University of Alberta

About: University of Alberta is a education organization based out in Edmonton, Alberta, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 65403 authors who have published 154847 publications receiving 5358338 citations. The organization is also known as: Ualberta & UAlberta.

1H, 13C and 15N random coil NMR chemical shifts of the common amino acids. I. Investigations of nearest-neighbor effects.

Abstract: In this study we report on the 1H, 13C and 15N NMR chemical shifts for the random coil state and nearest-neighbor sequence effects measured from the protected linear hexapeptide Gly-Gly-X-Y-Gly-Gly (where X and Y are any of the 20 common amino acids). We present data for a set of 40 peptides (of the possible 400) including Gly-Gly-X-Ala-Gly-Gly and Gly-Gly-X-Pro-Gly-Gly, measured under identical aqueous conditions. Because all spectra were collected under identical experimental conditions, the data from the Gly-Gly-X-Ala-Gly-Gly series provide a complete and internally consistent set of 1H, 13C and 15N random coil chemical shifts for all 20 common amino acids. In addition, studies were also conducted into nearest-neighbor effects on the random coil shift arising from a variety of X and Y positional substitutions. Comparisons between the chemical shift measurements obtained from Gly-Gly-X-Ala-Gly-Gly and Gly-Gly-X-Pro-Gly-Gly reveal significant systematic shift differences arising from the presence of proline in the peptide sequence. Similarly, measurements of the chemical shift changes occurring for both alanine and proline (i.e., the residues in the Y position) are found to depend strougly on the type of amino acid substituted into the X position. These data lend support to the hypothesis that sequence effects play a significant role in determining peptide and protein chemical shifts.

Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

Abstract: Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.

Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury.

Abstract: Introduction: Acute kidney injury (AKI) can evolve quickly and clinical measures of function often fail to detect AKI at a time when interventions are likely to provide benefit. Identifying early markers of kidney damage has been difficult due to the complex nature of human AKI, in which multiple etiologies exist. The objective of this study was to identify and validate novel biomarkers of AKI. Methods: We performed two multicenter observational studies in critically ill patients at risk for AKI - discovery and validation. The top two markers from discovery were validated in a second study (Sapphire) and compared to a number of previously described biomarkers. In the discovery phase, we enrolled 522 adults in three distinct cohorts including patients with sepsis, shock, major surgery, and trauma and examined over 300 markers. In the Sapphire validation study, we enrolled 744 adult subjects with critical illness and without evidence of AKI at enrollment; the final analysis cohort was a heterogeneous sample of 728 critically ill patients. The primary endpoint was moderate to severe AKI (KDIGO stage 2 to 3) within 12 hours of sample collection. Results: Moderate to severe AKI occurred in 14% of Sapphire subjects. The two top biomarkers from discovery were validated. Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2), both inducers of G1 cell cycle arrest, a key mechanism implicated in AKI, together demonstrated an AUC of 0.80 (0.76 and 0.79 alone). Urine [TIMP-2]·[IGFBP7] was significantly superior to all previously described markers of AKI (P 0.72. Furthermore, [TIMP2]·[IGFBP7] significantly improved risk stratification when added to a nine-variable clinical model when analyzed using Cox proportional hazards model, generalized estimating equation, integrated discrimination improvement or net reclassification improvement. Finally, in sensitivity analyses [TIMP-2]·[IGFBP7] remained significant and superior to all other markers regardless of changes in reference creatinine method.