3 results
Contributors
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- By Dor Abrahamson, Jerry Andriessen, Roger Azevedo, Michael Baker, Ryan Baker, Sasha Barab, Carl Bereiter, Susan Bridges, Mario Carretero, Carol K. K. Chan, Clark A. Chinn, Paul Cobb, Allan Collins, Kevin Crowley, Elizabeth A. Davis, Chris Dede, Sharon J. Derry, Andrea A. diSessa, Michael Eisenberg, Yrjö Engeström, Noel Enyedy, Barry J. Fishman, Ricki Goldman, James G. Greeno, Erica Rosenfeld Halverson, Cindy E. Hmelo-Silver, Michael J. Jacobson, Sanna Järvelä, Yasmin B. Kafai, Yael Kali, Manu Kapur, Paul A. Kirschner, Karen Knutson, Timothy Koschmann, Joseph S. Krajcik, Carol D. Lee, Peter Lee, Robb Lindgren, Jingyan Lu, Richard E. Mayer, Naomi Miyake, Na’ilah Suad Nasir, Mitchell J. Nathan, Narcis Pares, Roy Pea, James W. Pellegrino, William R. Penuel, Palmyre Pierroux, Brian J. Reiser, K. Ann Renninger, Ann S. Rosebery, R. Keith Sawyer, Marlene Scardamalia, Anna Sfard, Mike Sharples, Kimberly M. Sheridan, Bruce L. Sherin, Namsoo Shin, George Siemens, Peter Smagorinsky, Nancy Butler Songer, James P. Spillane, Kurt Squire, Gerry Stahl, Constance Steinkuehler, Reed Stevens, Daniel Suthers, Iris Tabak, Beth Warren, Uri Wilensky, Philip H. Winne, Carmen Zahn
- Edited by R. Keith Sawyer, University of North Carolina, Chapel Hill
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- Book:
- The Cambridge Handbook of the Learning Sciences
- Published online:
- 05 November 2014
- Print publication:
- 17 November 2014, pp xv-xviii
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Contributors
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- By Jon G. Allen, Robert F Anda, Susan L. Andersen, Carl M. Anderson, Wendy d’ Andrea, Tal Astrachan, Anthony W. Bateman, Carla Bernardes, Renato Borgatti, Bekh Bradley, J. Douglas Bremner, John Briere, Amy F. Buckley, Jean-Francois Bureau, Kathleen M. Chard, Dennis Charney, Anthony Charuvastra, Jeewook Choi, Marylene Cloitre, Melody D. Combs, Constance J. Dalenberg, Martin J. Dorahy, Michael D. De Bellis, Anne P. DePrince, Erin C. Dunn, Vincent J. Felitti, Philip A. Fisher, Peter Fonagy, Julian D. Ford, Amit Goldenberg, Megan R. Gunnar, Udi Harari, Felicia Heidenreich, Christine Heim, Judith Herman MD, Monica Hodges, Shlomit Jacobson-Pick, Joan Kaufman, Karestan C. Koenen, Ruth A. Lanius, Jamie L. LaPrairie, Alicia F. Lieberman, Richard J. Loewenstein, Sonia J. Lupien MD, Karlen Lyons-Ruth, Jodi Martin, Bruce McEwen, Alexander C. McFarlane, Rosario Montirosso, Charles B. Nemeroff, Pat Ogden, Fatih Ozbay, Clare Pain, Kelsey Paulson, Oxana G. Palesh, Ms. Keren Rabi, Gal Richter-Levin, Andrea L. Roberts, Cécile Rousseau, Cécile Rousseau, Monica Ruiz-Casares, Christian Schmahl, Allan N. Schore, Sally B. Seraphin, Vansh Sharma, Yi-Shin Sheu, Kelly Skelton, Steven Southwick, David Spiegel, Deborah M. Stone, Nathan Szajnberg, Martin H. Teicher, Akemi Tomoda, Ed Tronick, Onno van der Hart, Bessel van der Kolk, Eric Vermetten, Tamara Weiss, Victor Welzant
- Edited by Ruth A. Lanius, University of Western Ontario, Eric Vermetten, Universiteit Utrecht, The Netherlands, Clare Pain, University of Toronto
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- Book:
- The Impact of Early Life Trauma on Health and Disease
- Published online:
- 03 May 2011
- Print publication:
- 05 August 2010, pp vii-xii
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Using reliability information to annotate RNA secondary structures
- MICHAEL ZUKER, ANN B. JACOBSON
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A number of heuristic descriptors have been developed previously in conjunction with the mfold package that describe the propensity of individual bases to participate in base pairs and whether or not a predicted helix is “well-determined.” They were developed for the “energy dot plot” output of mfold. Two descriptors, P-num and H-num, are used to measure the level of promiscuity in the association of any given nucleotide or helix with alternative complementary pairs. The third descriptor, S-num, measures the propensity of bases to be single-stranded. In the current work, we describe a series of programs that were developed in order to annotate individual structures with “well-definedness” information. We use color annotation to present the information. The programs can annotate PostScript files that are created by the mfold package or the PostScript secondary structure plots produced by the Weiser and Noller program XRNA (Weiser B, Noller HF, 1995, XRNA: Auto-interactive program for modeling RNA, The Center for Molecular Biology of RNA, Santa Cruz, California: University of California; Internet: ftp://fangio.ucsc.edu/pub/XRNA). In addition, these programs can annotate ss files that serve as input to XRNA. The annotation package can also handle structure comparison with a reference structure. This feature can be used to compare predicted structure with a phylogenetically deduced model, to compare two different predicted foldings, and to identify conformational changes that are predicted between wild-type and mutant RNAs.
We provide several examples of application. Predicted structures of two RNase P RNAs were colored with P-num information and further annotated with comparative information. The comparative model of a 16S rRNA was annotated with P-num information from mfold and with base pair probabilities obtained from the Vienna RNA folding package. Further annotation adds comparisons with the optimal foldings obtained from mfold and the Vienna package, respectively. The results of all of these analyses are discussed in the context of the reliability of structure prediction.