Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models.

PubMed

Zhu, Hongyan; Chu, Bingquan; Fan, Yangyang; Tao, Xiaoya; Yin, Wenxin; He, Yong

2017-08-10

We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre  = 0.9812, RPD = 5.17) and SSC (R pre  = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre  = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US.

PubMed

Biagiotti, R; Desii, C; Vanzi, E; Gacci, G

1999-02-01

To compare the performance of artificial neural networks (ANNs) with that of multiple logistic regression (MLR) models for predicting ovarian malignancy in patients with adnexal masses by using transvaginal B-mode and color Doppler flow ultrasonography (US). A total of 226 adnexal masses were examined before surgery: Fifty-one were malignant and 175 were benign. The data were divided into training and testing subsets by using a "leave n out method." The training subsets were used to compute the optimum MLR equations and to train the ANNs. The cross-validation subsets were used to estimate the performance of each of the two models in predicting ovarian malignancy. At testing, three-layer back-propagation networks, based on the same input variables selected by using MLR (i.e., women's ages, papillary projections, random echogenicity, peak systolic velocity, and resistance index), had a significantly higher sensitivity than did MLR (96% vs 84%; McNemar test, p = .04). The Brier scores for ANNs were significantly lower than those calculated for MLR (Student t test for paired samples, P = .004). ANNs might have potential for categorizing adnexal masses as either malignant or benign on the basis of multiple variables related to demographic and US features.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Reporting quality of multivariable logistic regression in selected Indian medical journals.

PubMed

Kumar, R; Indrayan, A; Chhabra, P

2012-01-01

Use of multivariable logistic regression (MLR) modeling has steeply increased in the medical literature over the past few years. Testing of model assumptions and adequate reporting of MLR allow the reader to interpret results more accurately. To review the fulfillment of assumptions and reporting quality of MLR in selected Indian medical journals using established criteria. Analysis of published literature. Medknow.com publishes 68 Indian medical journals with open access. Eight of these journals had at least five articles using MLR between the years 1994 to 2008. Articles from each of these journals were evaluated according to the previously established 10-point quality criteria for reporting and to test the MLR model assumptions. SPSS 17 software and non-parametric test (Kruskal-Wallis H, Mann Whitney U, Spearman Correlation). One hundred and nine articles were finally found using MLR for analyzing the data in the selected eight journals. The number of such articles gradually increased after year 2003, but quality score remained almost similar over time. P value, odds ratio, and 95% confidence interval for coefficients in MLR was reported in 75.2% and sufficient cases (>10) per covariate of limiting sample size were reported in the 58.7% of the articles. No article reported the test for conformity of linear gradient for continuous covariates. Total score was not significantly different across the journals. However, involvement of statistician or epidemiologist as a co-author improved the average quality score significantly (P=0.014). Reporting of MLR in many Indian journals is incomplete. Only one article managed to score 8 out of 10 among 109 articles under review. All others scored less. Appropriate guidelines in instructions to authors, and pre-publication review of articles using MLR by a qualified statistician may improve quality of reporting.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

PubMed

Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

2017-05-04

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Successive Projections Algorithm-Multivariable Linear Regression Classifier for the Detection of Contaminants on Chicken Carcasses in Hyperspectral Images

NASA Astrophysics Data System (ADS)

Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.

2017-07-01

During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Virtual Beach v2.2 User Guide

EPA Science Inventory

Virtual Beach version 2.2 (VB 2.2) is a decision support tool. It is designed to construct site-specific Multi-Linear Regression (MLR) models to predict pathogen indicator levels (or fecal indicator bacteria, FIB) at recreational beaches. MLR analysis has outperformed persisten...

Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform

PubMed Central

Lim, Pooi Khoon; Ng, Siew-Cheok; Jassim, Wissam A.; Redmond, Stephen J.; Zilany, Mohammad; Avolio, Alberto; Lim, Einly; Tan, Maw Pin; Lovell, Nigel H.

2015-01-01

We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = −0.3 ± 5.8 mmHg; SVR and −0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = −1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity. PMID:26087370

QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

PubMed

Qin, Zijian; Wang, Maolin; Yan, Aixia

2017-07-01

In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

2014-09-01

In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data

PubMed Central

Avtar, Ram; Suzuki, Rikie; Sawada, Haruo

2014-01-01

Forests play a vital role in terrestrial carbon cycling; therefore, monitoring forest biomass at local to global scales has become a challenging issue in the context of climate change. In this study, we investigated the backscattering properties of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data in cashew and rubber plantation areas of Cambodia. The PALSAR backscattering coefficient (σ0) had different responses in the two plantation types because of differences in biophysical parameters. The PALSAR σ0 showed a higher correlation with field-based measurements and lower saturation in cashew plants compared with rubber plants. Multiple linear regression (MLR) models based on field-based biomass of cashew (C-MLR) and rubber (R-MLR) plants with PALSAR σ0 were created. These MLR models were used to estimate natural forest biomass in Cambodia. The cashew plant-based MLR model (C-MLR) produced better results than the rubber plant-based MLR model (R-MLR). The C-MLR-estimated natural forest biomass was validated using forest inventory data for natural forests in Cambodia. The validation results showed a strong correlation (R2 = 0.64) between C-MLR-estimated natural forest biomass and field-based biomass, with RMSE  = 23.2 Mg/ha in deciduous forests. In high-biomass regions, such as dense evergreen forests, this model had a weaker correlation because of the high biomass and the multiple-story tree structure of evergreen forests, which caused saturation of the PALSAR signal. PMID:24465908

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

PubMed

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

PubMed Central

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification.

PubMed

Wang, LiQiang; Li, CuiFeng

2014-10-01

A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.

Comparing performance of multinomial logistic regression and discriminant analysis for monitoring access to care for acute myocardial infarction.

PubMed

Hossain, Monir; Wright, Steven; Petersen, Laura A

2002-04-01

One way to monitor patient access to emergent health care services is to use patient characteristics to predict arrival time at the hospital after onset of symptoms. This predicted arrival time can then be compared with actual arrival time to allow monitoring of access to services. Predicted arrival time could also be used to estimate potential effects of changes in health care service availability, such as closure of an emergency department or an acute care hospital. Our goal was to determine the best statistical method for prediction of arrival intervals for patients with acute myocardial infarction (AMI) symptoms. We compared the performance of multinomial logistic regression (MLR) and discriminant analysis (DA) models. Models for MLR and DA were developed using a dataset of 3,566 male veterans hospitalized with AMI in 81 VA Medical Centers in 1994-1995 throughout the United States. The dataset was randomly divided into a training set (n = 1,846) and a test set (n = 1,720). Arrival times were grouped into three intervals on the basis of treatment considerations: <6 hours, 6-12 hours, and >12 hours. One model for MLR and two models for DA were developed using the training dataset. One DA model had equal prior probabilities, and one DA model had proportional prior probabilities. Predictive performance of the models was compared using the test (n = 1,720) dataset. Using the test dataset, the proportions of patients in the three arrival time groups were 60.9% for <6 hours, 10.3% for 6-12 hours, and 28.8% for >12 hours after symptom onset. Whereas the overall predictive performance by MLR and DA with proportional priors was higher, the DA models with equal priors performed much better in the smaller groups. Correct classifications were 62.6% by MLR, 62.4% by DA using proportional prior probabilities, and 48.1% using equal prior probabilities of the groups. The misclassifications by MLR for the three groups were 9.5%, 100.0%, 74.2% for each time interval, respectively. Misclassifications by DA models were 9.8%, 100.0%, and 74.4% for the model with proportional priors and 47.6%, 79.5%, and 51.0% for the model with equal priors. The choice of MLR or DA with proportional priors, or DA with equal priors for monitoring time intervals of predicted hospital arrival time for a population should depend on the consequences of misclassification errors.

