ray fluorescence yield information on parameters describing the bone ultrastructure and elemental composition at the bone-to-implant interface. Finally, with element specific mass spectrometry, the elements and their accumulation in the main organs and tissues are traced. The results show that Mg-xGd implants degrade in vivo under the formation of a stable degradation layer with bone remodeling similar to that of Ti after 10 weeks. No accumulation of Mg and Gd was observed in selected organs, except for the interfacial bone after 8 months of healing. Thus, we confirm that Mg-5Gd and Mg-10Gd are suitable material choices for bone implants.

Published
2024
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4. Radiopaque FeMnN-Mo composite drawn filled tubing wires for braided absorbable neurovascular devices

Author

Adam J. Griebel, Petra Maier, Henry Summers, Benjamin Clausius, Isabella Kanasty, Weilue He, Nicholas Peterson, Carolyn Czerniak, Alexander A. Oliver, David F. Kallmes, Ramanathan Kadirvel, Jeremy E. Schaffer, and Roger J. Guillory, II

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Biology (General), QH301-705.5
Abstract

Flow diverter devices are small stents used to divert blood flow away from aneurysms in the brain, stagnating flow and inducing intra-aneurysmal thrombosis which in time will prevent aneurysm rupture. Current devices are formed from thin (∼25 μm) wires which will remain in place long after the aneurysm has been mitigated. As their continued presence could lead to secondary complications, an absorbable flow diverter which dissolves into the body after aneurysm occlusion is desirable. The absorbable metals investigated to date struggle to achieve the necessary combination of strength, elasticity, corrosion rate, fragmentation resistance, radiopacity, and biocompatibility. This work proposes and investigates a new composite wire concept combining absorbable iron alloy (FeMnN) shells with one or more pure molybdenum (Mo) cores. Various wire configurations are produced and drawn to 25–250 μm wires. Tensile testing revealed high and tunable mechanical properties on par with existing flow diverter materials. In vitro degradation testing of 100 μm wire in DMEM to 7 days indicated progressive corrosion and cracking of the FeMnN shell but not of the Mo, confirming the cathodic protection of the Mo by the FeMnN and thus mitigation of premature fragmentation risk. In vivo implantation and subsequent μCT of the same wires in mouse aortas to 6 months showed meaningful corrosion had begun in the FeMnN shell but not yet in the Mo filament cores. In total, these results indicate that these composites may offer an ideal combination of properties for absorbable flow diverters.

Published
2024
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5. The gut microbiota and its metabolite butyrate shape metabolism and antiviral immunity along the gut-lung axis in the chicken

Author

Vincent Saint-Martin, Vanaique Guillory, Mélanie Chollot, Isabelle Fleurot, Emmanuel Kut, Ferdinand Roesch, Ignacio Caballero, Emmanuelle Helloin, Emilie Chambellon, Brian Ferguson, Philippe Velge, Florent Kempf, Sascha Trapp, and Rodrigo Guabiraba

Subjects
Biology (General), QH301-705.5
Abstract

Abstract The gut microbiota exerts profound influence on poultry immunity and metabolism through mechanisms that yet need to be elucidated. Here we used conventional and germ-free chickens to explore the influence of the gut microbiota on transcriptomic and metabolic signatures along the gut-lung axis in poultry. Our results demonstrated a differential regulation of certain metabolites and genes associated with innate immunity and metabolism in peripheral tissues of germ-free birds. Furthermore, we evidenced the gut microbiota’s capacity to regulate mucosal immunity in the chicken lung during avian influenza virus infection. Finally, by fine-analysing the antiviral pathways triggered by the short-chain fatty acid (SCFA) butyrate in chicken respiratory epithelial cells, we found that it regulates interferon-stimulated genes (ISGs), notably OASL, via the transcription factor Sp1. These findings emphasize the pivotal role of the gut microbiota and its metabolites in shaping homeostasis and immunity in poultry, offering crucial insights into the mechanisms governing the communication between the gut and lungs in birds.

Published
2024
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6. The Color of Overrepresentation: Disciplinary Implications of Implicit Bias towards African American Students

Author

Victoria Symphony-Guillory Anderson

Abstract

Disproportionate discipline, also known as the discipline gap, is the result of the frequent occurrence of punitive or exclusionary discipline measures. It represents a flaw in the educational system that negatively disrupts the African American student population more than any other demographic. This form of punishment removes students from the learning environment via in-school suspension, out of school expulsion, or expulsion. The impact of exclusion on students' academic trajectory can be devastating. Implicit bias is considered a driving force for the disparities in discipline data. How educators and school staff perceive African American students and their behavior is thought to be a result of stereotypes, misconceptions, and misjudgments stemming from historically adopted patterns of marginalization towards the African American population. This study sought to fill the dearth of research regarding the role of implicit bias in disproportionately assigning disciplinary sanctions to African American students. Using the lens of implicit bias theory and microaggression theory, this study sought to understand the nuances of implicit bias with connection to behaviors resulting from that bias. Both theories serve as a foundation for understanding the parallel between marginalization and racial prejudice. The researcher conducted virtual, semi-structured interviews with five participants via Zoom, then employed thematic analysis through narrative inquiry design. The participants consisted of two elementary teachers, two middle school teachers, and one high school teacher. Four themes demonstrated the impact of implicit bias. Through the data, the participants' experiences revealed that African American students are treated differently and often more harshly than other groups of students. Findings illustrated misconceptions and misjudgments associated with African. To mitigate personal bias in this study and improve trust, the researcher employed verification techniques including critical panel, peer review and debriefing as well as member checking. Recommendations for future research include continuing to explore the connection between the legacy of slavery and discrimination towards African Americans. Additionally, further research should be centered around the cultural awareness of educators, school administration and staff. Lastly, further exploration on this topic can provide a greater understanding of the positive impact of restorative justice and behavioral management training as a catalyst for equitable disciplinary procedures for all students. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2024

7. Hyperbolic Active Learning for Semantic Segmentation under Domain Shift

Author

Franco, Luca, Mandica, Paolo, Kallidromitis, Konstantinos, Guillory, Devin, Li, Yu-Teng, Darrell, Trevor, and Galasso, Fabio

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

We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the hyperbolic radius as an indicator of data scarcity. In HALO (Hyperbolic Active Learning Optimization), for the first time, we propose the use of epistemic uncertainty as a data acquisition strategy, following the intuition of selecting data points that are the least known. The hyperbolic radius, complemented by the widely-adopted prediction entropy, effectively approximates epistemic uncertainty. We perform extensive experimental analysis based on two established synthetic-to-real benchmarks, i.e. GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes. Additionally, we test HALO on Cityscape $\rightarrow$ ACDC for domain adaptation under adverse weather conditions, and we benchmark both convolutional and attention-based backbones. HALO sets a new state-of-the-art in active learning for semantic segmentation under domain shift and it is the first active learning approach that surpasses the performance of supervised domain adaptation while using only a small portion of labels (i.e., 1%)., Comment: ICML 2024. Project repository: https://github.com/paolomandica/HALO

Published
2023

8. From Tethered to Freestanding Stabilizers of 14-3-3 Protein-Protein Interactions through Fragment Linking.

Author

Visser, Emira, Jaishankar, Priyadarshini, Sijbesma, Eline, Pennings, Marloes, Vandenboorn, Edmee, Guillory, Xavier, Neitz, R, Morrow, John, Dutta, Shubhankar, Renslo, Adam, Brunsveld, Luc, Arkin, Michelle, and Ottmann, Christian

Subjects
Drug Discovery, Fragment Linking, Fragment-Based Drug Discovery, Molecular Glues, Proteins, 14-3-3 Proteins, Estrogen Receptor alpha, Protein Binding, Drug Discovery
Abstract

Small-molecule stabilization of protein-protein interactions (PPIs) is a promising strategy in chemical biology and drug discovery. However, the systematic discovery of PPI stabilizers remains a largely unmet challenge. Herein we report a fragment-linking approach targeting the interface of 14-3-3 and a peptide derived from the estrogen receptor alpha (ERα) protein. Two classes of fragments-a covalent and a noncovalent fragment-were co-crystallized and subsequently linked, resulting in a noncovalent hybrid molecule in which the original fragment interactions were largely conserved. Supported by 20 crystal structures, this initial hybrid molecule was further optimized, resulting in selective, 25-fold stabilization of the 14-3-3/ERα interaction. The high-resolution structures of both the single fragments, their co-crystal structures and those of the linked fragments document a feasible strategy to develop orthosteric PPI stabilizers by linking to an initial tethered fragment.

