Meta‐analysis of flow modeling performances—to build a matching system between catchment complexity and model types

Hydrological models play a significant role in modelling river flow for decision making support in water resource management. In the past decades, many researchers have made a great deal of efforts in calibrating and validating various models, with each study being focused on one or two models. As a result, there is a lack of comparative analysis on the performance of those models to guide hydrologists to choose appropriate models for the individual climate and physical conditions. This paper describes a two-level meta-analysis to develop a matching system between catchment complexity (based on catchment significant features (CSFs)) and model types. The intention is to use the available CSF information for choosing the most suitable model type for a given catchment. In this study, the CSFs include the elements of climate, soil type, land cover and catchment scale. Specific choices of model types in small and medium catchments are further explored with all CSF information obtained. In particular, it is interesting to find that semi-distributed models are the most suitable model type for catchments with the area over 3000 km2, regardless of other CSFs. The potential methodology for expanding the matching system between catchment complexity and model complexity is discussed. Copyright © 2014 John Wiley & Sons, Ltd

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs