Association Between Admission Serum Phosphate and Risk of Acute Kidney Injury in Critically Ill Patients with Rhabdomyolysis: A Retrospective Study Based on MIMIC-III

Background The incidence of acute kidney injury(AKI) is high in critically ill patients with rhabdomyolysis. Limited evidence was proved of the association between serum phosphate levels at intensive care unit(ICU) admission and the subsequent risk of AKI. Our study aims to assess if serum phosphate level at admission was independently associated with AKI risk in these patients. Methods This study extracted and analyzed data from Medical Information Mart for Intensive Care-Ⅲ(MIMIC-Ⅲ,version1.4). Rhabdomyolysis was defined as a peak creatine kinase(CK) level higher than 1000 U/L. Serum phosphate was measured within the first day into the ICU and was categorized to 4 groups(<2.6, 2.6-3.4, 3.5-4.5, >4.5mg/dl). AKI was defined according to the Kidney Disease Improving Global Outcome (KDIGO) guidelines. Adjusted smoothing spline plots and multivariate logistic regressions were carried out to explode the association between serum phosphate and risk of AKI. Subgroup analyse was applied to verify the consistency of the association.Results Three hundred and twenty-one patients(67.8% male) diagnosed as rhabdomyolysis were eligible for this analysis. AKI occurred in 204(63.6%) patients of total. Incidence of AKI with admission serum phosphate groups<2.6, 2.6-3.4, 3.5-4.5 and>4.5mg/dl were 52.6%, 56.8%, 68.4% and 75.9%, respectively. Smoothing spline curve showed that there was a positive curve between the elevated phosphate values and increasing risk of AKI, and there was no threshold saturation effect. In multivariate logistic regression, OR was 1.3(95%CI 1.1-1.6, P=0.012, P trend=0.034) after adjusting confounders. Subgroup analyses proved the consistency of the relationship in these patients except in the strata of creatine kinase.Conclusion In rhabdomyolysis patients admitted to ICU, serum phosphate level at admission was independently associated with an increased risk of AKI. As phosphate levels rise, the risk of AKI increased.

The Application of Mixed Smoothing Spline and Fourier Series Model in Nonparametric Regression

In daily life, mixed data patterns are often found, namely, those that change at a certain sub-interval or that follow a repeating pattern in a certain trend. To handle this kind of data, a mixed estimator of a Smoothing Spline and a Fourier Series has been developed. This paper describes a simulation study of the estimator in nonparametric regression and its implementation in the case of poor households. The minimum Generalized Cross Validation (GCV) was used in order to select the best model. The simulation study used generation data with a Uniform distribution and a random error with a symmetrical Normal distribution. The result of the simulation study shows that the larger the sample size n, the better the mixed estimator as a model of nonparametric regression for all variances. The smaller the variance, the better the model for all combinations of samples n. Very poor households are characterized predominantly in their consumption of carbohydrates compared to that of fat and protein. The results of this study suggest that the distribution of assistance to poor households is not the same, because in certain groups there are poor households that consume higher carbohydrates, and some households may consume higher fats.

Seasonal Changes in Climate Variables in Rainfed Crop Areas in the Lerma-Chapala-Santiago Basin, Mexico

This paper shows the effects of changes in the spatial-temporal behavior and phase shift of climate variables on rainfed agriculture in the Lerma-Chapala-Santiago Basin in central Mexico. Specifically, changes in rainfall (R), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed over two 25-year periods (1960 to 1985 and 1986 to 2010). Climate surfaces were generated by interpolation using the thin-plate smoothing spline algorithm in the software ANUSPLIN. Climate data were Fourier-transformed and fitted to a sinusoidal curve model, and changes in amplitude (increase) and phase were analyzed. The temporal behavior (1960–2010) indicated that rainfall was the most stable variable at the monthly level and presented no significant changes. However, Tmax increased by 2°C in the final period, and Tmin increased by 0.7°C at the end of the final period. The basin was discretized into ten rainfed crop areas (RCAs) according to the extent of changes in the amplitude and phase of the climate variables. The central and southern portions (55% of the area) presented more significant changes in amplitude, mainly in Tmin and Tmax. The remaining RCAs were smaller (14.6%) but presented greater variation: the amplitude of the Tmin decreased in addition to showing a phase shift, whereas Tmax increased in addition to showing a phase shift. These results translate into a delay in the characteristic temperatures of the spring and summer seasons, which can impact the rainfed crop cycle. Additionally, rainfall showed an annual decrease of approximately 50 mm in all RCAs, which can affect the phenological development of crops during critical stages (emergence through flowering). These changes represent a significant threat to the regional economy and food security of Mexico.