Published
2023

12. The First Flight of the Marshall Grazing Incidence X-ray Spectrometer (MaGIXS)

Author

Savage, Sabrina L., Winebarger, Amy R., Kobayashi, Ken, Athiray, P. S., Beabout, Dyana, Golub, Leon, Walsh, Robert W., Beabout, Brent, Bradshaw, Stephen, Bruccoleri, Alexander R., Champey, Patrick R., Cheimets, Peter, Cirtain, Jonathan, DeLuca, Edward, Del Zanna, Giulio, Guillory, Anthony, Haight, Harlan, Heilmann, Ralf K., Hertz, Edward, Hogue, William, Kegley, Jeffery, Kolodziejczak, Jeffery, Madsen, Chad, Mason, Helen, McKenzie, David E., Ranganathan, Jagan, Reeves, Katharine K., Robertson, Bryan, Schattenburg, Mark L., Scholvin, Jorg, Siler, Richard, Testa, Paola, Vigil, Genevieve D., Warren, Harry P., Watkinson, Bejamin, Weddendorf, Bruce, and Wright, Ernest

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

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) sounding rocket experiment launched on July 30, 2021 from the White Sands Missile Range in New Mexico. MaGIXS is a unique solar observing telescope developed to capture X-ray spectral images, in the 6 - 24 Angstrom wavelength range, of coronal active regions. Its novel design takes advantage of recent technological advances related to fabricating and optimizing X-ray optical systems as well as breakthroughs in inversion methodologies necessary to create spectrally pure maps from overlapping spectral images. MaGIXS is the first instrument of its kind to provide spatially resolved soft X-ray spectra across a wide field of view. The plasma diagnostics available in this spectral regime make this instrument a powerful tool for probing solar coronal heating. This paper presents details from the first MaGIXS flight, the captured observations, the data processing and inversion techniques, and the first science results., Comment: 20 pages, 18 figures

Published
2022
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13. Content analysis of conversations on Reddit: reactions to FDA’s ENDS prioritized enforcement policy

Author

Jamie Guillory, Sarah Trigger, Jenna Brophy, Ashley Ross, Stephanie Lane, Annice Kim, James Nonnemaker, Sherry T. Liu, Kimberly Snyder, and Janine Delahanty

Subjects
electronic nicotine delivery systems, policy, social media, tobacco, flavored tobacco, public policy, Communication. Mass media, P87-96
Abstract

IntroductionOn January 2, 2020, the FDA announced a policy focused in part on prioritizing enforcement of flavored (other than tobacco- or menthol-flavored) cartridge-based electronic nicotine delivery systems (ENDS) without premarket authorization.MethodsWe used a query to identify Reddit conversations relevant to the policy from January 2 to May 6, 2020. Our sample included 576 posts (46 posts and 530 accompanying comments). Two analysts coded posts for mentions of use behaviors (e.g., switching, quitting), purchasing behaviors (e.g., purchasing from retailer new to the user), and flavored products. We summarized frequencies of coded data and provided illustrative quotes.ResultsOnly 21.0% (121/576) of posts mentioned use behavior. Switching behavior was the most common use behavior mentioned (50.4%, 61/121). Most switching behavior posts focused on ENDS-related switching (91.8%, 56/61). The most common ENDS-related switching behaviors mentioned were switching to an open tank (45.9%, 28/61) or device with refillable pods/cartridges (44.3%, 27/61); 8.2% (5/61) mentioned switching to disposables. Just 15.5% (89/576) of posts mentioned purchasing behavior, with the most common being purchasing from a retailer new to the user (32.6%, 29/89). Only 6.8% (39/576) of posts mentioned specific flavors.ConclusionReddit posts about the policy commonly discussed switching to non-cartridge-based ENDS products, such as open tank systems or disposable devices, and purchasing products from different online sources that were still selling these products. Findings suggest that publicly available Reddit data can complement data from traditional sources (e.g., surveys, sales) to understand potential unintended consequences associated with policies by exploring the public’s reactions.

Published
2024
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14. Post-acute COVID-19 outcomes including participant-reported long COVID: amubarvimab/romlusevimab versus placebo in the ACTIV-2 trialResearch in context

Author

Teresa H. Evering, Carlee Moser, Nikolaus Jilg, Justin Ritz, David A. Wohl, Jonathan Z. Li, David Margolis, Arzhang Cyrus Javan, Joseph J. Eron, Judith S. Currier, Eric S. Daar, Davey M. Smith, Michael D. Hughes, Kara W. Chew, Kara Chew, David (Davey) Smith, Eric Daar, David Wohl, Judith Currier, Joseph Eron, Michael Hughes, Mark Giganti, Lara Hosey, Jhoanna Roa, Nilam Patel, Kelly Colsh, Irene Rwakazina, Justine Beck, Scott Sieg, Jonathan Li, Courtney Fletcher, William Fischer, Rachel Bender Ignacio, Sandra Cardoso, Katya Corado, Prasanna Jagannathan, Alan Perelson, Sandy Pillay, Cynthia Riviere, Upinder Singh, Babafemi Taiwo, Joan Gottesman, Matthew Newell, Susan Pedersen, Joan Dragavon, Cheryl Jennings, Brian Greenfelder, William Murtaugh, Jan Kosmyna, Morgan Gapara, Akbar Shahkolahi, Verónica Lacal, Diego Salusso, Sebastian Nuñez, Marcelo Rodrigo Rodriguez, Luciana Laborde, Marcelo Papasidero, Luis Wehbe, Mariana Gonzalez, Felicitas Fernandez Voena, Tomas Alvarez, Amaru Lopez, Virginia Huhn, Ulises D'Andrea Nores, Pablo Dieser, Fernando Bordese, Marisa Mussi, Rodrigo de Carvalho Santana, Adriana Aparecida Tiraboschi Bárbaro, Breno Santos, Rita de Cássia Alves Lira, Andre Luiz Machado da Silva, Sandra Wagner Cardoso, Maria Pia Diniz Ribeiro, Nathália Soliva, Eduardo Vasconcellos, Jorge Eurico Ribeiro, Miriam Amaral Enéas, Jorge Pinto, Julia Fonseca de Morais Caporali, Flávia Gomes Faleiro Ferreira, Norma Erendira Rivera Martinez, Victor Casildo Bohorquez Lopez, Melchor Victor Frias, Krystle Fetalvero, Alyxzza Maranan, Jennifer Rosa, Thomas Coetzer, Maureen Mohata, Sr., Umesh Lalloo, Penelope Madlala, Larisha Pillay-Ramaya, Jaclyn Ann Bennet, Noluthando Mwelase, Nokuphiwa Mbhele, Frederick Petrick, Leonard Joubert, Rose Mbali, Sr., Natasha Joseph, Mmatsie Manentsa, Eugene van der Walt, Mduduzi Sandile Lawrance Masilela, Zinhle Zwane, Tendai Chiperera, Lerato Mohapi, Suri Moonsamy, Usha Singh, Kirsten McHarry, Elizma Snyman, Pieter Lennox, James Craig Innes, Oteng Letlape, Olebogeng Jonkane, William Brumskine, Tania Adonis, Ni Ni Sein, Modulakgotla Sebe, Yacoob Vahed, Nazreen Jeewa Hussen, Ismail Mitha, Vasundhara Cheekati, Purna Cheekati, Christie Lummus, Samuel Idarraga, Andrew Kim, David N. Pham, Wei-Hsin Kao, Michael M. Pfeffer, Miriam Batule Dominguez, Anju Malik, Anna Bryan, Melanie Arnold, Idania Fernandez, Cinzia Karpf, Aniuska Ruiz, David Taylor, Eric Folkens, Jennifer Manne, Sigal Yawetz, Cheryl Keenan, Emeka Eziri, Carl Fichtenbaum, Jenifer Baer, Sarah Trentman, Robert Call, Leroy Vaughan, Aaron Milstone, Jamie Alex Slandzicki, Jessica Wallan, Clinton Guillory, Nancy Andrews, Leslie Hughes, Jonathan Berardi, Celine Arar, Randall Quinn, Jorge P. Amaya, Marissa Gomez-Martinez, Luis Cantu, Monica Betancourt-Garcia, Nwora Lance Okeke, Charles M. Burns, Fadi Haddad, Victoria Haddad, Augusto Focil, Griselda Rosas, Susana Moyano, Yaneicy Gonzalez Rojas, Ahmad Aswad, Yevgeniy Bukhman, Manish Jain, Eugene Bukhman, Humam Farah, Rebekah McClain, Sadia Shaik, Timothy Hatlen, Deepa Gotur, Joseph Surber, Jeffrey Kingsley, April Pixler, Alex Zopo, Jack Herman, Craig Herman, Ramon Leon, Boris Nikolov, Fernando Gonzalez Vergara, Ana I. Gonzalez, Noemi Gonzalez, Michael Gelman, Olga Andriunas, Zarema Jagizarov, Jan Westerman, David Davis, Donna Sherer, Kelly Dooley, Becky Becker, Adaliah Wilkins, Jose Pérez, Eloy Roman, Heriberto Fernández, Bharat Mocherla, Kelly Beck, Valarie Maldonado, Jennifer Veltman, Rajesh Gandhi, Katrina Shea, Matthew Planchon, Laura Bogan Herpel, Kaushlendra K. Tripathi, Donald C. Day, John Pullman, Sr., Erin Williams-Leber, Misty Johnson, Michelle Hecker, Ann Avery, Keila Hoover, George W. Monlux, Elizabeth Juneja, Jr., Arthur Wernick, Karelia Ruiz, Maureen Hernández, Yadilys Pérez, Babafemi O. Taiwo, Claudia Hawkins, Baiba Berzins, Carlos Malvestutto, Heather Harber, Robyn Cicarella, Edwin DeJesus, Charlotte-Paige Rolle, Almena L. Free, Sallie D. Pulliam, Debra Weinstein, Rosa M. Suarez, Ezequiel Socorro, Estefania Socorro, Gene Neytman, Raymond Easley, Mariam Aziz, Joan Swiatek, Avish Nagpal, Breanna Kompelien, Kathryn McEvoy, Susan E. Hoover, Allison Lutz, Jessica Just, Manuel Hernandez, Yanly B. Victoria, Gabriel Rodriguez, Divya Pathak, Joshua J. Ordway, Megan Heffner, Patrick Weston, Khalilah Weston, Madhu Choudhary, Jennifer Sullivano, Olayemi Osiyemi, Myriam Izquierdo, Odelsey Torna, Brian Clemency, Renoj Varughese, Joshua Lynch, Aleen Khodabakhshian, Samantha Fortier, Christopher Coyne, Alexandrea Cronin, Constance Benson, Steven Hendrickx, Rosemarie Ramirez, Anne Luetkemeyer, Suzanne Hendler, Dennis Dentoni-Lasofsky, Mobeen Rathore, Saniyyah Mahmoudi, Amna Riaz, Mario Castro, Leslie Spikes, Chase Hall, Jonathan Oakes, Amy James Loftis, Pablo Tebas, William Short, Michael P. Dube, Saahir Khan, Luis M. Mendez, Sarah McGuffin, Chris Jonsson, Mamta K. Jain, Smruthi Senthil, Kimberly Turner-Gray, Sanjay Mehta, Mary Lewinski, Masoud Azizad, Christopher Chow, Lisa Nakatani, Derrick Williamson, Hisham Atriss, Matthew Caloura, Midhun Malla, Hannah Hazard-Jenkins, Aimee Wilkin, Jamraus Fayssoux, Hannah Seagle, Rachel Presti, and Alem Haile