Feasibility of using a miniature NIR spectrometer to measure volumic mass during alcoholic fermentation.

PubMed

Fernández-Novales, Juan; López, María-Isabel; González-Caballero, Virginia; Ramírez, Pilar; Sánchez, María-Teresa

2011-06-01

Volumic mass-a key component of must quality control tests during alcoholic fermentation-is of great interest to the winemaking industry. Transmitance near-infrared (NIR) spectra of 124 must samples over the range of 200-1,100-nm were obtained using a miniature spectrometer. The performance of this instrument to predict volumic mass was evaluated using partial least squares (PLS) regression and multiple linear regression (MLR). The validation statistics coefficient of determination (r(2)) and the standard error of prediction (SEP) were r(2) = 0.98, n = 31 and r(2) = 0.96, n = 31, and SEP = 5.85 and 7.49 g/dm(3) for PLS and MLR equations developed to fit reference data for volumic mass and spectral data. Comparison of results from MLR and PLS demonstrates that a MLR model with six significant wavelengths (P < 0.05) fit volumic mass data to transmittance (1/T) data slightly worse than a more sophisticated PLS model using the full scanning range. The results suggest that NIR spectroscopy is a suitable technique for predicting volumic mass during alcoholic fermentation, and that a low-cost NIR instrument can be used for this purpose.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

dos Santos, T. S.; Mendes, D.; Torres, R. R.

2015-08-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

PubMed

Hoffman, Haydn; Lee, Sunghoon I; Garst, Jordan H; Lu, Derek S; Li, Charles H; Nagasawa, Daniel T; Ghalehsari, Nima; Jahanforouz, Nima; Razaghy, Mehrdad; Espinal, Marie; Ghavamrezaii, Amir; Paak, Brian H; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C

2015-09-01

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Seasonal forecasting of high wind speeds over Western Europe

NASA Astrophysics Data System (ADS)

Palutikof, J. P.; Holt, T.

2003-04-01

As financial losses associated with extreme weather events escalate, there is interest from end users in the forestry and insurance industries, for example, in the development of seasonal forecasting models with a long lead time. This study uses exceedences of the 90th, 95th, and 99th percentiles of daily maximum wind speed over the period 1958 to present to derive predictands of winter wind extremes. The source data is the 6-hourly NCEP Reanalysis gridded surface wind field. Predictor variables include principal components of Atlantic sea surface temperature and several indices of climate variability, including the NAO and SOI. Lead times of up to a year are considered, in monthly increments. Three regression techniques are evaluated; multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS). PCR and PLS proved considerably superior to MLR with much lower standard errors. PLS was chosen to formulate the predictive model since it offers more flexibility in experimental design and gave slightly better results than PCR. The results indicate that winter windiness can be predicted with considerable skill one year ahead for much of coastal Europe, but that this deteriorates rapidly in the hinterland. The experiment succeeded in highlighting PLS as a very useful method for developing more precise forecasting models, and in identifying areas of high predictability.

The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai`i

NASA Astrophysics Data System (ADS)

Frazier, Abby G.; Elison Timm, Oliver; Giambelluca, Thomas W.; Diaz, Henry F.

2017-11-01

Over the last century, significant declines in rainfall across the state of Hawai`i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November-April) variability, while ENSO is most significant in the dry season (May-October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.

The Patient Protection and Affordable Care Act's provisions regarding medical loss ratios and quality: evidence from Texas.

PubMed

Quast, Troy

2013-01-01

The Patient Protection and Affordable Care Act (PPACA) includes a provision that penalizes insurance companies if their Medical Loss Ratio (MLR) falls below a specified threshold. The MLR is roughly measured as the ratio of health care expenses to premiums paid by enrollees. I investigate whether there is a relationship between MLRs and the quality of care provided by insurance companies. I employ a ten-year sample of market-level financial data and quality variables for Texas insurers, as well as relevant control variables, in regression analyses that utilize insurer and market fixed effects. Of the 15 quality measures, only one has a statistically significant relationship with the MLR. For this measure, the relationship is negative. Although the MLR provision may provide incentives for insurance companies to lower premiums, this sample does not suggest that there is likely to be a beneficial effect on quality.

Building "e-rater"® Scoring Models Using Machine Learning Methods. Research Report. ETS RR-16-04

ERIC Educational Resources Information Center

Chen, Jing; Fife, James H.; Bejar, Isaac I.; Rupp, André A.

2016-01-01

The "e-rater"® automated scoring engine used at Educational Testing Service (ETS) scores the writing quality of essays. In the current practice, e-rater scores are generated via a multiple linear regression (MLR) model as a linear combination of various features evaluated for each essay and human scores as the outcome variable. This…

Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.

PubMed

Pineda, Silvia; Van Steen, Kristel; Malats, Núria

2017-09-01

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC.

Further Analysis of Boiling Points of Small Molecules, CH[subscript w]F[subscript x]Cl[subscript y]Br[subscript z

ERIC Educational Resources Information Center

Beauchamp, Guy

2005-01-01

A study to present specific hypothesis that satisfactorily explain the boiling point of a number of molecules, CH[subscript w]F[subscript x]Cl[subscript y]Br[subscript z] having similar structure, and then analyze the model with the help of multiple linear regression (MLR), a data analysis tool. The MLR analysis was useful in selecting the…

Prediction of the true digestible amino acid contents from the chemical composition of sorghum grain for poultry.

PubMed

Ebadi, M R; Sedghi, M; Golian, A; Ahmadi, H

2011-10-01

Accurate knowledge of true digestible amino acid (TDAA) contents of feedstuffs is necessary to accurately formulate poultry diets for profitable production. Several experimental approaches that are highly expensive and time consuming have been used to determine available amino acids. Prediction of the nutritive value of a feed ingredient from its chemical composition via regression methodology has been attempted for many years. The artificial neural network (ANN) model is a powerful method that may describe the relationship between digestible amino acid contents and chemical composition. Therefore, multiple linear regressions (MLR) and ANN models were developed for predicting the TDAA contents of sorghum grain based on chemical composition. A precision-fed assay trial using cecectomized roosters was performed to determine the TDAA contents in 48 sorghum samples from 12 sorghum varieties differing in chemical composition. The input variables for both MLR and ANN models were CP, ash, crude fiber, ether extract, and total phenols whereas the output variable was each individual TDAA for every sample. The results of this study revealed that it is possible to satisfactorily estimate the TDAA of sorghum grain through its chemical composition. The chemical composition of sorghum grain seems to highly influence the TDAA contents when considering components such as CP, crude fiber, ether extract, ash and total phenols. It is also possible to estimate the TDAA contents through multiple regression equations with reasonable accuracy depending on composition. However, a more satisfactory prediction may be achieved via ANN for all amino acids. The R(2) values for the ANN model corresponding to testing and training parameters showed a higher accuracy of prediction than equations established by the MLR method. In addition, the current data confirmed that chemical composition, often considered in total amino acid prediction, could be also a useful predictor of true digestible values of selected amino acids for poultry.

Random Forests for Global and Regional Crop Yield Predictions.