Goodness of Fit Test of an Autocorrelated Time Series Cubic Smoothing Spline Model

We investigated the finite properties as well as the goodness of fit test for the cubic smoothing spline selection methods like the Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV) and Mallow CP criterion (MCP) estimators for time-series observation when there is the presence of Autocorrelation in the error term of the model. The Monte-Carlo study considered 1,000 replication with six sample sizes: 30; 60; 120; 240; 480 and 960, four degree of autocorrelations; 0.1; 0.3; 0.5; and 0.9 and three smoothing parameters; lambdaGML= 0.07271685, lambdaGCV= 0.005146929, lambdaMCP= 0.7095105. The cubic smoothing spline selection methods were also applied to a real-life dataset. The Predictive mean square error, R-square, and adjusted R-square criteria for assessing finite properties and goodness of fit among competing models discovered that the performance of the estimators is affected by changes in the sample sizes and autocorrelation levels of the simulated and real-life data set. The study concluded that the Generalized Cross-Validation estimator provides a better fit for Autocorrelated time series observation. It is recommended that the GCV works well at the four autocorrelation levels and provides the best fit for time-series observations at all sample sizes considered. This study can be applied to; non –parametric regression, non –parametric forecasting, spatial, survival and econometric observations.

Spatial Mesh Refinement using Cubic Smoothing Spline Interpolation in Simulation of 2-D Elastic Wave Equation: Forward Modeling of Full-waveform Inversion

Full-waveform inversion (FWI) is a non-destructive health monitoring technique that can be used to identify and quantify the embedded anomalies. The forward modeling of the FWI consists of a simulation of elastic wave equation to generate synthetic data. Thus the accuracy of the FWI method highly depends on the simulation method used in the forward modeling. Simulation of a 3-D seismic survey with small-scale heterogeneities is impossible with the classic finite difference approach even on modern super computers. In this work, we adopted a mesh refinement approach for simulation of the wave equation in the presence of small-scale heterogeneities. This approach uses cubic smoothing spline interpolation for spatial mesh refinement step in solving the wave equation. The simulation results for the 2-D elastic wave equation are presented and compared with the classic finite difference approach.

Simplification of gel point characterization of cellulose nano and microfiber suspensions

Nanocellulose is an emerging material that needs to be well characterized to control its performance during industrial applications. Gel point (Øg) is a convenient parameter commonly used to estimate the aspect ratio (AR) of cellulose nano/microfibers (CNFs/CMFs), providing critical information on the nanofiber network. However, its estimation requires many sedimentation experiments, tedious and time consuming. In this study, a simpler and faster technique is presented to estimate Øg, based on one or two sedimentation experiments, reducing the experiments by a factor of at least 2.5. Here, this new methodology is successfully validated by using the Øg of different CNF/CMF hydrogels calculated with the traditional methodology, showing an error lower than 7%. The error in the estimation of the AR is lower than 3% in all cases. Furthermore, the two mathematical models currently used to estimate Øg, the smoothing spline and the quadratic fit, are compared and the mathematical assumptions improved. Graphical abstract

Articulatory Strategies for Place Contrasts of Unreleased Final Stops on Preceding Vowels: Evidence from Ultrasound Imaging

This ultrasound study examines the gestural coordination involved in vowel-to-consonant sequences concerning unreleased final stops, which are more susceptible to reduction than their released counterparts. Thus, coarticulatory information on the preceding vowel is important to signal place contrasts of post-vocalic stops. The gestural coordination of vowel-consonant sequences of monosyllabic words in Cantonese represents a testing case for having preserved phonemic contrasts of six unreleased final stops in a range of vowel contexts. Preliminary results from smoothing spline ANOVA and linear mixed-effect regression show that coarticulatory patterns depend on vowel height, that is, non-high vowels are undergoing gradual coarticulation whereas high vowels are phonologising the lingual properties of the unreleased final stops on the preceding vowels.

Forecasting the Market Equity Premium: Does Nonlinearity Matter?

We propose using the nonlinear method of smoothing splines in conjunction with forecast combination to predict the market equity premium. The smooth splines are flexible enough to capture the possible nonlinear relationship between the equity premium and predictive variables while controlling for complexity, overcoming the difficulties often attached to nonlinear methods such as computational cost, overfitting and interpretation. Our empirical results show that when used with forecast combination, the smoothing spline forecasts outperform many competing methods such as the adaptive combinations, shrinkage estimators and technical indicators, in delivering statistical and economic gains consistently.