Subjects
COVID-19, Monoclonal antibodies, Outpatient treatment, Clinical trial, Post COVID conditions, Long COVID, Medicine (General), R5-920
Abstract

Summary: Background: It is unknown if early COVID-19 monoclonal antibody (mAb) therapy can reduce risk of Long COVID. The mAbs amubarvimab/romlusevimab were previously demonstrated to reduce risk of hospitalization/death by 79%. This study assessed the impact of amubarvimab/romlusevimab on late outcomes, including Long COVID. Methods: Non-hospitalized high-risk adults within 10 days of COVID-19 symptom onset enrolled in a randomized, double-blind, placebo-controlled phase 2/3 trial of amubarvimab/romlusevimab for COVID-19 treatment. Late symptoms, assessed using a participant-completed symptom diary, were a pre-specified exploratory endpoint. The primary outcome for this analysis was the composite of Long COVID by participant self-report (presence of COVID-19 symptoms as recorded in the diary at week 36) or hospitalization or death by week 36. Inverse probability weighting (IPW) was used to address incomplete outcome ascertainment, giving weighted risk ratios (wRR) comparing amubarvimab/romlusevimab to placebo. Findings: Participants received amubarvimab/romlusevimab (n = 390) or placebo (n = 390) between January and July 2021. Median age was 49 years, 52% were female, 18% Black/African American, 49% Hispanic/Latino, and 9% COVID-19-vaccinated at entry. At week 36, 103 (13%) had incomplete outcome ascertainment, and 66 (17%) on amubarvimab/romlusevimab and 92 (24%) on placebo met the primary outcome (wRR = 0.70, 95% confidence interval (CI) 0.53–0.93). The difference was driven by fewer hospitalizations/deaths with amubarvimab/romlusevimab (4%) than placebo (13%). Among 652 participants with available diary responses, 53 (16%) on amubarvimab/romlusevimab and 44 (14%) on placebo reported presence of Long COVID. Interpretation: Amubarvimab/romlusevimab treatment, while highly effective in preventing hospitalizations/deaths, did not reduce risk of Long COVID. Additional interventions are needed to prevent Long COVID. Funding: National Institute of Allergy and Infectious Diseases of the National Institutes of Health. Amubarvimab and romlusevimab supplied by Brii Biosciences.

Published
2024
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16. Using Language to Extend to Unseen Domains

Author

Dunlap, Lisa, Mohri, Clara, Guillory, Devin, Zhang, Han, Darrell, Trevor, Gonzalez, Joseph E., Raghunathan, Aditi, and Rohrbach, Anja

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply verbalizing the training domain (e.g. "photos of birds") as well as domains we want to extend to but do not have data for (e.g. "paintings of birds") can improve robustness. Using a multimodal model with a joint image and language embedding space, our method LADS learns a transformation of the image embeddings from the training domain to each unseen test domain, while preserving task relevant information. Without using any images from the unseen test domain, we show that over the extended domain containing both training and unseen test domains, LADS outperforms standard fine-tuning and ensemble approaches over a suite of four benchmarks targeting domain adaptation and dataset bias.

Published
2022

17. Studying Bias in GANs through the Lens of Race

Author

Maluleke, Vongani H., Thakkar, Neerja, Brooks, Tim, Weber, Ethan, Darrell, Trevor, Efros, Alexei A., Kanazawa, Angjoo, and Guillory, Devin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.4
Abstract

In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling the racial distributions in various training datasets, we are able to observe the impacts of different training distributions on generated image quality and the racial distributions of the generated images. Our results show that the racial compositions of generated images successfully preserve that of the training data. However, we observe that truncation, a technique used to generate higher quality images during inference, exacerbates racial imbalances in the data. Lastly, when examining the relationship between image quality and race, we find that the highest perceived visual quality images of a given race come from a distribution where that race is well-represented, and that annotators consistently prefer generated images of white people over those of Black people., Comment: ECCV 2022. Project Page: https://neerja.me/bias-gans/

Published
2022

18. Disentangled Action Recognition with Knowledge Bases

Author

Luo, Zhekun, Ghosh, Shalini, Guillory, Devin, Kato, Keizo, Darrell, Trevor, and Xu, Huijuan

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

Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have training data for all possible combinations. In this paper, we aim to improve the generalization ability of the compositional action recognition model to novel verbs or novel nouns that are unseen during training time, by leveraging the power of knowledge graphs. Previous work utilizes verb-noun compositional action nodes in the knowledge graph, making it inefficient to scale since the number of compositional action nodes grows quadratically with respect to the number of verbs and nouns. To address this issue, we propose our approach: Disentangled Action Recognition with Knowledge-bases (DARK), which leverages the inherent compositionality of actions. DARK trains a factorized model by first extracting disentangled feature representations for verbs and nouns, and then predicting classification weights using relations in external knowledge graphs. The type constraint between verb and noun is extracted from external knowledge bases and finally applied when composing actions. DARK has better scalability in the number of objects and verbs, and achieves state-of-the-art performance on the Charades dataset. We further propose a new benchmark split based on the Epic-kitchen dataset which is an order of magnitude bigger in the numbers of classes and samples, and benchmark various models on this benchmark., Comment: NAACL 2022

Published
2022

19. Absorbable metal stents for vascular use in pediatric cardiology: progress and outlook

Author

Daniel I. McLennan, Jennifer R. Maldonado, Susan R. Foerster, Stephanie S. Handler, John F. LaDisa, Todd M. Gudausky, and Roger J. Guillory

Subjects
absorbable metals, magnesium, zinc, iron, pediatric stents, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

The past five years have yielded impressive advancements in fully absorbable metal stent technology. The desired ultimate ability for such devices to treat a vascular stenosis without long-term device-related complications or impeding future treatment continues to evoke excitement in clinicians and engineers alike. Nowhere is the need for fully absorbable metal stents greater than in patients experiencing vascular anomalies associated with congenital heart disease (CHD). Perhaps not surprisingly, commercially available absorbable metal stents have been implanted in pediatric cardiology patients with conditions ranging from pulmonary artery and vein stenosis to coarctation of the aorta and conduit/shunt reconstructions. Despite frequent short term procedural success, device performance has missed the mark with the commercially available devices not achieving degradation benchmarks for given applications. In this review we first provide a general overview detailing the theory of absorbable metal stents, and then review recent clinical use in CHD patients since the release of current-generation absorbable metal stents around 2019. We also discuss the challenges and our center's experience associated with the use of absorbable metal stents in this pediatric population. Lastly, we present potential directions for future engineering endeavors to mitigate existing challenges.

Published
2024
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20. Can Curricular Reform for Coherence and Efficiency Occur Quickly in Higher Education

Author

Zubov, Lynn Roch, Guillory, Monica D., and Farmer, Deborah F.