PubMed

Jeong, Jig Han; Resop, Jonathan P; Mueller, Nathaniel D; Fleisher, David H; Yun, Kyungdahm; Butler, Ethan E; Timlin, Dennis J; Shim, Kyo-Moon; Gerber, James S; Reddy, Vangimalla R; Kim, Soo-Hyung

2016-01-01

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

PubMed

Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

2014-08-15

A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.

The eMLR(C*) Method to Determine Decadal Changes in the Global Ocean Storage of Anthropogenic CO2

NASA Astrophysics Data System (ADS)

Clement, Dominic; Gruber, Nicolas

2018-04-01

The determination of the decadal change in anthropogenic CO2 in the global ocean from repeat hydrographic surveys represents a formidable challenge, which we address here by introducing a seamless new method. This method builds on the extended multiple linear regression (eMLR) approach to identify the anthropogenic CO2 signal, but in order to improve the robustness of this method, we fit C∗ rather than dissolved inorganic carbon and use a probabilistic method for the selection of the predictors. In order to account for the multiyear nature of the surveys, we adjust all C∗ observations of a particular observing period to a common reference year by assuming a transient steady state. We finally use the eMLR models together with global gridded climatological distributions of the predictors to map the estimated change in anthropogenic CO2 to the global ocean. Testing this method with synthetic data generated from a hindcast simulation with an ocean model reveals that the method is able to reconstruct the change in anthropogenic CO2 with only a small global bias (<5%). Within ocean basins, the errors can be larger, mostly driven by changes in ocean circulation. Overall, we conclude from the model that the method has an accuracy of retrieving the column integrated change in anthropogenic CO2 of about ±10% at the scale of whole ocean basins. We expect that this uncertainty needs to be doubled to about ±20% when the change in anthropogenic CO2 is reconstructed from observations.

Comparison of Regression Methods to Compute Atmospheric Pressure and Earth Tidal Coefficients in Water Level Associated with Wenchuan Earthquake of 12 May 2008

NASA Astrophysics Data System (ADS)

He, Anhua; Singh, Ramesh P.; Sun, Zhaohua; Ye, Qing; Zhao, Gang

2016-07-01

The earth tide, atmospheric pressure, precipitation and earthquake fluctuations, especially earthquake greatly impacts water well levels, thus anomalous co-seismic changes in ground water levels have been observed. In this paper, we have used four different models, simple linear regression (SLR), multiple linear regression (MLR), principal component analysis (PCA) and partial least squares (PLS) to compute the atmospheric pressure and earth tidal effects on water level. Furthermore, we have used the Akaike information criterion (AIC) to study the performance of various models. Based on the lowest AIC and sum of squares for error values, the best estimate of the effects of atmospheric pressure and earth tide on water level is found using the MLR model. However, MLR model does not provide multicollinearity between inputs, as a result the atmospheric pressure and earth tidal response coefficients fail to reflect the mechanisms associated with the groundwater level fluctuations. On the premise of solving serious multicollinearity of inputs, PLS model shows the minimum AIC value. The atmospheric pressure and earth tidal response coefficients show close response with the observation using PLS model. The atmospheric pressure and the earth tidal response coefficients are found to be sensitive to the stress-strain state using the observed data for the period 1 April-8 June 2008 of Chuan 03# well. The transient enhancement of porosity of rock mass around Chuan 03# well associated with the Wenchuan earthquake (Mw = 7.9 of 12 May 2008) that has taken its original pre-seismic level after 13 days indicates that the co-seismic sharp rise of water well could be induced by static stress change, rather than development of new fractures.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

PubMed

Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

2013-01-01

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals.

PubMed

Zhang, Ying-Ying; Zhou, Xiao-Bin; Wang, Qiu-Zhen; Zhu, Xiao-Yan

2017-05-01

Multivariable logistic regression (MLR) has been increasingly used in Chinese clinical medical research during the past few years. However, few evaluations of the quality of the reporting strategies in these studies are available.To evaluate the reporting quality and model accuracy of MLR used in published work, and related advice for authors, readers, reviewers, and editors.A total of 316 articles published in 5 leading Chinese clinical medical journals with high impact factor from January 2010 to July 2015 were selected for evaluation. Articles were evaluated according 12 established criteria for proper use and reporting of MLR models.Among the articles, the highest quality score was 9, the lowest 1, and the median 5 (4-5). A total of 85.1% of the articles scored below 6. No significant differences were found among these journals with respect to quality score (χ = 6.706, P = .15). More than 50% of the articles met the following 5 criteria: complete identification of the statistical software application that was used (97.2%), calculation of the odds ratio and its confidence interval (86.4%), description of sufficient events (>10) per variable, selection of variables, and fitting procedure (78.2%, 69.3%, and 58.5%, respectively). Less than 35% of the articles reported the coding of variables (18.7%). The remaining 5 criteria were not satisfied by a sufficient number of articles: goodness-of-fit (10.1%), interactions (3.8%), checking for outliers (3.2%), collinearity (1.9%), and participation of statisticians and epidemiologists (0.3%). The criterion of conformity with linear gradients was applicable to 186 articles; however, only 7 (3.8%) mentioned or tested it.The reporting quality and model accuracy of MLR in selected articles were not satisfactory. In fact, severe deficiencies were noted. Only 1 article scored 9. We recommend authors, readers, reviewers, and editors to consider MLR models more carefully and cooperate more closely with statisticians and epidemiologists. Journals should develop statistical reporting guidelines concerning MLR.

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

NASA Astrophysics Data System (ADS)

Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

2018-02-01

The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

Integrating auxiliary data and geophysical techniques for the estimation of soil clay content using CHAID algorithm

NASA Astrophysics Data System (ADS)

Abbaszadeh Afshar, Farideh; Ayoubi, Shamsollah; Besalatpour, Ali Asghar; Khademi, Hossein; Castrignano, Annamaria

2016-03-01

This study was conducted to estimate soil clay content in two depths using geophysical techniques (Ground Penetration Radar-GPR and Electromagnetic Induction-EMI) and ancillary variables (remote sensing and topographic data) in an arid region of the southeastern Iran. GPR measurements were performed throughout ten transects of 100 m length with the line spacing of 10 m, and the EMI measurements were done every 10 m on the same transect in six sites. Ten soil cores were sampled randomly in each site and soil samples were taken from the depth of 0-20 and 20-40 cm, and then the clay fraction of each of sixty soil samples was measured in the laboratory. Clay content was predicted using three different sets of properties including geophysical data, ancillary data, and a combination of both as inputs to multiple linear regressions (MLR) and decision tree-based algorithm of Chi-Squared Automatic Interaction Detection (CHAID) models. The results of the CHAID and MLR models with all combined data showed that geophysical data were the most important variables for the prediction of clay content in two depths in the study area. The proposed MLR model, using the combined data, could explain only 0.44 and 0.31% of the total variability of clay content in 0-20 and 20-40 cm depths, respectively. Also, the coefficient of determination (R2) values for the clay content prediction, using the constructed CHAID model with the combined data, was 0.82 and 0.76 in 0-20 and 20-40 cm depths, respectively. CHAID models, therefore, showed a greater potential in predicting soil clay content from geophysical and ancillary data, while traditional regression methods (i.e. the MLR models) did not perform as well. Overall, the results may encourage researchers in using georeferenced GPR and EMI data as ancillary variables and CHAID algorithm to improve the estimation of soil clay content.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

NASA Astrophysics Data System (ADS)

Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

2011-08-01

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

Multivariate modelling of density, strength, and stiffness from near infared for mature, juvenile, and pith wood of longleaf pine (Pinus Palustris)

Treesearch

Brian K. Via; Todd F. Shupe; Leslie H. Groom; Michael Stine; Chi-Leung So

2003-01-01

In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more...

