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Objective This study aimed to quantify the effect of social media posts on study enrollment among children with mild coronavirus disease 2019 (COVID-19). Methods The primary outcome was weekly study enrollments analyzed using a run chart. A secondary analysis used linear regression to assess study enrollments two days before and after a social media post, adjusted for the statewide pediatric seven-day-average severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case rate, social media posting day, and the interaction of these two variables. Results In seven months before social media posting, only eight patients were enrolled. One week after social media posting began, the median weekly enrollment increased (0 to 3). In the regression model, neither social media post day nor the pediatric SARS-CoV-2 case rate was significantly associated with enrollment rate. However, the interaction of a post day and the pediatric case rate was significant. Conclusion Social media posts significantly increased enrollment among children with mild COVID-19 in a prospective study. This effect was amplified by the presence of high community case rates during the Omicron wave.

The ability to transform matter between numerous physical states or shapes without wires or external devices is a major challenge for robotics and materials design. Organisms can transform their shapes using biomolecules carrying specific information and localize at sites where transitions occur. Here, we introduce gel automata, which likewise can transform between a large number of prescribed shapes in response to a combinatorial library of biomolecular instructions. Gel automata are centimeter-scale materials consisting of multiple micro-segments. A library of DNA activator sequences can each reversibly grow or shrink different micro-segments by polymerizing or depolymerizing within them. We develop DNA activator designs that maximize the extent of growth and shrinking, and a photolithography process for precisely fabricating gel automata with elaborate segmentation patterns. Guided by simulations of shape change and neural networks that evaluate gel automata designs, we create gel automata that reversibly transform between multiple, wholly distinct shapes: four different letters and every even or every odd numeral. The sequential and repeated metamorphosis of gel automata demonstrates how soft materials and robots can be digitally programmed and reprogrammed with information-bearing chemical signals. Shape transformation without the need for external devices or wires is desirable for soft robotics, but challenging to achieve. Here, the authors report the development of actuators capable of shape changes by polymerization and depolymerization of DNA activator sequences.

Kirjoittajalista kokonaisuudessaan: Principal investigators John P. Vogel, David F. Garvin, Todd C. Mockler, Jeremy Schmutz, Dan Rokhsar, Michael W. Bevan; DNA sequencing and assembly Kerrie Barry, Susan Lucas, Miranda Harmon-Smith, Kathleen Lail, Hope Tice, Jeremy Schmutz (Leader), Jane Grimwood, Neil McKenzie, Michael W. Bevan; Pseudomolecule assembly and BACend sequencing NaxinHuo, Yong Q.Gu,GerardR. Lazo, OlinD.Anderson, John P. Vogel (Leader), Frank M. You,Ming-Cheng Luo, Jan Dvorak, Jonathan Wright, Melanie Febrer, Michael W. Bevan, Dominika Idziak, Robert Hasterok, David F. Garvin; Transcriptome sequencing and analysis Erika Lindquist, Mei Wang, Samuel E. Fox, Henry D. Priest, Sergei A. Filichkin, Scott A. Givan, Douglas W. Bryant, JeffH.Chang, ToddC.Mockler (Leader), HaiyanWu, Wei Wu, An-Ping Hsia, Patrick S. Schnable, Anantharaman Kalyanaraman, Brad Barbazuk, Todd P.Michael, Samuel P.Hazen, JenniferN. Bragg, Debbie Laudencia-Chingcuanco, John P. Vogel, David F. Garvin, Yiqun Weng, Neil McKenzie, Michael W. Bevan; Gene analysis and annotation Georg Haberer, Manuel Spannagl, Klaus Mayer (Leader), Thomas Rattei, ThereseMitros, Dan Rokhsar, Sang-Jik Lee, Jocelyn K. C. Rose, Lukas A. Mueller, Thomas L. York; Repeats analysis Thomas Wicker (Leader), Jan P. Buchmann, Jaakko Tanskanen, Alan H. Schulman (Leader), Heidrun Gundlach, Jonathan Wright, Michael Bevan, Antonio Costa de Oliveira, Luciano da C. Maia, William Belknap, Yong Q. Gu, Ning Jiang, Jinsheng Lai, Liucun Zhu, JianxinMa, Cheng Sun, Ellen Pritham; Comparative genomics Jerome Salse (Leader), Florent Murat, Michael Abrouk, Georg Haberer, Manuel Spannagl, Klaus Mayer, Remy Bruggmann, Joachim Messing, Frank M. You, Ming-Cheng Luo, Jan Dvorak; Small RNA analysis Noah Fahlgren, Samuel E. Fox, Christopher M. Sullivan, Todd C. Mockler, James C. Carrington, Elisabeth J. Chapman, Greg D.May, Jixian Zhai, Matthias Ganssmann, Sai Guna Ranjan Gurazada, Marcelo German, Blake C. Meyers, Pamela J. Green (Leader); Manual annotation and gene family analysis Jennifer N. Bragg, Ludmila Tyler, Jiajie Wu, Yong Q. Gu, Gerard R. Lazo, Debbie Laudencia-Chingcuanco, James Thomson, John P. Vogel (Leader), Samuel P. Hazen, Shan Chen, Henrik V. Scheller, JesperHarholt, Peter Ulvskov, Samuel E. Fox, Sergei A. Filichkin, Noah Fahlgren, Jeffrey A. Kimbrel, Jeff H. Chang, Christopher M. Sullivan, Elisabeth J. Chapman, James C. Carrington, Todd C. Mockler, Laura E. Bartley, Peijian Cao, Ki-Hong Jung, Manoj K Sharma, Miguel Vega-Sanchez, Pamela Ronald, Christopher D.Dardick, StefanieDe Bodt,Wim Verelst, Dirk Inze, Maren Heese, Arp Schnittger, Xiaohan Yang, Udaya C. Kalluri, GeraldA. Tuskan, ZhihuaHua, Richard D. Vierstra, David F. Garvin, Yu Cui, Shuhong Ouyang, Qixin Sun, Zhiyong Liu, Alper Yilmaz, Erich Grotewold, Richard Sibout, Kian Hematy, Gregory Mouille, Herman Hofte, Todd Michael, JeŽrome Pelloux, Devin O Connor, James Schnable, Scott Rowe, Frank Harmon, Cynthia L. Cass, John C. Sedbrook, Mary E. Byrne, SeanWalsh, Janet Higgins, Michael Bevan, PinghuaLi, ThomasBrutnell, TurgayUnver,Hikmet Budak, Harry Belcram, Mathieu Charles, Boulos Chalhoub, Ivan Baxter

Controlling material shapes using information-bearing molecular signals is central to the creation of autonomous, reconfigurable soft devices. While physical and chemical stimuli can direct simple material swelling, bending, or folding, it has been challenging to direct multi-step shape-change programs crucial for complex, robotic tasks. Here, we demonstrate gel automata—sub-millimeter, photopatterned, highly swellable DNA gels—whose parts grow or shrink in response to easily designed DNA activator sequences, allowing for precisely controlled device articulation. We design and fabricate gel automata that reversibly transform between different letter shapes, and use neural networks to design automata that transform into every even or every odd numeral via designed reconfiguration programs. This sequential and repetitive metamorphosis of materials via chemical reorganization could dramatically advance our ability to manipulate micro-particles, cells, and tissues. Competing Interest Statement RSh, JF, DHG, and RSc are listed as inventors on approved (11,332493) and filed patents related to the technology.