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2. Undesignable RNA Structure Identification via Rival Structure Generation and Structure Decomposition

   Zhou, Tianshuo; Tang, Wei Yu; Mathews, David H; Huang, Liang (May 2024, Springer)

   RNA design is the search for a sequence or set of sequences that will fold into predefined structures, also known as the inverse problem of RNA folding. While numerous RNA design methods have been invented to find sequences capable of folding into a target structure, little attention has been given to the identification of undesignable structures according to the minimum free energy ( ) criterion under the Turner model. In this paper, we address this gap by first introducing mathematical theorems outlining sufficient conditions for recognizing undesignable structures, then proposing efficient algorithms, guided by these theorems, to verify the undesignability of RNA structures. Through the application of these theorems and algorithms to the Eterna100 puzzles, we demonstrate the ability to efficiently establish that 15 of the puzzles indeed fall within the category of undesignable structures. In addition, we provide specific insights from the study of undesignability, in the hope that it will enable more understanding of RNA folding and RNA design. Availability: Our source code is available at https://github.com/shanry/RNA-Undesign. 

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5. Social-Structure Learning

   https://doi.org/10.1177/0963721420924481  

   Gershman, Samuel J.; Cikara, Mina (October 2020, Current Directions in Psychological Science)

   null (Ed.)

   Social-structure learning is the process by which social groups are identified on the basis of experience. Building on models of structure learning in other domains, we formalize this problem within a Bayesian framework. According to this framework, the probabilistic assignment of individuals to groups is computed by combining information about individuals with prior beliefs about group structure. Experiments with adults and children provide support for this framework, ruling out alternative accounts based on dyadic similarity. More broadly, we highlight the implications of social-structure learning for intergroup cognition, stereotype updating, and coalition formation. 

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