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

PubMed

Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

2017-09-05

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Spectroscopic studies on Solvatochromism of mixed-chelate copper(II) complexes using MLR technique

NASA Astrophysics Data System (ADS)

Golchoubian, Hamid; Moayyedi, Golasa; Fazilati, Hakimeh

2012-01-01

Mixed-chelate copper(II) complexes with a general formula [Cu(acac)(diamine)]X where acac = acetylacetonate ion, diamine = N,N-dimethyl,N'-benzyl-1,2-diaminoethane and X = BPh 4-, PF 6-, ClO 4- and BF 4- have been prepared. The complexes were characterized on the basis of elemental analysis, molar conductance, UV-vis and IR spectroscopies. The complexes are solvatochromic and their solvatochromism were investigated by visible spectroscopy. All complexes demonstrated the positive solvatochromism and among the complexes [Cu(acac)(diamine)]BPh 4·H 2O showed the highest Δ νmax value. To explore the mechanism of interaction between solvent molecules and the complexes, different solvent parameters such as DN, AN, α and β using multiple linear regression (MLR) method were employed. The statistical results suggested that the DN parameter of the solvent plays a dominate contribution to the shift of the d-d absorption band of the complexes.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Role of neutrophil to lymphocyte and monocyte to lymphocyte ratios in the diagnosis of bacterial infection in patients with fever.

PubMed

Naess, Are; Nilssen, Siri Saervold; Mo, Reidun; Eide, Geir Egil; Sjursen, Haakon

2017-06-01

To study the role of the neutrophil:lymphocyte ratio (NLR) and monocyte:lymphocyte ratio (MLR) in discriminating between different patient groups hospitalized for fever due to infection and those without infection. For 299 patients admitted to hospital for fever with unknown cause, a number of characteristics including NLR and MLR were recorded. These characteristics were used in a multiple multinomial regression analysis to estimate the probability of a final diagnostic group of bacterial, viral, clinically confirmed, or no infection. Both NLR and MLR significantly predicted final diagnostic group. Being highly correlated, however, both variables could not be retained in the same model. Both variables also interacted significantly with duration of fever. Generally, higher values of NLR and MLR indicated larger probabilities for bacterial infection and low probabilities for viral infection. Patients with septicemia had significantly higher NLR compared to patients with other bacterial infections with fever for less than one week. White blood cell counts, neutrophil counts, and C-reactive proteins did not differ significantly between septicemia and the other bacterial infection groups. NLR is a more useful diagnostic tool to identify patients with septicemia than other more commonly used diagnostic blood tests. NLR and MLR may be useful in the diagnosis of bacterial infection among patients hospitalized for fever.

Interpolations of groundwater table elevation in dissected uplands.

PubMed

Chung, Jae-won; Rogers, J David

2012-01-01

The variable elevation of the groundwater table in the St. Louis area was estimated using multiple linear regression (MLR), ordinary kriging, and cokriging as part of a regional program seeking to assess liquefaction potential. Surface water features were used to determine the minimum water table for MLR and supplement the principal variables for ordinary kriging and cokriging. By evaluating the known depth to the water and the minimum water table elevation, the MLR analysis approximates the groundwater elevation for a contiguous hydrologic system. Ordinary kriging and cokriging estimate values in unsampled areas by calculating the spatial relationships between the unsampled and sampled locations. In this study, ordinary kriging did not incorporate topographic variations as an independent variable, while cokriging included topography as a supporting covariable. Cross validation suggests that cokriging provides a more reliable estimate at known data points with less uncertainty than the other methods. Profiles extending through the dissected uplands terrain suggest that: (1) the groundwater table generated by MLR mimics the ground surface and elicits a exaggerated interpolation of groundwater elevation; (2) the groundwater table estimated by ordinary kriging tends to ignore local topography and exhibits oversmoothing of the actual undulations in the water table; and (3) cokriging appears to give the realistic water surface, which rises and falls in proportion to the overlying topography. The authors concluded that cokriging provided the most realistic estimate of the groundwater surface, which is the key variable in assessing soil liquefaction potential in unconsolidated sediments. © 2011, The Author(s). Ground Water © 2011, National Ground Water Association.

Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms

NASA Astrophysics Data System (ADS)

Helama, S.; Makarenko, N. G.; Karimova, L. M.; Kruglun, O. A.; Timonen, M.; Holopainen, J.; Meriläinen, J.; Eronen, M.

2009-03-01

Tree-rings tell of past climates. To do so, tree-ring chronologies comprising numerous climate-sensitive living-tree and subfossil time-series need to be "transferred" into palaeoclimate estimates using transfer functions. The purpose of this study is to compare different types of transfer functions, especially linear and nonlinear algorithms. Accordingly, multiple linear regression (MLR), linear scaling (LSC) and artificial neural networks (ANN, nonlinear algorithm) were compared. Transfer functions were built using a regional tree-ring chronology and instrumental temperature observations from Lapland (northern Finland and Sweden). In addition, conventional MLR was compared with a hybrid model whereby climate was reconstructed separately for short- and long-period timescales prior to combining the bands of timescales into a single hybrid model. The fidelity of the different reconstructions was validated against instrumental climate data. The reconstructions by MLR and ANN showed reliable reconstruction capabilities over the instrumental period (AD 1802-1998). LCS failed to reach reasonable verification statistics and did not qualify as a reliable reconstruction: this was due mainly to exaggeration of the low-frequency climatic variance. Over this instrumental period, the reconstructed low-frequency amplitudes of climate variability were rather similar by MLR and ANN. Notably greater differences between the models were found over the actual reconstruction period (AD 802-1801). A marked temperature decline, as reconstructed by MLR, from the Medieval Warm Period (AD 931-1180) to the Little Ice Age (AD 1601-1850), was evident in all the models. This decline was approx. 0.5°C as reconstructed by MLR. Different ANN based palaeotemperatures showed simultaneous cooling of 0.2 to 0.5°C, depending on algorithm. The hybrid MLR did not seem to provide further benefit above conventional MLR in our sample. The robustness of the conventional MLR over the calibration, verification and reconstruction periods qualified it as a reasonable transfer function for our forest-limit (i.e., timberline) dataset. ANN appears a potential tool for other environments and/or proxies having more complex and noisier climatic relationships.

Seasonal prediction of winter haze days in the north central North China Plain

NASA Astrophysics Data System (ADS)

Yin, Zhicong; Wang, Huijun

2016-11-01

Recently, the winter (December-February) haze pollution over the north central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM). By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53 % (54 %), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (-0.07) and -3.33 (-1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

Mapping water table depth using geophysical and environmental variables.

PubMed

Buchanan, S; Triantafilis, J

2009-01-01

Despite its importance, accurate representation of the spatial distribution of water table depth remains one of the greatest deficiencies in many hydrological investigations. Historically, both inverse distance weighting (IDW) and ordinary kriging (OK) have been used to interpolate depths. These methods, however, have major limitations: namely they require large numbers of measurements to represent the spatial variability of water table depth and they do not represent the variation between measurement points. We address this issue by assessing the benefits of using stepwise multiple linear regression (MLR) with three different ancillary data sets to predict the water table depth at 100-m intervals. The ancillary data sets used are Electromagnetic (EM34 and EM38), gamma radiometric: potassium (K), uranium (eU), thorium (eTh), total count (TC), and morphometric data. Results show that MLR offers significant precision and accuracy benefits over OK and IDW. Inclusion of the morphometric data set yielded the greatest (16%) improvement in prediction accuracy compared with IDW, followed by the electromagnetic data set (5%). Use of the gamma radiometric data set showed no improvement. The greatest improvement, however, resulted when all data sets were combined (37% increase in prediction accuracy over IDW). Significantly, however, the use of MLR also allows for prediction in variations in water table depth between measurement points, which is crucial for land management.

