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Daily urine protein (UP) loss is a cumbersome but important measurement to guide diagnosis and treatment of renal disease. Spot urine protein-creatinine ratio (UPCR) can been applied to estimate daily proteinuria. However, the correlations between spot and 24h proteinuria remain controversial. In this cross-sectional study, simultaneous collection of 24h and spot urines were performed from 1,039 (derivation cohort) and 204 CKD patients (validation cohort) of Chang Gung Memorial Hospital, from 2007 to 2017. The correlations between spot UPCR and 24h proteinuria were compared. The mean age of patients of derivation and validation cohort was 63 and 55 years and the mean estimated glomerular filtration rate was 62 ± 35 and 59 ± 36 mL/min/m2, respectively. The correlation coefficient was 0.819 between UPCR and 24hUP. Prediction equation was derived as: Log1024hUP (g) = 0.814 x Log10UPCR (mg/mg) + 0.110 x Gender– 0.004 x Age + 0.004 x Body weight (kg) + 0.002 x CKD stage coefficient– 0.018, where CKD stage coefficient: CKD stage G1 = 1, G2 = 2, G3a = 3.1, G3b = 3.2, G4 = 4, G5 = 5. Correlation coefficient between measured and predicted 24hUP among derivation group and validation group is 0.866 and 0.915, respectively. However, the agreement of spot and daily estimates was less pronounced with proteinuria > 3g than lower values in Bland-Altman analysis. Spot UPCR can accurately predict 24hUP in patients with daily proteinuria below 3g. The development of this equation may facilitate estimation of 24hUP in the clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: We estimated the volume of vestibular schwannomas by an ice cream cone formula using thin-sliced magnetic resonance images (MRI) and compared the estimation accuracy among different estimating formulas and between different models. Methods: The study was approved by a local institutional review board. A total of 100 patients with vestibular schwannomas examined by MRI between January 2011 and November 2015 were enrolled retrospectively. Informed consent was waived. Volumes of vestibular schwannomas were estimated by cuboidal, ellipsoidal, and spherical formulas based on a one-component model, and cuboidal, ellipsoidal, Linskey’s, and ice cream cone formulas based on a two-component model. The estimated volumes were compared to the volumes measured by planimetry. Intraobserver reproducibility and interobserver agreement was tested. Estimation error, including absolute percentage error (APE) and percentage error (PE), was calculated. Statistical analysis included intraclass correlation coefficient (ICC), linear regression analysis, one-way analysis of variance, and paired t-tests with P < 0.05 considered statistically significant. Results: Overall tumor size was 4.80 ± 6.8 mL (mean ±standard deviation). All ICCs were no less than 0.992, suggestive of high intraobserver reproducibility and high interobserver agreement. Cuboidal formulas significantly overestimated the tumor volume by a factor of 1.9 to 2.4 (P ≤ 0.001). The one-component ellipsoidal and spherical formulas overestimated the tumor volume with an APE of 20.3% and 29.2%, respectively. The two-component ice cream cone method, and ellipsoidal and Linskey’s formulas significantly reduced the APE to 11.0%, 10.1%, and 12.5%, respectively (all P < 0.001). Conclusion: The ice cream cone method and other two-component formulas including the ellipsoidal and Linskey’s formulas allow for estimation of vestibular schwannoma volume more accurately than all one-component formulas. [ABSTRACT FROM AUTHOR]

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. [ABSTRACT FROM AUTHOR]

Meta-analysis methods that combine -values into a single unified -value are frequently employed to improve confidence in hypothesis testing. An assumption made by most meta-analysis methods is that the -values to be combined are independent, which may not always be true. To investigate the accuracy of the unified -value from combining correlated -values, we have evaluated a family of statistical methods that combine: independent, weighted independent, correlated, and weighted correlated -values. Statistical accuracy evaluation by combining simulated correlated -values showed that correlation among -values can have a significant effect on the accuracy of the combined -value obtained. Among the statistical methods evaluated those that weight -values compute more accurate combined -values than those that do not. Also, statistical methods that utilize the correlation information have the best performance, producing significantly more accurate combined -values. In our study we have demonstrated that statistical methods that combine -values based on the assumption of independence can produce inaccurate -values when combining correlated -values, even when the -values are only weakly correlated. Therefore, to prevent from drawing false conclusions during hypothesis testing, our study advises caution be used when interpreting the -value obtained from combining -values of unknown correlation. However, when the correlation information is available, the weighting-capable statistical method, first introduced by Brown and recently modified by Hou, seems to perform the best amongst the methods investigated. [ABSTRACT FROM AUTHOR]