Abstract

The purpose of this research was to investigate whether a mid-sized public university could create a more coherent and efficient undergraduate curriculum over a short period of time with minimal resources. We pursued this question using an action research design. Using a curriculum model, we developed that focused on foundational, breadth, and depth of knowledge as a framework, we asked academic programs at the targeted university to evaluate and reform their degree programs for coherency and efficiency. Data were collected using a summary data template for curriculum framework during the planning year and then again after two years of reform. Data were also collected on the number of program and course revisions implemented throughout the study. Additionally, using the Stages of Concern Questionnaire and an ad hoc survey, we gathered faculty and department chairs' perceptions about the process. The results indicate that systemic change to a university's curriculum can occur relatively quickly and with limited financial investment. However, the study suggests that for significant change to occur, faculty must be given time to process the paradigm shift. Additionally, when engaging in curriculum renewal, department chairs require opportunities to discuss the process with their peers and often need individual, targeted assistance.

Published
2021

21. Multi-modal investigation of the bone micro- and ultrastructure, and elemental distribution in the presence of Mg-xGd screws at mid-term healing stages

Author

Iskhakova, Kamila, Cwieka, Hanna, Meers, Svenja, Helmholz, Heike, Davydok, Anton, Storm, Malte, Baltruschat, Ivo Matteo, Galli, Silvia, Pröfrock, Daniel, Will, Olga, Gerle, Mirko, Damm, Timo, Sefa, Sandra, He, Weilue, MacRenaris, Keith, Soujon, Malte, Beckmann, Felix, Moosmann, Julian, O'Hallaran, Thomas, Guillory, Roger J., II, Wieland, D.C. Florian, Zeller-Plumhoff, Berit, and Willumeit-Römer, Regine

Published
2024
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23. Post-acute COVID-19 outcomes including participant-reported long COVID: amubarvimab/romlusevimab versus placebo in the ACTIV-2 trial

Author

Chew, Kara, Smith, David (Davey), Daar, Eric, Wohl, David, Currier, Judith, Eron, Joseph, Javan, Arzhang Cyrus, Hughes, Michael, Moser, Carlee, Giganti, Mark, Ritz, Justin, Hosey, Lara, Roa, Jhoanna, Patel, Nilam, Colsh, Kelly, Rwakazina, Irene, Beck, Justine, Sieg, Scott, Li, Jonathan, Fletcher, Courtney, Fischer, William, Evering, Teresa H., Ignacio, Rachel Bender, Cardoso, Sandra, Corado, Katya, Jagannathan, Prasanna, Jilg, Nikolaus, Perelson, Alan, Pillay, Sandy, Riviere, Cynthia, Singh, Upinder, Taiwo, Babafemi, Gottesman, Joan, Newell, Matthew, Pedersen, Susan, Dragavon, Joan, Jennings, Cheryl, Greenfelder, Brian, Murtaugh, William, Kosmyna, Jan, Gapara, Morgan, Shahkolahi, Akbar, Margolis, David, Lacal, Verónica, Salusso, Diego, Nuñez, Sebastian, Rodriguez, Marcelo Rodrigo, Laborde, Luciana, Papasidero, Marcelo, Wehbe, Luis, Gonzalez, Mariana, Voena, Felicitas Fernandez, Alvarez, Tomas, Lopez, Amaru, Huhn, Virginia, D'Andrea Nores, Ulises, Dieser, Pablo, Bordese, Fernando, Mussi, Marisa, de Carvalho Santana, Rodrigo, Tiraboschi Bárbaro, Adriana Aparecida, Santos, Breno, de Cássia Alves Lira, Rita, Machado da Silva, Andre Luiz, Cardoso, Sandra Wagner, Diniz Ribeiro, Maria Pia, Soliva, Nathália, Vasconcellos, Eduardo, Ribeiro, Jorge Eurico, Enéas, Miriam Amaral, Pinto, Jorge, Fonseca de Morais Caporali, Julia, Faleiro Ferreira, Flávia Gomes, Rivera Martinez, Norma Erendira, Bohorquez Lopez, Victor Casildo, Frias, Melchor Victor, Fetalvero, Krystle, Maranan, Alyxzza, Rosa, Jennifer, Coetzer, Thomas, Mohata, Maureen, Sr., Lalloo, Umesh, Madlala, Penelope, Pillay-Ramaya, Larisha, Bennet, Jaclyn Ann, Mwelase, Noluthando, Mbhele, Nokuphiwa, Petrick, Frederick, Joubert, Leonard, Mbali, Rose, Sr., Joseph, Natasha, Manentsa, Mmatsie, van der Walt, Eugene, Lawrance Masilela, Mduduzi Sandile, Zwane, Zinhle, Chiperera, Tendai, Mohapi, Lerato, Moonsamy, Suri, Singh, Usha, McHarry, Kirsten, Snyman, Elizma, Lennox, Pieter, Innes, James Craig, Letlape, Oteng, Jonkane, Olebogeng, Brumskine, William, Adonis, Tania, Sein, Ni Ni, Sebe, Modulakgotla, Vahed, Yacoob, Hussen, Nazreen Jeewa, Mitha, Ismail, Cheekati, Vasundhara, Cheekati, Purna, Lummus, Christie, Idarraga, Samuel, Kim, Andrew, Pham, David N., Kao, Wei-Hsin, Pfeffer, Michael M., Dominguez, Miriam Batule, Malik, Anju, Bryan, Anna, Arnold, Melanie, Fernandez, Idania, Karpf, Cinzia, Ruiz, Aniuska, Taylor, David, Folkens, Eric, Manne, Jennifer, Yawetz, Sigal, Keenan, Cheryl, Eziri, Emeka, Fichtenbaum, Carl, Baer, Jenifer, Trentman, Sarah, Call, Robert, Vaughan, Leroy, Milstone, Aaron, Slandzicki, Jamie Alex, Wallan, Jessica, Guillory, Clinton, Andrews, Nancy, Hughes, Leslie, Berardi, Jonathan, Arar, Celine, Quinn, Randall, Amaya, Jorge P., Gomez-Martinez, Marissa, Cantu, Luis, Betancourt-Garcia, Monica, Okeke, Nwora Lance, Burns, Charles M., Haddad, Fadi, Haddad, Victoria, Focil, Augusto, Rosas, Griselda, Moyano, Susana, Rojas, Yaneicy Gonzalez, Aswad, Ahmad, Bukhman, Yevgeniy, Jain, Manish, Bukhman, Eugene, Farah, Humam, McClain, Rebekah, Shaik, Sadia, Hatlen, Timothy, Gotur, Deepa, Surber, Joseph, Kingsley, Jeffrey, Pixler, April, Zopo, Alex, Herman, Jack, Herman, Craig, Leon, Ramon, Nikolov, Boris, Vergara, Fernando Gonzalez, Gonzalez, Ana I., Gonzalez, Noemi, Gelman, Michael, Andriunas, Olga, Jagizarov, Zarema, Westerman, Jan, Davis, David, Sherer, Donna, Dooley, Kelly, Becker, Becky, Wilkins, Adaliah, Pérez, Jose, Roman, Eloy, Fernández, Heriberto, Mocherla, Bharat, Beck, Kelly, Maldonado, Valarie, Veltman, Jennifer, Gandhi, Rajesh, Shea, Katrina, Planchon, Matthew, Herpel, Laura Bogan, Tripathi, Kaushlendra K., Day, Donald C., Pullman, John, Sr., Williams-Leber, Erin, Johnson, Misty, Hecker, Michelle, Avery, Ann, Hoover, Keila, Monlux, George W., Juneja, Elizabeth, Jr., Wernick, Arthur, Ruiz, Karelia, Hernández, Maureen, Pérez, Yadilys, Taiwo, Babafemi O., Hawkins, Claudia, Berzins, Baiba, Malvestutto, Carlos, Harber, Heather, Cicarella, Robyn, DeJesus, Edwin, Rolle, Charlotte-Paige, Free, Almena L., Pulliam, Sallie D., Weinstein, Debra, Suarez, Rosa M., Socorro, Ezequiel, Socorro, Estefania, Neytman, Gene, Easley, Raymond, Aziz, Mariam, Swiatek, Joan, Nagpal, Avish, Kompelien, Breanna, McEvoy, Kathryn, Hoover, Susan E., Lutz, Allison, Just, Jessica, Hernandez, Manuel, Victoria, Yanly B., Rodriguez, Gabriel, Pathak, Divya, Ordway, Joshua J., Heffner, Megan, Weston, Patrick, Weston, Khalilah, Choudhary, Madhu, Sullivano, Jennifer, Osiyemi, Olayemi, Izquierdo, Myriam, Torna, Odelsey, Clemency, Brian, Varughese, Renoj, Lynch, Joshua, Khodabakhshian, Aleen, Fortier, Samantha, Coyne, Christopher, Cronin, Alexandrea, Benson, Constance, Hendrickx, Steven, Ramirez, Rosemarie, Luetkemeyer, Anne, Hendler, Suzanne, Dentoni-Lasofsky, Dennis, Rathore, Mobeen, Mahmoudi, Saniyyah, Riaz, Amna, Castro, Mario, Spikes, Leslie, Hall, Chase, Oakes, Jonathan, Loftis, Amy James, Tebas, Pablo, Short, William, Dube, Michael P., Khan, Saahir, Mendez, Luis M., McGuffin, Sarah, Jonsson, Chris, Jain, Mamta K., Senthil, Smruthi, Turner-Gray, Kimberly, Mehta, Sanjay, Lewinski, Mary, Azizad, Masoud, Chow, Christopher, Nakatani, Lisa, Williamson, Derrick, Atriss, Hisham, Caloura, Matthew, Malla, Midhun, Hazard-Jenkins, Hannah, Wilkin, Aimee, Fayssoux, Jamraus, Seagle, Hannah, Presti, Rachel, Haile, Alem, Wohl, David A., Li, Jonathan Z., Eron, Joseph J., Currier, Judith S., Daar, Eric S., Smith, Davey M., Hughes, Michael D., and Chew, Kara W.