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

PubMed

Smyczynska, Joanna; Hilczer, Maciej; Smyczynska, Urszula; Stawerska, Renata; Tadeusiewicz, Ryszard; Lewinski, Andrzej

2015-01-01

The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness. Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH. For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability. ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.

Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

PubMed

Ahmadi, Hamed; Rodehutscord, Markus

2017-01-01

In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [ R 2  = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM ( R 2  = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR ( R 2  = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel ® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida.

PubMed

Haji Gholizadeh, Mohammad; Melesse, Assefa M; Reddi, Lakshmi

2016-10-01

In this study, principal component analysis (PCA), factor analysis (FA), and the absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, 15years (2000-2014) dataset of 12 water quality variables covering 16 monitoring stations, and approximately 35,000 observations was used. The PCA/FA method identified five and four potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules and causes were explained. The APCS-MLR apportioned their contributions to each water quality variable. Results showed that the point source pollution discharges from anthropogenic factors due to the discharge of agriculture waste and domestic and industrial wastewater were the major sources of river water contamination. Also, the studied variables were categorized into three groups of nutrients (total kjeldahl nitrogen, total phosphorus, total phosphate, and ammonia-N), water murkiness conducive parameters (total suspended solids, turbidity, and chlorophyll-a), and salt ions (magnesium, chloride, and sodium), and average contributions of different potential pollution sources to these categories were considered separately. The data matrix was also subjected to PMF receptor model using the EPA PMF-5.0 program and the two-way model described was performed for the PMF analyses. Comparison of the obtained results of PMF and APCS-MLR models showed that there were some significant differences in estimated contribution for each potential pollution source, especially in the wet season. Eventually, it was concluded that the APCS-MLR receptor modeling approach appears to be more physically plausible for the current study. It is believed that the results of apportionment could be very useful to the local authorities for the control and management of pollution and better protection of important riverine water quality. Copyright © 2016 Elsevier B.V. All rights reserved.

Missing data imputation of solar radiation data under different atmospheric conditions.

PubMed

Turrado, Concepción Crespo; López, María Del Carmen Meizoso; Lasheras, Fernando Sánchez; Gómez, Benigno Antonio Rodríguez; Rollé, José Luis Calvo; Juez, Francisco Javier de Cos

2014-10-29

Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.

Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions

PubMed Central

Turrado, Concepción Crespo; López, María del Carmen Meizoso; Lasheras, Fernando Sánchez; Gómez, Benigno Antonio Rodríguez; Rollé, José Luis Calvo; de Cos Juez, Francisco Javier

2014-01-01

Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW. PMID:25356644

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.

PubMed

D'Archivio, Angelo Antonio; Incani, Angela; Ruggieri, Fabrizio

2011-01-01

In this paper, we use a quantitative structure-retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GC×GC). We analyse the GC×GC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column ((1) t (R) and (2) t (R), respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for (1) t (R) and (2) t (R) from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling (1) t (R) and (2) t (R), respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model (1) t (R) and (2) t (R) without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for (1) t (R) and (2) t (R) in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo; Craig, Tim

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and appliedmore » three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.« less

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].

PubMed

Wang, Jia-Hua; Zhang, Xiao-Wei; Wang, Jun; Han, Dong-Hai

2014-10-01

To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

PubMed Central

Zainudin, Suhaila; Arif, Shereena M.

2017-01-01

Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767

Developing a stroke severity index based on administrative data was feasible using data mining techniques.

PubMed

Sung, Sheng-Feng; Hsieh, Cheng-Yang; Kao Yang, Yea-Huei; Lin, Huey-Juan; Chen, Chih-Hung; Chen, Yu-Wei; Hu, Ya-Han

2015-11-01

Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data. We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database. We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models. The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data. Copyright © 2015 Elsevier Inc. All rights reserved.

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

PubMed

León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa

2018-01-01

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine

NASA Astrophysics Data System (ADS)

Ebrahimi, Hadi; Rajaee, Taher

2017-01-01

Simulation of groundwater level (GWL) fluctuations is an important task in management of groundwater resources. In this study, the effect of wavelet analysis on the training of the artificial neural network (ANN), multi linear regression (MLR) and support vector regression (SVR) approaches was investigated, and the ANN, MLR and SVR along with the wavelet-ANN (WNN), wavelet-MLR (WLR) and wavelet-SVR (WSVR) models were compared in simulating one-month-ahead of GWL. The only variable used to develop the models was the monthly GWL data recorded over a period of 11 years from two wells in the Qom plain, Iran. The results showed that decomposing GWL time series into several sub-time series, extremely improved the training of the models. For both wells 1 and 2, the Meyer and Db5 wavelets produced better results compared to the other wavelets; which indicated wavelet types had similar behavior in similar case studies. The optimal number of delays was 6 months, which seems to be due to natural phenomena. The best WNN model, using Meyer mother wavelet with two decomposition levels, simulated one-month-ahead with RMSE values being equal to 0.069 m and 0.154 m for wells 1 and 2, respectively. The RMSE values for the WLR model were 0.058 m and 0.111 m, and for WSVR model were 0.136 m and 0.060 m for wells 1 and 2, respectively.

Source apportionment of PAH in Hamilton Harbour suspended sediments: comparison of two factor analysis methods.

PubMed

Sofowote, Uwayemi M; McCarry, Brian E; Marvin, Christopher H

2008-08-15

A total of 26 suspended sediment samples collected over a 5-year period in Hamilton Harbour, Ontario, Canada and surrounding creeks were analyzed for a suite of polycyclic aromatic hydrocarbons and sulfur heterocycles. Hamilton Harbour sediments contain relatively high levels of polycyclic aromatic compounds and heavy metals due to emissions from industrial and mobile sources. Two receptor modeling methods using factor analyses were compared to determine the profiles and relative contributions of pollution sources to the harbor; these methods are principal component analyses (PCA) with multiple linear regression analysis (MLR) and positive matrix factorization (PMF). Both methods identified four factors and gave excellent correlation coefficients between predicted and measured levels of 25 aromatic compounds; both methods predicted similar contributions from coal tar/coal combustion sources to the harbor (19 and 26%, respectively). One PCA factor was identified as contributions from vehicular emissions (61%); PMF was able to differentiate vehicular emissions into two factors, one attributed to gasoline emissions sources (28%) and the other to diesel emissions sources (24%). Overall, PMF afforded better source identification than PCA with MLR. This work constitutes one of the few examples of the application of PMF to the source apportionment of sediments; the addition of sulfur heterocycles to the analyte list greatly aided in the source identification process.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Surfactants in the sea-surface microlayer and atmospheric aerosol around the southern region of Peninsular Malaysia.

