(DOCX) Neighborhood Properties Are Important Determinants of Temperature Sensitive Mutations Temperature-sensitive (TS) mutants are powerful tools to study gene function in vivo. These mutants exhibit wild-type activity at permissive temperatures and reduced activity at restrictive temperatures. Although random mutagenesis can be used to generate TS mutants, the procedure is laborious and unfeasible in multicellular organisms. Further, the underlying molecular mechanisms of the TS phenotype are poorly understood. To elucidate TS mechanisms, we used a machine learning method–logistic regression–to investigate a large number of sequence and structure features. We developed and tested 133 features, describing properties of either the mutation site or the mutation site neighborhood. We defined three types of neighborhood using sequence distance, Euclidean distance, and topological distance. We discovered that neighborhood features outperformed mutation site features in predicting TS mutations. The most predictive features suggest that TS mutations tend to occur at buried and rigid residues, and are located at conserved protein domains. The environment of a buried residue often determines the overall structural stability of a protein, thus may lead to reversible activity change upon temperature switch. We developed TS prediction models based on logistic regression and the Lasso regularized procedure. Through a ten-fold cross-validation, we obtained the area under the curve of 0.91 for the model using both sequence and structure features. Testing on independent datasets suggested that the model predicted TS mutations with a 50% precision. In summary, our study elucidated the molecular basis of TS mutants and suggested the importance of neighborhood properties in determining TS mutations. We further developed models to predict TS mutations derived from single amino acid substitutions. In this way, TS mutants can be efficiently obtained through experimentally introducing the predicted mutations. 0.38 -0.03 0.48 1.11 0.00 Turn breaker: turn is defined by DSSP [2] 0.58 -0.03 0.48 0.32 0.00 * ACC = accuracy, MCC = Matthews correlation coefficient, AUC = area under the curve, KL = Kullback-Leibler divergence, DD = distribution distance. These values were calculated from a ten-fold cross-validation of each feature. Missing and accounted for: gaps and areas of wealth in the public health review literature BACKGROUND: High-quality review evidence is useful for informing and influencing public health policy and practice decisions. However, certain topic areas lack representation in terms of the quantity and quality of review literature available. The objectives of this paper are to identify the quantity, as well as quality, of review-level evidence available on the effectiveness of public health interventions for public health decision makers. METHODS: Searches conducted on http://www.health-evidence.ca produced an inventory of public health review literature in 21 topic areas. Gaps and areas of wealth in the review literature, as well as the proportion of reviews rated methodologically strong, moderate, or weak were identified. The top 10 topic areas of interest for registered users and visitors of http://www.health-evidence.ca were extracted from user profile data and Google Analytics. RESULTS: Registered users' top three interests included: 1) healthy communities, 2) chronic diseases, and 3) nutrition. The top three preferences for visitors included: 1) chronic diseases, 2) physical activity, and 3) addiction/substance use. All of the topic areas with many (301+) available reviews were of interest to registered users and/or visitors (mental health, physical activity, addiction/substance use, adolescent health, child health, nutrition, adult health, and chronic diseases). Conversely, the majority of registered users and/or visitors did not have preference for topic areas with few (≤ 150) available reviews (food safety and inspection, dental health, environmental health) with the exception of social determinants of health and healthy communities. Across registered users' and visitors' topic areas of preference, 80.2% of the reviews were of well-done methodological quality, with 43.5% of reviews having a strong quality rating and 36.7% a moderate review quality rating. CONCLUSIONS: In topic areas in which many reviews are available, higher level syntheses are needed to guide policy and practice. For other topic areas with few reviews, it is necessary to determine whether primary study evidence exists, or is needed, so that reviews can be conducted in the future. Considering that less than half of the reviews available on http://www.health-evidence.ca are of strong methodological quality, the quality of the review-level evidence needs to improve across the range of public health topic areas. Using Systematic Reviews A systematic review consists of an examination of all of the primary studies on a topic, which includes searching for, collating, and assessing the studies, to establish conclusive evidence about a topic [1] . The Cochrane Collaboration is an international body that produces systematic reviews of primary research at the highest standard, and as such, this is a commonly accepted definition of systematic reviews. Evidence-informed public health advocates the incorporation of the best available scientific evidence into decision making [2] . Review level evidence is an important part of evidence-informed public health decision making, since reviews synthesize the results of individual studies, providing a more accurate estimate of the effects of an intervention [3] . Rigorous synthesis of primary research minimizes bias [4] [5] [6] , explains differences among studies relating to the same research question [7, 8] , and presents more precise and consistent summary statistics than the effect sizes found in individual studies [5, [8] [9] [10] . Well-conducted reviews provide high-quality, accurate evidence [4] [5] [6] , increasing decision-makers' confidence in the strength of the review evidence and in applying the findings in practice [4] . Public health decision makers prefer using systematic reviews to assist in decision-making given that review level evidence saves time and is more efficient compared to using primary studies [6, 11] . Systematic review findings can be generalized to a larger sample, providing a great evidence base for users; such external validity is essential to ensure adaptability and applicability of evidence-based interventions into the local context [12] . Consequently, systematic reviews are useful for informing and influencing public health policy and practice decisions [7, 13, 14] .