Published
2024
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27. Different binding modalities of quercetin to inositol-requiring enzyme 1 of S. cerevisiae and human lead to opposite regulation

Author

S. Jalil Mahdizadeh, Johan Grandén, Diana Pelizzari-Raymundo, Xavier Guillory, Antonio Carlesso, Eric Chevet, and Leif A. Eriksson

Subjects
Chemistry, QD1-999
Abstract

Abstract The flavonoid Quercetin (Qe) was identified as an activator of Inositol-requiring enzyme 1 (IRE1) in S. cerevisiae (scIre1p), but its impact on human IRE1 (hIRE1) remains controversial due to the absence of a conserved Qe binding site. We have explored the binding modes and effect of Qe on both scIre1p and hIRE1 dimers using in silico and in vitro approaches. The activation site in scIre1p stably accommodates both Qe and its derivative Quercitrin (Qi), thus enhancing the stability of the RNase pocket. However, the corresponding region in hIRE1 does not bind any of the two molecules. Instead, we show that both Qe and Qi block the RNase activity of hIRE1 in vitro, with sub-micromolar IC50 values. Our results provide a rationale for why Qe is an activator in scIre1p but a potent inhibitor in hIRE1. The identification of a new allosteric site in hIRE1 opens a promising window for drug development and UPR modulation.

Published
2024
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28. Beyond Ticking Boxes: Holistic Assessment of Travel Award Programs Is Essential for Inclusivity

Author

Etson, Candice M, Block, Kirsten F, Burton, Michael D, Edwards, Ashanti, Flores, Sonia C, Fry, Catherine, Guillory, Ashley N, Ingram, Susan L, McGee, Richard, Neely-Fisher, Deborah L, Paxson, Stephanie, Phelan, Laura, Primus, Clara, Suggs, Kirsta, Vega, Leticia R, Vuong, Elizabeth, Hammonds-Odie, Latanya, Leibowitz, Michael J, Zavala, MariaElena, Lujan, J Luis, Ramirez-Alvarado, Marina, and Segarra, Verónica A

Published
2022

29. Educators' Perceptions of Mental Illness in Elementary Schools: A Phenomenological Study

Author

Adrienne Berry Guillory

Abstract

The debate about mental illness and the importance of good mental health especially in the classroom has surged. Promoting child mental health is a global priority, and educators have an important role to play in mitigating this public health crisis. Early identification, accurate diagnosis, and effective treatment of mental health disorders can decrease the suffering of children and their families. The purpose of this study is to research elementary school teachers' perceptions of their importance in identifying, referring, and providing support for the mental health of their students. This qualitative study framed through a phenomenological lens aimed to determine the best practices and strategies for addressing the mental health needs and unique challenges faced by primary school children. The research questions and Bronfenbrenner's Theory informed this study which was conducted using semi-structured open-ended face-to-face interviews. The study participants included K-5 elementary school teachers in Southeast Texas. Evidence suggests that school-based mental health services have the highest likelihood of reaching children in need. In this paper, the researcher presents a foundation that can serve as a blueprint for an intervention plan for policymakers and educational leaders. Recommendations for practice and future research are also included. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2023

32. Predicting with Confidence on Unseen Distributions

Author

Guillory, Devin, Shankar, Vaishaal, Ebrahimi, Sayna, Darrell, Trevor, and Schmidt, Ludwig

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning, I.2.10
Abstract

Recent work has shown that the performance of machine learning models can vary substantially when models are evaluated on data drawn from a distribution that is close to but different from the training distribution. As a result, predicting model performance on unseen distributions is an important challenge. Our work connects techniques from domain adaptation and predictive uncertainty literature, and allows us to predict model accuracy on challenging unseen distributions without access to labeled data. In the context of distribution shift, distributional distances are often used to adapt models and improve their performance on new domains, however accuracy estimation, or other forms of predictive uncertainty, are often neglected in these investigations. Through investigating a wide range of established distributional distances, such as Frechet distance or Maximum Mean Discrepancy, we determine that they fail to induce reliable estimates of performance under distribution shift. On the other hand, we find that the difference of confidences (DoC) of a classifier's predictions successfully estimates the classifier's performance change over a variety of shifts. We specifically investigate the distinction between synthetic and natural distribution shifts and observe that despite its simplicity DoC consistently outperforms other quantifications of distributional difference. $DoC$ reduces predictive error by almost half ($46\%$) on several realistic and challenging distribution shifts, e.g., on the ImageNet-Vid-Robust and ImageNet-Rendition datasets., Comment: ICCV Camera ready; new scatter plots in supplementary material

Published
2021

33. The European GeoMetre project: developing enhanced large-scale dimensional metrology for geodesy

Author

Pollinger, Florian, Baselga, Sergio, Courde, Clément, Eschelbach, Cornelia, García-Asenjo, Luis, Garrigues, Pascual, Guillory, Joffray, Hedekvist, Per Olof, Helojärvi, Tuomas, Jokela, Jorma, Kallio, Ulla, Klügel, Thomas, Köchert, Paul, Lösler, Michael, Luján, Raquel, Meyer, Tobias, Neyezhmakov, Pavel, Pesce, Damien, Pisani, Marco, Poutanen, Markku, Prellinger, Günther, Sauthoff, Anni, Seppä, Jeremias, Truong, Daniel, Underwood, Robin, Wezka, Kinga, Wallerand, Jean-Pierre, and Wiśniewski, Mariusz

Published
2023
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35. Self-Supervised Pretraining Improves Self-Supervised Pretraining

Author

Reed, Colorado J., Yue, Xiangyu, Nrusimha, Ani, Ebrahimi, Sayna, Vijaykumar, Vivek, Mao, Richard, Li, Bo, Zhang, Shanghang, Guillory, Devin, Metzger, Sean, Keutzer, Kurt, and Darrell, Trevor

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to data augmentation. Prior work demonstrates that models pretrained on datasets dissimilar to their target data, such as chest X-ray models trained on ImageNet, underperform models trained from scratch. Users that lack the resources to pretrain must use existing models with lower performance. This paper explores Hierarchical PreTraining (HPT), which decreases convergence time and improves accuracy by initializing the pretraining process with an existing pretrained model. Through experimentation on 16 diverse vision datasets, we show HPT converges up to 80x faster, improves accuracy across tasks, and improves the robustness of the self-supervised pretraining process to changes in the image augmentation policy or amount of pretraining data. Taken together, HPT provides a simple framework for obtaining better pretrained representations with less computational resources.

Published
2021

36. Effect of Metformin and Lifestyle Interventions on Mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study

Author

Lee, Christine G, Heckman-Stoddard, Brandy, Dabelea, Dana, Gadde, Kishore M, Ehrmann, David, Ford, Leslie, Prorok, Philip, Boyko, Edward J, Pi-Sunyer, Xavier, Wallia, Amisha, Knowler, William C, Crandall, Jill P, Temprosa, Marinella, Group:, Diabetes Prevention Program Research, Bray, George A, Culbert, Iris W, Arceneaux, Jennifer, Chatellier, Annie, Dragg, Amber, Champagne, Catherine M, Duncan, Crystal, Eberhardt, Barbara, Greenway, Frank, Guillory, Fonda G, Herbert, April A, Jeffirs, Michael L, Kennedy, Betty M, Levy, Erma, Lockett, Monica, Lovejoy, Jennifer C, Morris, Laura H, Melancon, Lee E, Ryan, Donna H, Sanford, Deborah A, Smith, Kenneth G, Smith, Lisa L, St. Amant, Julia A, Tulley, Richard T, Vicknair, Paula C, Williamson, Donald, Zachwieja, Jeffery J, Polonsky, Kenneth S, Tobian, Janet, Ehrmann, David A, Matulik, Margaret J, Temple, Karla A, Clark, Bart, Czech, Kirsten, DeSandre, Catherine, Dotson, Brittnie, Hilbrich, Ruthanne, McNabb, Wylie, Semenske, Ann R, Caro, Jose F, Furlong, Kevin, Goldstein, Barry J, Watson, Pamela G, Smith, Kellie A, Mendoza, Jewel, Simmons, Marsha, Wildman, Wendi, Liberoni, Renee, Spandorfer, John, Pepe, Constance, Donahue, Richard P, Goldberg, Ronald B, Prineas, Ronald, Calles, Jeanette, Giannella, Anna, Rowe, Patricia, Sanguily, Juliet, Cassanova-Romero, Paul, Castillo-Florez, Sumaya, Florez, Hermes J, Garg, Rajesh, Kirby, Lascelles, Lara, Olga, Larreal, Carmen, McLymont, Valerie, Mendez, Jadell, Perry, Arlette, Saab, Patrice, Veciana, Bertha, Haffner, Steven M, Hazuda, Helen P, Montez, Maria G, Hattaway, Kathy, Isaac, Juan, Lorenzo, Carlos, Martinez, Arlene, Salazar, Monica, Walker, Tatiana, Hamman, Richard F, Nash, Patricia V, Steinke, Sheila C, Testaverde, Lisa, Truong, Jennifer, and Anderson, Denise R