PubMed

Jaafar, Shoffian Amin; Latif, Mohd Talib; Chian, Chong Woan; Han, Wong Sook; Wahid, Nurul Bahiyah Abd; Razak, Intan Suraya; Khan, Md Firoz; Tahir, Norhayati Mohd

2014-07-15

This study was conducted to determine the composition of surfactants in the sea-surface microlayer (SML) and atmospheric aerosol around the southern region of the Peninsular Malaysia. Surfactants in samples taken from the SML and atmospheric aerosol were determined using a colorimetric method, as either methylene blue active substances (MBAS) or disulphine blue active substances (DBAS). Principal component analysis with multiple linear regressions (PCA-MLR), using the anion and major element composition of the aerosol samples, was used to determine possible sources of surfactants in atmospheric aerosol. The results showed that the concentrations of surfactants in the SML and atmospheric aerosol were dominated by anionic surfactants and that surfactants in aerosol were not directly correlated (p>0.05) with surfactants in the SML. Further PCA-MLR from anion and major element concentrations showed that combustion of fossil fuel and sea spray were the major contributors to surfactants in aerosol in the study area. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling rainfall-runoff process using soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Tombul, Mustafa

2013-02-01

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods.

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang

2006-01-01

Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

Empirical tools for simulating salinity in the estuaries in Everglades National Park, Florida

NASA Astrophysics Data System (ADS)

Marshall, F. E.; Smith, D. T.; Nickerson, D. M.

2011-12-01

Salinity in a shallow estuary is affected by upland freshwater inputs (surface runoff, stream/canal flows, groundwater), atmospheric processes (precipitation, evaporation), marine connectivity, and wind patterns. In Everglades National Park (ENP) in South Florida, the unique Everglades ecosystem exists as an interconnected system of fresh, brackish, and salt water marshes, mangroves, and open water. For this effort a coastal aquifer conceptual model of the Everglades hydrologic system was used with traditional correlation and regression hydrologic techniques to create a series of multiple linear regression (MLR) salinity models from observed hydrologic, marine, and weather data. The 37 ENP MLR salinity models cover most of the estuarine areas of ENP and produce daily salinity simulations that are capable of estimating 65-80% of the daily variability in salinity depending upon the model. The Root Mean Squared Error is typically about 2-4 salinity units, and there is little bias in the predictions. However, the absolute error of a model prediction in the nearshore embayments and the mangrove zone of Florida Bay may be relatively large for a particular daily simulation during the seasonal transitions. Comparisons show that the models group regionally by similar independent variables and salinity regimes. The MLR salinity models have approximately the same expected range of simulation accuracy and error as higher spatial resolution salinity models.

Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties.

PubMed

Sano, Yuko; Kandori, Akihiko; Shima, Keisuke; Yamaguchi, Yuki; Tsuji, Toshio; Noda, Masafumi; Higashikawa, Fumiko; Yokoe, Masaru; Sakoda, Saburo

2016-06-01

We propose a novel index of Parkinson's disease (PD) finger-tapping severity, called "PDFTsi," for quantifying the severity of symptoms related to the finger tapping of PD patients with high accuracy. To validate the efficacy of PDFTsi, the finger-tapping movements of normal controls and PD patients were measured by using magnetic sensors, and 21 characteristics were extracted from the finger-tapping waveforms. To distinguish motor deterioration due to PD from that due to aging, the aging effect on finger tapping was removed from these characteristics. Principal component analysis (PCA) was applied to the age-normalized characteristics, and principal components that represented the motion properties of finger tapping were calculated. Multiple linear regression (MLR) with stepwise variable selection was applied to the principal components, and PDFTsi was calculated. The calculated PDFTsi indicates that PDFTsi has a high estimation ability, namely a mean square error of 0.45. The estimation ability of PDFTsi is higher than that of the alternative method, MLR with stepwise regression selection without PCA, namely a mean square error of 1.30. This result suggests that PDFTsi can quantify PD finger-tapping severity accurately. Furthermore, the result of interpreting a model for calculating PDFTsi indicated that motion wideness and rhythm disorder are important for estimating PD finger-tapping severity.

Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Treesearch

Becky K. Kerns; Janet L. Ohmann

2004-01-01

We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were...

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Contributions of wood smoke and vehicle emissions to ambient concentrations of volatile organic compounds and particulate matter during the Yakima wintertime nitrate study

NASA Astrophysics Data System (ADS)

VanderSchelden, Graham; de Foy, Benjamin; Herring, Courtney; Kaspari, Susan; VanReken, Tim; Jobson, Bertram

2017-02-01

A multiple linear regression (MLR) chemical mass balance model was applied to data collected during an air quality field experiment in Yakima, WA, during January 2013 to determine the relative contribution of residential wood combustion (RWC) and vehicle emissions to ambient pollutant levels. Acetonitrile was used as a chemical tracer for wood burning and nitrogen oxides (NOx) as a chemical tracer for mobile sources. RWC was found to be a substantial source of gas phase air toxics in wintertime. The MLR model found RWC primarily responsible for emissions of formaldehyde (73%), acetaldehyde (69%), and black carbon (55%) and mobile sources primarily responsible for emissions of carbon monoxide (CO; 83%), toluene (81%), C2-alkylbenzenes (81%), and benzene (64%). When compared with the Environmental Protection Agency's 2011 winter emission inventory, the MLR results suggest that the contribution of RWC to CO emissions was underestimated in the inventory by a factor of 2. Emission ratios to NOx from the MLR model agreed to within 25% with wintertime emission ratios predicted from the Motor Vehicle Emissions Simulator (MOVES) 2010b emission model for Yakima County for all pollutants modeled except for CO, C2-alkylbenzenes, and black carbon. The MLR model results suggest that MOVES was overpredicting mobile source emissions of CO relative to NOx by a factor of 1.33 and black carbon relative to NOx by about a factor of 3.

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

The clinical significance of neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio in Guillain-Barré syndrome.

PubMed

Huang, Yuanyuan; Ying, Zhaojian; Quan, Weiwei; Xiang, Weiwei; Xie, Dewei; Weng, Yiyun; Li, Xiang; Li, Jia; Zhang, Xu

2018-08-01

Purpose/Aim of the study: Guillain Barré syndrome (GBS) is a severe peripheral nervous disease that leads to muscle weakness and areflexia. We now commonly accept a synthesis that inflammation and immunity play key role in GBS pathogenesis. Many studies pointed out that neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) are novel promising markers of inflammation or immunity. Our study aimed to evaluate whether the NLR and the MLR were associated to GBS or not. We measured blood cell count in 334 individuals including 117 GBS and 217 healthy controls. Our findings demonstrated that the GBS patients had higher levels of NLR and MLR than the healthy controls. The severe group also had higher levels of NLR and MLR compared to the mild group. We took the method of receiver-operating characteristic curve to find out the cut-off value of NLR for GBS occurrence and severity; it was 2.295 and 3.05, respectively. The cut-off values of MLR for GBS incidence and severity were the same, it was 0.235. In the setting of GBS, the NLR and MLR were significantly increased and they may be pathophysiologically and clinically relevant in GBS. The NLR and MLR would be new biomarkers of medical application.

[Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].

PubMed

Liu, Fei; Feng, Lei; Lou, Bing-gan; Sun, Guang-ming; Wang, Lian-ping; He, Yong

2010-07-01

The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.

