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This paper offers a guide for those seeking to integrate the Scholarship of Teaching and Learning (SoTL) into higher education institutions to improve the quality of student learning. The authors posit that weaving SoTL into institutional cultures requires the coordinated actions of individuals working in linked social net works rather than individuals acting in isolation. analyzing both the barriers and potential pathways to integrating SoTL into institutional cultures, the authors provide a conceptual model along with examples of practical strategies for overcoming resistance to change within institutions. The paper provides examples from a variety of different international contexts to show how incentives and other non-coercive measures can motivate faculty and administrators to weave SoTL into institutional fabrics. Drawing on social network theory and the concept of communities of practice , the paper presents a model with attendant strategies for disseminating SoTL values and practices across all three levels of postsec ondary institutions: the micro, the meso, and the macro. The authors argue that for SoTL to take root in organizational cultures, there must be 1) effective communication and dissemination of SoTL activity across all levels, 2) well established social networks and links between these levels (nodes), and 3) sustained support by senior administration. The authors conclude by suggesting ways their model could be tested. Key WOrDS model for integrating SoTL, SoTL and institutional culture, social networks in higher education, change in higher education A departmental committee has been asked to develop a policies and procedures document in response to growing concerns that plagiarism is on the rise. There was a time when

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Dynamic programming algorithms that predict RNA secondary structure by minimizing the free energy have had one important limitation. They were able to predict only one optimal structure. Given the uncertainties of the thermodynamic data and the effects of proteins and other environmental factors on structure, the optimal structure predicted by these methods may not have biological significance. We present a dynamic programming algorithm that can determine optimal and suboptimal secondary structures for an RNA. The power and utility of the method is demonstrated in the folding of the intervening sequence of the rRNA of Tetrahymena. By first identifying the major secondary structures corresponding to the lowest free energy minima, a secondary structure of possible biological significance is derived.