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Clinical Sciences, Prevention, Clinical Trials and Supportive Activities, Nutrition, Clinical Research, Cancer, Cardiovascular, Diabetes, Aetiology, 2.2 Factors relating to the physical environment, Good Health and Well Being, Adult, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Hypoglycemic Agents, Life Style, Metformin, Diabetes Prevention Program Research Group, Diabetes Prevention Program Research Group:, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveTo determine whether metformin or lifestyle modification can lower rates of all-cause and cause-specific mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study.Research design and methodsFrom 1996 to 1999, 3,234 adults at high risk for type 2 diabetes were randomized to an intensive lifestyle intervention, masked metformin, or placebo. Placebo and lifestyle interventions stopped in 2001, and a modified lifestyle program was offered to everyone, but unmasked study metformin continued in those originally randomized. Causes of deaths through 31 December 2018 were adjudicated by blinded reviews. All-cause and cause-specific mortality hazard ratios (HRs) were estimated from Cox proportional hazards regression models and Fine-Gray models, respectively.ResultsOver a median of 21 years (interquartile range 20-21), 453 participants died. Cancer was the leading cause of death (n = 170), followed by cardiovascular disease (n = 131). Compared with placebo, metformin did not influence mortality from all causes (HR 0.99 [95% CI 0.79, 1.25]), cancer (HR 1.04 [95% CI 0.72, 1.52]), or cardiovascular disease (HR 1.08 [95% CI 0.70, 1.66]). Similarly, lifestyle modification did not impact all-cause (HR 1.02 [95% CI 0.81, 1.28]), cancer (HR 1.07 [95% CI 0.74, 1.55]), or cardiovascular disease (HR 1.18 [95% CI 0.77, 1.81]) mortality. Analyses adjusted for diabetes status and duration, BMI, cumulative glycemic exposure, and cardiovascular risks yielded results similar to those for all-cause mortality.ConclusionsCancer was the leading cause of mortality among adults at high risk for type 2 diabetes. Although metformin and lifestyle modification prevented diabetes, neither strategy reduced all-cause, cancer, or cardiovascular mortality rates.

Published
2021

37. Large-Scale Dimensional Metrology for Geodesy—First Results from the European GeoMetre Project

Author

Pollinger, Florian, Courde, Clément, Eschelbach, Cornelia, García-Asenjo, Luis, Guillory, Joffray, Hedekvist, Per Olof, Kallio, Ulla, Klügel, Thomas, Neyezhmakov, Pavel, Pesce, Damien, Pisani, Marco, Seppä, Jeremias, Underwood, Robin, Wezka, Kinga, Wiśniewski, Mariusz, Freymueller, Jeffrey T., Series Editor, and Sánchez, Laura, Assistant Editor

Published
2023
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38. Investigating Motor Preparation in Autism Spectrum Disorder with and without Attention Deficit/Hyperactivity Disorder

Author

Migó, Marta, Guillory, Sylvia B., McLaughlin, Christopher S., Isenstein, Emily L., Grosman, Hannah E., Thakkar, Katharine N., Castellanos, Francisco X., and Foss-Feig, Jennifer H.

Abstract

This study investigated motor preparation and action-consequence prediction using the lateralized readiness potential (LRP). Motor impairments are common in autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. Alterations in predictive processes may impact motor planning. Whether motor planning deficits are characteristic of ASD broadly or magnified in the context of co-morbid ADHD is unclear. ASD children with (ASD + ADHD; n = 12) and without (ASD - ADHD; n = 9) comorbid ADHD and typical controls (n = 29) performed voluntary motor actions that either did or did not result in auditory consequences. ASD - ADHD children demonstrated LRP enhancement when their action produced an effect while ASD + ADHD children had attenuated responses regardless of action-effect pairings. Findings suggest influence of ADHD comorbidity on motor preparation and prediction in ASD.

Published
2022
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39. Assessment of Baseline Ultrawidefield Fluorescein Angiographic Quantitative Leakage Parameters with Ultrawidefield Fundus Features and Clinical Parameters in Diabetic Retinopathy in Protocol AA