Structure-based predictions of 13C-NMR chemical shifts for a series of 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indoles derivatives using GA-based MLR method

NASA Astrophysics Data System (ADS)

Ghavami, Raouf; Sadeghi, Faridoon; Rasouli, Zolikha; Djannati, Farhad

2012-12-01

Experimental values for the 13C NMR chemical shifts (ppm, TMS = 0) at 300 K ranging from 96.28 ppm (C4' of indole derivative 17) to 159.93 ppm (C4' of indole derivative 23) relative to deuteride chloroform (CDCl3, 77.0 ppm) or dimethylsulfoxide (DMSO, 39.50 ppm) as internal reference in CDCl3 or DMSO-d6 solutions have been collected from literature for thirty 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indole derivatives containing different substituted groups. An effective quantitative structure-property relationship (QSPR) models were built using hybrid method combining genetic algorithm (GA) based on stepwise selection multiple linear regression (SWS-MLR) as feature-selection tools and correlation models between each carbon atom of indole derivative and calculated descriptors. Each compound was depicted by molecular structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum chemical features. The accuracy of all developed models were confirmed using different types of internal and external procedures and various statistical tests. Furthermore, the domain of applicability for each model which indicates the area of reliable predictions was defined.

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Shengzhi; Ming, Bo; Huang, Qiang

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecastingmore » models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.« less

Gain modulation of the middle latency cutaneous reflex in patients with chronic joint instability after ankle sprain.

PubMed

Futatsubashi, Genki; Sasada, Shusaku; Tazoe, Toshiki; Komiyama, Tomoyoshi

2013-07-01

To investigate the neural alteration of reflex pathways arising from cutaneous afferents in patients with chronic ankle instability. Cutaneous reflexes were elicited by applying non-noxious electrical stimulation to the sural nerve of subjects with chronic ankle instability (n=17) and control subjects (n=17) while sitting. Electromyographic (EMG) signals were recorded from each ankle and thigh muscle. The middle latency response (MLR; latency: 70-120 ms) component was analyzed. In the peroneus longus (PL) and vastus lateralis (VL) muscles, linear regression analyses between the magnitude of the inhibitory MLR and background EMG activity showed that, compared to the uninjured side and the control subjects, the gain of the suppressive MLR was increased in the injured side. This was also confirmed by the pooled data for both groups. The degree of MLR alteration was significantly correlated to that of chronic ankle instability in the PL. The excitability of middle latency cutaneous reflexes in the PL and VL is modulated in subjects with chronic ankle instability. Cutaneous reflexes may be potential tools to investigate the pathological state of the neural system that controls the lower limbs in subjects with chronic ankle instability. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Factors affecting Escherichia coli concentrations at Lake Erie public bathing beaches

USGS Publications Warehouse

Francy, Donna S.; Darner, Robert A.

1998-01-01

The environmental and water-quality factors that affect concentrations of Escherichia coli (E. coli) in water and sediment were investigated at three public bathing beachesEdgewater Park, Villa Angela, and Sims Parkin the Cleveland, Ohio metropolitan area. This study was done to aid in the determination of safe recreational use and to help water- resource managers assess more quickly and accurately the degradation of recreational water quality. Water and lake-bottom sediments were collected and ancillary environmental data were compiled for 41 days from May through September 1997. Water samples were analyzed for E. coli concentrations, suspended sediment concentrations, and turbidity. Lake- bottom sediment samples from the beach area were analyzed for E. coli concentrations and percent dry weight. Concentrations of E. coli were higher and more variable at Sims Park than at Villa Angela or Edgewater Park; concentrations were lowest at Edgewater Park. Time-series plots showed that short-term storage (less than one week) of E. coli in lake-bottom sediments may have occurred, although no evidence for long-term storage was found during the sampling period. E. coli concentrations in water were found to increase with increasing wave height, but the resuspension of E. coli from lake-bottom sediments by wave action could not be adequately assessed; higherwave heights were often associated with the discharge of sewage containing E. coli during or after a rainfall and wastewater-treatment plant overflow. Multiple linear regression (MLR) was used to develop models to predict recreational water quality at the in water. The related variables included turbidity, antecedent rainfall, antecedent weighted rainfall, volumes of wastewater-treatment plant overflows and metered outfalls (composed of storm-water runoff and combined-sewer overflows), a resuspension index, and wave heights. For the beaches in this study, wind speed, wind direction, water temperature, and the prswimmers were not included in the model because they were shown to be statistically unrelated to E. coli concentrations. From the several models developed, one model was chosen that accounted for 58 percent of the variability in E. coli concentrations. The chosen MLR model contained weighted categorical rainfall, beach-specific turbidity, wave height, and terms to correct for the different magnitudes of E. coli concentrations among the three beaches. For 1997, the MLR model predicted the recreational water quality as well as, and in some cases better than, antecedent E. coli concentrations (the current method). The MLR model improved the sensitivity of the prediction and the percentage of correct predictions over the current method; however, the MLR model predictions still erred to a similar degree as the current method with regard to false negatives. A false negative would allow swimming when, in fact, the bathing water standard was exceeded. More work needs to be done to validate the MLR model with data collected during other recreational seasons, especially during a season with a greater frequency and intensity of summer rains. Studies could focus on adding to the MLR model other environmental and water-quality variables that improve the predictive ability of the model. These variables might include concentrations of E. coli in deeper sediments outside the bathing area, the direction of lake currents, site-specific-rainfall amounts, time-of-day information on overflows and metered outfalls, concentrations of E. coli in treated wastewater-treatment plant effluents, and occurrences of sewage-line breaks. Rapid biological or chemical methods for determination of recreational water quality could also be used as variables in model refinements. Possible methods include the use of experimental rapid assay methods for determination of E. coli concentrations or other fecal indicators and the use of chemical tracers for fecal contamination, such as coprostanol (a degradation

A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

PubMed

Bersabé, Rosa; Rivas, Teresa

2010-05-01

The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study.

PubMed

Antanasijević, Davor; Pocajt, Viktor; Povrenović, Dragan; Perić-Grujić, Aleksandra; Ristić, Mirjana

2013-12-01

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ± 10 %. In case of the MLR, only 55 % of predictions were within the error of less than ± 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.

DFT study on oxidation of HS(CH2) m SH ( m = 1-8) in oxidative desulfurization

NASA Astrophysics Data System (ADS)

Song, Y. Z.; Song, J. J.; Zhao, T. T.; Chen, C. Y.; He, M.; Du, J.

2016-06-01

Density functional theory was employed for calculation of HS(CH2) m SH ( m = 1-8) and its derivatives at B3LYP method at 6-31++g ( d, p) level. Using eigenvalues of LUMO and HOMO for HS(CH2) m SH, the standard electrode potentials were estimated by a stepwise multiple regression techniques (MLR), and obtained as E° = 1.500 + 7.167 × 10-3 HOMO-0.229 LUMO with high correlation coefficients of 0.973 and F values of 43.973.

Field Balancing of Magnetically Levitated Rotors without Trial Weights

PubMed Central

Fang, Jiancheng; Wang, Yingguang; Han, Bangcheng; Zheng, Shiqiang

2013-01-01

Unbalance in magnetically levitated rotor (MLR) can cause undesirable synchronous vibrations and lead to the saturation of the magnetic actuator. Dynamic balancing is an important way to solve these problems. However, the traditional balancing methods, using rotor displacement to estimate a rotor's unbalance, requiring several trial-runs, are neither precise nor efficient. This paper presents a new balancing method for an MLR without trial weights. In this method, the rotor is forced to rotate around its geometric axis. The coil currents of magnetic bearing, rather than rotor displacement, are employed to calculate the correction masses. This method provides two benefits when the MLR's rotation axis coincides with the geometric axis: one is that unbalanced centrifugal force/torque equals the synchronous magnetic force/torque, and the other is that the magnetic force is proportional to the control current. These make calculation of the correction masses by measuring coil current with only a single start-up precise. An unbalance compensation control (UCC) method, using a general band-pass filter (GPF) to make the MLR spin around its geometric axis is also discussed. Experimental results show that the novel balancing method can remove more than 92.7% of the rotor unbalance and a balancing accuracy of 0.024 g mm kg−1 is achieved.

