The practice of service learning in local school–community contexts.

In this article, the authors set out to examine the ways in which service learning is practiced, perceived, and sustained in school settings. Drawing on extensive qualitative case studies, the authors highlight three cases of schools at varying levels of integration of service into their curriculum. Their history of offering service learning is summarized. Participant perceptions regarding the role of service learning in student learning and concerns regarding the relationship between service learning and student academic achievement are explored. The authors conclude that service learning is practiced amidst a series of complex, and often times conflicting, assumptions regarding the aims of education and the proper formats through which student achievement should be assessed

Management Capabilities and Policy Capacity Where are the Links?

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SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles

<p>Abstract</p> <p>Background</p> <p>Post-translational modifications have a substantial influence on the structure and functions of protein. Post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. Accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. In this study, we propose a new machine learning based protein phosphorylation site predictor, SiteSeek. SiteSeek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. The newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors.</p> <p>Results</p> <p>The performance of the proposed model was compared to nine existing different machine learning models and four widely known phosphorylation site predictors with the newly proposed PS-Benchmark_1 dataset to contrast their accuracy, sensitivity, specificity and correlation coefficient. SiteSeek showed better predictive performance with 86.6% accuracy, 83.8% sensitivity, 92.5% specificity and 0.77 correlation-coefficient on the four main kinase families (CDK, CK2, PKA, and PKC).</p> <p>Conclusion</p> <p>Our newly proposed methods used in SiteSeek were shown to be useful for the identification of protein phosphorylation sites as it performed much better than widely known predictors on the newly built PS-Benchmark_1 dataset.</p

Re-visiting Meltsner: Policy Advice Systems and the Multi-Dimensional Nature of Professional Policy Analysis

10.2139/ssrn.15462511-2