Author

Harara, Abla M., Palacios, Angela N., Berger, Brian B., Corak, Boris, Luong, Bianca, Jhaveri, Chirag D., Wilson, Daniela Mariel, Jonna, Gowtham, Gunderson, Ivana, Hosein, Kimberly, Reid, Ryan M., Chexal, Saradha, Moore, Tori, Seidu, Tina A., Gatavaski, Valerie, Ren, Yong, Stern, Bradley A., Benvenutti, Celia E., Oude-Reimerink, Dinah S., Shaheen, Jenny, Grybas, John, Vitale-Kuhn, Julianne, Staffne, Jessica L., Ventimiglia, Katie M., Allis, Megan, Monk, Mary K., Thomas, Marc E., Massu, Nicole M., Edwards, Paul Andrew, Troszak, Tracy A., Irons, Amber N., Rego, Brittany, Han, Dennis P., Dorsey, Eleanor, Nelson, Erika, Sheppard, Hannah, Beringer, Joseph R., Kim, Judy E., Keller, Kristy L., Packard, Krissa L., Altmann, Marriner L., Goldberg, Mara, Chen, Nickolas, Winter, Pat A., Bourgeois, Shay, Jacobo, Samantha, Moebius, Stephanie J., Connor, Thomas B., Barwick, Vicki, Williams, Vesper V., Wirostko, William J., Ghuman, A. Thomas, Leslie, Anita H., Sharma, Ashish G., Kiesel, Cheryl, Dyshanowitz, Danielle, Knips, Eileen, Wing, Glenn, Walker, Joseph P., Raskauskas, Paul A., Kiesel, Raymond K., Schlossman, Deborah K., Weimann, Elizabeth S., Sharuk, George S., Kwak, Hanna, Cavallerano, Jerry D., Rhee, Jae W., Sampani, Konstantina, Tran, Katie V., Bestourous, Leila, Miranda, Linette, Krigman, Michael N., Stockman, Margaret E., Arrigg, Paul G., Cavicchi, Robert W., Kirby, Rita K., Glynn, Shireen, Papaconstantinou, Steve L., Shah, Sabera T., Murtha, Timothy J., Carli, William, Finch, Autumn K., Gentile, Angella K., Price, Angela K., Murphy, Brittany A., Rowland, Beverly O., Fleming, Christina J., Mahr, Courtney, Shore, Carol A., Browning, David, McClain, Donna, Breglio, Erica, Lester, Gina M., Herby, Jenna T., Bratcher, Kayla A., Clark, Loraine M., Jackson, Lisa A., Watson, Lynn, McOwen, Michael D., Punjabi, Omar S., Bojaj, Swann J., Ennis, Sarah A., Fredenberg, Sherry L., Jones, Taylor S., Ragin, Teneisha A., Balasubramaniam, Uma M., Ornelas, Blanca, Rodriquez, Brenda, Edwards, Carla, Carns, Danielle R., Tonner, Eileen E., Woo, Kisung, Richine, Len, MacCumber, Mathew W., Merrill, Pauline Townsend, Kociborski, Sarah, Harless, Ashley M., Harris, Charlotte, White, Lorraine, Maturi, Raj K., Asher, Julie, Walsh, Justin, Wheeler, Jeff, Milstead, Katie, Oliver, Kristina, Lovelady, Lisa, Anderson, Nicholas G., Coppola, Patricia, Lince, Raul E., Shuler, R. Keith, Morris, Steve, Oelrich, Sarah M., Gardner, Brandon S., Moore, Bob, Cain, Dennis, Donohue, Deborah, Emmert, David, Adeyemo, Kemi, Levin, Lisa K., Frey, Mary, Rhoton, Nick, Bressler, Susan, Solomon, Sharon D., Ford, Amy L., Hughes, Ashley, Brewer, Alisha N., Booth, JoAnn T., Lunsford, Keven W., Ukleya, Lauren D., Burris, Russ, Kingsley, Ronald M., Almeida, Shannon R., Icks, Sonny, Shah, Vinay A., Bergman, Vanessa A., Castellarin, Alessandro A., Shook, Aimee H., Walker, Aimee, Pieramici, Dante J., Hong, Gina, Avery, Kelly, McKee, Kate M., Giust, Matthew, Munoz, Marco A., Fishbein, Sarah, Camp, Alecia B., Baker, Carl W., Baker, Jil D., Sedberry, Kylie S., Lambert, Lynnette F., Orr, Margaret J., Alcaraz, Sonya L., Kettler, Samantha, Caldwell, Tracey M., Miller, Abigail, Dorr, Christine M., Hampton, G. Robert, Brown, Jamin S., Barker, Jeffrey P., Rosenberg, Kevin I., Kwasniewski, Lynn M., Sienkiewycz, Laurie J., Spuches, Lisa, Manley, Michelle L., Robarge, Nicole E., DeSantis, Stefanie R., DeForge, Teresa M., Brucker, Alexander J., Kim, Benjamin J., Berger, Jim M., DuPont, Joan C., Drossner, Sheri, Freeman, Sara, Studebaker, Ashley, Payne, John F., Wells, John A., Spivey, Robbin, Ogbuewu, Tiffany N., Swinford, Tiffany R., Guillory, Adrienne, Hutson, Amy, Schefler, Amy C., Shah, Ankoor R., Almanza, Belinda A., Dives, Brenda, Richter, Beau A., Stoever, Cary A., Brown, David M., Foerster, Danee, Garcia, David, Rodriguez, Diana, Park, Daniel, Chen, Eric, Kegley, Eric N., Quellar, Elizabeth, Twining, Garret L., Koger-Grifaldo, Heather, Ortega, Ilsa, Carranza, Jolene, Major, James C., Williamson, Kimberly, Burt, Lindsay, Salinas, Luis R., Wolff, Lisa M., Benz, Matthew S., Estes, Maura A., James, Miranda F., Berry, Meredith, Vela, Melina, Landaverde, Nubia, Webb, Nina A., Fish, Richard H., Kim, Rosa Y., Yee, Rebecca, Karani, Sadia Y., Supapo, Stacy M., Dodel, Tamara L., McCoy, Tyneisha, Wong, Tien P., Sneed, Veronica A., Barnhart, Cassandra J., Cantrell, Debra, DuBose, Elizabeth L., Sharpe, Houston P., Ulrich, Jan Niklas, Bhansali, Kanika A., Esquejo, Rona Lyn, Garg, Seema, Grout, Sean, McKinney, Allen, Bobbitt, Brenda J., Wendel, Ceara L., Fagan, Damanda F., Andrews, Jacqueline, Holmes, Krystal Nikki, Seyez, Karen L., Williamson, Kimberly A., Moinfar, Nader, Walters, Paige N., Carlton, Steve, Rehling, Shannon M., Williams, Shana E., Reed, Tiara L., VandeVelde, Amber R., Yeager, Frank T., Fox, Gregory M., Batlle, Ivan R., Bruce, Kiersten, Pippin, Katherine, Ainley, Lexie R., Singh, Ravi S.J., Adamo, Ashley M., Guardado, Adrian, Patel, Apurva K., Puckett, Brian S., Hoerner, Christine, Ma, Colin, Clark, David J., Flato, Inessa M., Cohen, Joshua, Charpentier, Margaret E., Kopfer, Marcia, Peters, Mark A., Smith, Pualani, Tlucek, Paul S., Hobbs, Stephen, Ho, Stephanie L., Metzger, Ashley M., McCalla, Alesia K., Thompson, Amy, Ringrose, Christine, Sandler, Dallas R., Leder, Henry A., Belz, Jennifer L., Starr, JoAnn, Simmons, Jennifer L., Orr, Peggy R., Sotirakos, Peter, Singletary, Pamela V., Cain, Terri, Coffey, Teresa, Carter, Tiffany M., Robinson, Twyla J., Shah, Chirag P., Cammarata, Dominique, Kruger, Jennifer L., Colegrove, Lindsey, Graham, Margie, Gleason, Shane T., Noel, Bryan, Damron, Catherine, Holcomb, Diana M., Slade, Edward A., Van Arsdall, Jeanne, Bicknell, Lisa, Buck, Michelle, Stone, Thomas W., Farooq, Amina, Parsons, Brook, Singh, Harinderjit, Ivey, Ken, Foster, Lindsay Allison, Woodward, Michele, Ortiz, Siobhan O., Bailey, Thomas, Mynampati A, Bharani Krishna, White, Cheryl L., Hamdani, Ghulam Shabbir, Smith, Jazzmin N., Chalam, Kakarla V., Sambhav, Kumar, Babaria, Romesh, Grover, Sandeep, Carroll, Catherine, Chau, Felix Y., Lim, Jennifer I., Talasnik, Lauren A., Janowicz, Mark, Stankovic, Natasa, Berlatsky, Sarah L., Niec, Marcia, Sun, Jie, Johnson, Tametha, Ovando, Yesenia, Nakoski, Brenda, Mein, Calvin E., Wienecke, Christopher Sean, Castillo, Elaine, Baker, Jaynee, San Roman, Jonathan, Adams, Lydia, Kirschbaum, Lita, Chica, Moises A., Cloudt, Sara L., Moore, Tori R., Sabates, Felix N., Gallimore, Gary S., Chen, Yin C., Swann, Adrienne C., Cadwell, Deborah M., Diddie, Kenneth R., Boisvert, Taryn F., Tessau, Carrie D., Bowers, Jack, Nielsen, Jared S., Rostvold, Jay, Spillman, Jamie, Alliman, Kyle J., Boender, Lisa M., Johnson, Marilyn A., Parker, Marianne, Bix, Paula L., Ridgway, Spencer D., Woehl, Tami Jo, Stonewall, Whitney, Brown, Christopher M., Lema, Gareth M.C., Wiechelt, Luann, Yoganathan, Pradeepa, Boglione, Sandra L., Montesclaros, Chris A., Mangham, Cory, Karsaliya, Gopal, Le, Phillip V., Wong, Robert W., Godfrey, Anne Marie, Kuzmanovic, Aleksandra, Kirker, Andrew William, Harrison, Bryan, Forooghian, Farzin, Elvena, Garnet Louise, Hall, Laura J., Turhal, Bilgin, Brown, Ian, Kotei, Isaac A., Chen, Lina, Brent, Michael Henry, Moon, Michelle, Sutakovic, Olivera, Chang, Angela, Godfrey, Anne-Marie, Albiani, David, Maberley, David A.L., Navajas, Eduardo Vitor, Grant, Kelly, Tran, Khoi A., Jovanovic, Mira, Cao, Sijia, Wiens, Theresa, Kozbial, Andrzej, Orlin, Anton, Lenane, Courtney Nichole, Herder, Susan P., Kiss, Szilard, Reeves, Tom, Cruess, Alan F., Dean, Andrea, Hoskin-Mott, Ann, Morrison, Christine, Caldwell, Meggie D., Hynes, Mitzi, Gupta, R. Rishi, Durling, Stacey, MacDonnell, Trina, Beck, Roy W., Baptista, Alyssa, Beaulieu, Wesley T., Calhoun, Claire T., Constantine, Sharon R., Correia, Isabella, Dale, Brian B., Dupre, Simone S., Franklin, Crystal A., Galusic, Sandra, Huggins, Meagan, Hunter, Brenda L., Johnson, Paula A., Josic, Kristin, Kelly, Brittany, Maguire, Maureen G., Meadows, Britney, Stockdale, Cynthia R., Zokruah, Alice, Bhargava, Sangeeta, Barkmeier, Andrew J., Baskin, Darrell, Blodi, Barbra, Chew, Emily, Ferris, Frederick L., Jaffe, Glenn J., Bressler, Neil M., Lujan, Brandon, Tolls, Dorothy, Sheridan, Daniel, Pitoc, Cloyd M., Anne C Aquino, Lizzie, Salva, Claude Michael G., Lewis, Drew, Stainback, Jeffery, Makkena, Vijaya, Winter, Katrina, Mora, Adiel, Harrington, Chris, Vinh, Doc-Lap, Ehlers, Justis P., Yordi, Sari, Martin, Alison, Srivastava, Sunil K., and Sun, Jenifer K.

Published
2024
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41. 3-hour genome sequencing and targeted analysis to rapidly assess genetic risk

Author

Zalusky, Miranda P.G., Gustafson, Jonas A., Bohaczuk, Stephanie C., Mallory, Ben, Reed, Paxton, Wenger, Tara, Beckman, Erika, Chang, Irene J., Paschal, Cate R., Buchan, Jillian G., Lockwood, Christina M., Puia-Dumitrescu, Mihai, Garalde, Daniel R., Guillory, Joseph, Markham, Androo J., Bamshad, Michael J., Eichler, Evan E., Stergachis, Andrew B., and Miller, Danny E.

Published
2024
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43. 3-hour genome sequencing and targeted analysis to rapidly assess genetic risk

Author

Miranda P.G. Zalusky, Jonas A. Gustafson, Stephanie C. Bohaczuk, Ben Mallory, Paxton Reed, Tara Wenger, Erika Beckman, Irene J. Chang, Cate R. Paschal, Jillian G. Buchan, Christina M. Lockwood, Mihai Puia-Dumitrescu, Daniel R. Garalde, Joseph Guillory, Androo J. Markham, Michael J. Bamshad, Evan E. Eichler, Andrew B. Stergachis, and Danny E. Miller

Subjects
Genetic testing, Genomics, Long-read sequencing, Nanopore, Ultrarapid sequencing, Genetics, QH426-470, Medicine
Abstract

Purpose: Rapid genetic testing in the critical care setting may guide diagnostic evaluation, direct therapies, and help families and care providers make informed decisions about goals of care. We tested whether a simplified DNA extraction and library preparation process would enable us to perform ultrarapid assessment of genetic risk for a Mendelian condition, based on information from an affected sibling, using long-read genome sequencing and targeted analysis. Methods: Following extraction of DNA from cord blood and rapid library preparation, genome sequencing was performed on an Oxford Nanopore PromethION. FASTQ files were generated from original sequencing data in near real-time and aligned to a reference genome. Variant calling and analysis were performed at timed intervals. Results: We optimized the DNA extraction and library preparation methods to create sufficient library for sequencing from 500 μL of blood. Real-time, targeted analysis was performed to determine that the newborn was neither affected nor a heterozygote for variants underlying a Mendelian condition. Phasing of the target region and prior knowledge of the affected haplotypes supported our interpretation despite a low level of coverage at 3 hours of life. Conclusion: This proof-of-concept experiment demonstrates how prior knowledge of haplotype structure or familial variants can be used to rapidly evaluate an individual at risk for a genetic disease. Although ultrarapid sequencing remains both complex and cost prohibitive, our method is more easily automated than prior approaches and uses smaller volumes of blood and thus may be more easily adopted for future studies of ultrarapid genome sequencing in the clinical setting.

Published
2024
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44. Calibration of the MaGIXS experiment I: Calibration of the X-ray source at the X-ray and Cryogenic Facility (XRCF)

Author

Athiray, P. S., Winebarger, Amy R., Champey, Patrick, Kobayashi, Ken, Vigil, Genevieve D., Haight, Harlan, Johnson, Steven, Bethge, Christian, Rachmeler, Laurel A., Savage, Sabrina, Beabout, Brent, Beabout, Dyana, Hogue, William, Guillory, Anthony, Siler, Richard, Wright, Ernest, and Kegley, Jeffrey

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Marshall Grazing Incidence Spectrometer {\it MaGIXS} is a sounding rocket experiment that will observe the soft X-ray spectrum of the Sun from 24 - 6.0 \AA\ (0.5 - 2.0 keV) and is scheduled for launch in 2021. Component and instrument level calibrations for the {\it MaGIXS} instrument are carried out using the X-ray and Cryogenic Facility (XRCF) at NASA Marshall Space Flight Center. In this paper, we present the calibration of the incident X-ray flux from the electron impact source with different targets at the XRCF using a CCD camera; the photon flux at the CCD was low enough to enable its use as a "photon counter" i.e. the ability to identify individual photon hits and calculate their energy. The goal of this paper is two-fold: 1) to confirm that the flux measured by the XRCF beam normalization detectors is consistent with the values reported in the literature and therefore reliable for {\it MaGIXS} calibration and 2) to develop a method of counting photons in CCD images that best captures their number and energy, Comment: 11 pages, 8 figures, Accepted for publication in The Astrophysical Journal

Published
2020
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45. Combating Anti-Blackness in the AI Community

Author

Guillory, Devin

Subjects
Computer Science - Computers and Society, I.2.0
Abstract

In response to a national and international awakening on the issues of anti-Blackness and systemic discrimination, we have penned this piece to serve as a resource for allies in the AI community who are wondering how they can more effectively engage with dismantling racist systems. This work aims to help elucidate areas where the AI community actively and passively contributes to anti-Blackness and offers actionable items on ways to reduce harm.

Published
2020

46. Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning

Author

Luo, Zhekun, Guillory, Devin, Shi, Baifeng, Ke, Wei, Wan, Fang, Darrell, Trevor, and Xu, Huijuan

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

Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag's label is known, the main challenge is assigning which key instances within the bag to trigger the bag's label. Most previous models use attention-based approaches applying attentions to generate the bag's representation from instances, and then train it via the bag's classification. These models, however, implicitly violate the MIL assumption that instances in negative bags should be uniformly negative. In this work, we explicitly model the key instances assignment as a hidden variable and adopt an Expectation-Maximization (EM) framework. We derive two pseudo-label generation schemes to model the E and M process and iteratively optimize the likelihood lower bound. We show that our EM-MIL approach more accurately models both the learning objective and the MIL assumptions. It achieves state-of-the-art performance on two standard benchmarks, THUMOS14 and ActivityNet1.2., Comment: Accepted at European Conference on Computer Vision (ECCV), 2020

Published
2020

47. Argonaute3-SF3B3 complex controls pre-mRNA splicing to restrain type 2 immunity

Author

Riccardo Guidi, Christopher Wedeles, Daqi Xu, Krzysztof Kolmus, Sarah E. Headland, Grace Teng, Joseph Guillory, Yi Jimmy Zeng, Tommy K. Cheung, Subhra Chaudhuri, Zora Modrusan, Yuxin Liang, Stuart Horswell, Benjamin Haley, Sascha Rutz, Christopher Rose, Yvonne Franke, Donald S. Kirkpatrick, Jason A. Hackney, and Mark S. Wilson

Subjects
CP: Molecular biology, CP: Immunology, Biology (General), QH301-705.5
Abstract

Summary: Argonaute (AGO) proteins execute microRNA (miRNA)-mediated gene silencing. However, it is unclear whether all 4 mammalian AGO proteins (AGO1, AGO2, AGO3, and AGO4) are required for miRNA activity. We generate Ago1, Ago3, and Ago4-deficient mice (Ago134Δ) and find AGO1/3/4 to be redundant for miRNA biogenesis, homeostasis, or function, a role that is carried out by AGO2. Instead, AGO1/3/4 regulate the expansion of type 2 immunity via precursor mRNA splicing in CD4+ T helper (Th) lymphocytes. Gain- and loss-of-function experiments demonstrate that nuclear AGO3 interacts directly with SF3B3, a component of the U2 spliceosome complex, to aid global mRNA splicing, and in particular the isoforms of the gene Nisch, resulting in a dysregulated Nisch isoform ratio. This work uncouples AGO1, AGO3, and AGO4 from miRNA-mediated RNA interference, identifies an AGO3:SF3B3 complex in the nucleus, and reveals a mechanism by which AGO proteins regulate inflammatory diseases.

Published
2023
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48. Changes in industry marketing of electronic nicotine delivery systems on social media following FDA's prioritized enforcement policy: a content analysis of Instagram and Twitter posts

Author

Jamie Guillory, Sarah Trigger, Ashley Ross, Stephanie Lane, Annice Kim, James Nonnemaker, Sherry T. Liu, Kimberly Snyder, and Janine Delahanty

Subjects
electronic nicotine delivery systems, tobacco industry, policy, marketing, Twitter, Instagram, Communication. Mass media, P87-96
Abstract

BackgroundIn February 2020, FDA prioritized enforcement of flavored (other than tobacco- or menthol-flavored) cartridge-based electronic nicotine delivery systems (ENDS) without premarket authorization. To explore potential marketing changes, we conducted a content analysis of brands' social media posts, comparing devices and flavors before/after the policy.MethodsWe sampled up to three posts before (November 6, 2019–February 5, 2020) and after the policy (February 6–May 6, 2020) from brands' Instagram (n = 33) and Twitter (n = 30) accounts (N = 302 posts). Two analysts coded posts for device type and flavor. We summarized coded frequencies by device, flavor, and device-flavor combination, and by platform.ResultsIn posts mentioning devices and flavors, those featuring flavored (other than tobacco- or menthol-flavored) cartridge-based devices (before: 2.5%; after: 0%) or tobacco- or menthol-flavored cartridge-based devices (before: 0%; after: 2.8%) were uncommon while any flavor disposables were most common (before: 10.8%; after: 14.6%) particularly after the policy. Half of posts featured devices without flavor (before: 50.0%; after: 50.0%) and one-fifth had no device or flavor references (before: 21.5%; after: 18.8%).ConclusionsIn the months before and after the policy, it appears ENDS brands were not using social media to market flavored (excluding tobacco- or menthol-flavored) cartridge-based ENDS (i.e., explicitly prioritized) or tobacco- or menthol-flavored cartridge-based devices (i.e., explicitly not prioritized). Brands were largely not advertising specific flavored products, but rather devices without mentioning flavor (e.g., open/refillable, disposable devices). We presented a snapshot of what consumers saw on social media around the time of the policy, which is important to understanding strategies to reach consumers in an evolving ENDS landscape.

Published
2023
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49. Argonaute3-SF3B3 complex controls pre-mRNA splicing to restrain type 2 immunity

Author

Guidi, Riccardo, Wedeles, Christopher, Xu, Daqi, Kolmus, Krzysztof, Headland, Sarah E., Teng, Grace, Guillory, Joseph, Zeng, Yi Jimmy, Cheung, Tommy K., Chaudhuri, Subhra, Modrusan, Zora, Liang, Yuxin, Horswell, Stuart, Haley, Benjamin, Rutz, Sascha, Rose, Christopher, Franke, Yvonne, Kirkpatrick, Donald S., Hackney, Jason A., and Wilson, Mark S.

Published
2023
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