METHODS: Fourteen datasets extracted from three published papers were used in a meta-analysis to examine the cyclic behaviour of the Arabidopsis thaliana photosynthesis-related gene CAB2 and the clock oscillator genes TOC1 and LHY in T cycles and N-H cycles.
KEY RESULTS: Changes in the rhythms of CAB2, TOC1 and LHY in plants subjected to non-24-h light:dark cycles matched the hypothesized changes in their behaviour as predicted by the solar clock model, thus validating it. The analysis further showed that TOC1 expression peaked ∼5·5 h after mid-day, CAB2 peaked close to noon, while LHY peaked ∼7·5 h after midnight, regardless of the cycle period, the photoperiod or the light:dark period ratio. The solar clock model correctly predicted the zeitgeber timing of these genes under 11 different lighting regimes comprising combinations of seven light periods, nine dark periods, four cycle periods and four light:dark period ratios. In short cycles that terminated before LHY could be expressed, the solar clock correctly predicted zeitgeber timing of its expression in the following cycle.
CONCLUSIONS: Regulation of gene phases by the solar clock enables the plant to tell the time, by which means a large number of genes are regulated. This facilitates the initiation of gene expression even before the arrival of sunrise, sunset or noon, thus allowing the plant to 'anticipate' dawn, dusk or mid-day respectively, independently of the photoperiod.
MATERIALS/METHODS: Multivariable models developed to predict atomised and generalised urinary symptoms, both acute and late, were considered for validation using a dataset representing 754 participants from the TROG 03.04-RADAR trial. Endpoints and features were harmonised to match the predictive models. The overall performance, calibration and discrimination were assessed.
RESULTS: 14 models from four publications were validated. The discrimination of the predictive models in an independent external validation cohort, measured using the area under the receiver operating characteristic (ROC) curve, ranged from 0.473 to 0.695, generally lower than in internal validation. 4 models had ROC >0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients.
CONCLUSIONS: Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models.