Factors Influencing Intraocular Pressure Changes after Laser In Situ Keratomileusis with Flaps Created by Femtosecond Laser or Mechanical Microkeratome.

PubMed

Lin, Meng-Yin; Chang, David C K; Shen, Yun-Dun; Lin, Yen-Kuang; Lin, Chang-Ping; Wang, I-Jong

2016-01-01

The aim of this study is to describe factors that influence the measured intraocular pressure (IOP) change and to develop a predictive model after myopic laser in situ keratomileusis (LASIK) with a femtosecond (FS) laser or a microkeratome (MK). We retrospectively reviewed preoperative, intraoperative, and 12-month postoperative medical records in 2485 eyes of 1309 patients who underwent LASIK with an FS laser or an MK for myopia and myopic astigmatism. Data were extracted, such as preoperative age, sex, IOP, manifest spherical equivalent (MSE), central corneal keratometry (CCK), central corneal thickness (CCT), and intended flap thickness and postoperative IOP (postIOP) at 1, 6 and 12 months. Linear mixed model (LMM) and multivariate linear regression (MLR) method were used for data analysis. In both models, the preoperative CCT and ablation depth had significant effects on predicting IOP changes in the FS and MK groups. The intended flap thickness was a significant predictor only in the FS laser group (P < .0001 in both models). In the FS group, LMM and MLR could respectively explain 47.00% and 18.91% of the variation of postoperative IOP underestimation (R2 = 0.47 and R(2) = 0.1891). In the MK group, LMM and MLR could explain 37.79% and 19.13% of the variation of IOP underestimation (R(2) = 0.3779 and 0.1913 respectively). The best-fit model for prediction of IOP changes was the LMM in LASIK with an FS laser.

Factors Influencing Intraocular Pressure Changes after Laser In Situ Keratomileusis with Flaps Created by Femtosecond Laser or Mechanical Microkeratome

PubMed Central

Lin, Meng-Yin; Chang, David C. K.; Shen, Yun-Dun; Lin, Yen-Kuang; Lin, Chang-Ping; Wang, I-Jong

2016-01-01

The aim of this study is to describe factors that influence the measured intraocular pressure (IOP) change and to develop a predictive model after myopic laser in situ keratomileusis (LASIK) with a femtosecond (FS) laser or a microkeratome (MK). We retrospectively reviewed preoperative, intraoperative, and 12-month postoperative medical records in 2485 eyes of 1309 patients who underwent LASIK with an FS laser or an MK for myopia and myopic astigmatism. Data were extracted, such as preoperative age, sex, IOP, manifest spherical equivalent (MSE), central corneal keratometry (CCK), central corneal thickness (CCT), and intended flap thickness and postoperative IOP (postIOP) at 1, 6 and 12 months. Linear mixed model (LMM) and multivariate linear regression (MLR) method were used for data analysis. In both models, the preoperative CCT and ablation depth had significant effects on predicting IOP changes in the FS and MK groups. The intended flap thickness was a significant predictor only in the FS laser group (P < .0001 in both models). In the FS group, LMM and MLR could respectively explain 47.00% and 18.91% of the variation of postoperative IOP underestimation (R2 = 0.47 and R2 = 0.1891). In the MK group, LMM and MLR could explain 37.79% and 19.13% of the variation of IOP underestimation (R2 = 0.3779 and 0.1913 respectively). The best-fit model for prediction of IOP changes was the LMM in LASIK with an FS laser. PMID:26824754

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

NASA Astrophysics Data System (ADS)

Rahmati, Mehdi

2017-08-01

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed that Ks exclusion from input variables list caused around 30 percent decrease in PTFs accuracy for all applied procedures. However, it seems that Ks exclusion resulted in more practical PTFs especially in the case of GMDH network applying input variables which are less time consuming than Ks. In general, it is concluded that GMDH provides more accurate and reliable estimates of cumulative infiltration (a non-readily available characteristic of soil) with a minimum set of input variables (2-4 input variables) and can be promising strategy to model soil infiltration combining the advantages of ANN and MLR methodologies.

Quantitative computed tomography applied to interstitial lung diseases.

PubMed

Obert, Martin; Kampschulte, Marian; Limburg, Rebekka; Barańczuk, Stefan; Krombach, Gabriele A

2018-03-01

To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers. Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15 th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram's functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R 2 (NR 2 ) effect size were estimated. NR 2 was used to set up a ranking list of the different methods. MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR 2 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR 2 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR 2 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR 2 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR 2 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR 2 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR 2 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR 2 0.48). The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas. Copyright © 2018 Elsevier B.V. All rights reserved.

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.

PubMed

Song, Xiao-Dong; Zhang, Gan-Lin; Liu, Feng; Li, De-Cheng; Zhao, Yu-Guo

2016-11-01

The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400 m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.

DEVELOPMENT OF THE VIRTUAL BEACH MODEL, PHASE 1: AN EMPIRICAL MODEL

EPA Science Inventory

With increasing attention focused on the use of multiple linear regression (MLR) modeling of beach fecal bacteria concentration, the validity of the entire statistical process should be carefully evaluated to assure satisfactory predictions. This work aims to identify pitfalls an...

Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle.

PubMed

Cheng, Jun-Hu; Sun, Da-Wen; Pu, Hongbin

2016-04-15

The potential use of feature wavelengths for predicting drip loss in grass carp fish, as affected by being frozen at -20°C for 24 h and thawed at 4°C for 1, 2, 4, and 6 days, was investigated. Hyperspectral images of frozen-thawed fish were obtained and their corresponding spectra were extracted. Least-squares support vector machine and multiple linear regression (MLR) models were established using five key wavelengths, selected by combining a genetic algorithm and successive projections algorithm, and this showed satisfactory performance in drip loss prediction. The MLR model with a determination coefficient of prediction (R(2)P) of 0.9258, and lower root mean square error estimated by a prediction (RMSEP) of 1.12%, was applied to transfer each pixel of the image and generate the distribution maps of exudation changes. The results confirmed that it is feasible to identify the feature wavelengths using variable selection methods and chemometric analysis for developing on-line multispectral imaging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis.

PubMed

Kamruzzaman, Mohammed; Sun, Da-Wen; ElMasry, Gamal; Allen, Paul

2013-01-15

Many studies have been carried out in developing non-destructive technologies for predicting meat adulteration, but there is still no endeavor for non-destructive detection and quantification of adulteration in minced lamb meat. The main goal of this study was to develop and optimize a rapid analytical technique based on near-infrared (NIR) hyperspectral imaging to detect the level of adulteration in minced lamb. Initial investigation was carried out using principal component analysis (PCA) to identify the most potential adulterate in minced lamb. Minced lamb meat samples were then adulterated with minced pork in the range 2-40% (w/w) at approximately 2% increments. Spectral data were used to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range (910-1700 nm) with a coefficient of determination (R(2)(cv)) of 0.99 and root-mean-square errors estimated by cross validation (RMSECV) of 1.37%. Four important wavelengths (940, 1067, 1144 and 1217 nm) were selected using weighted regression coefficients (Bw) and a multiple linear regression (MLR) model was then established using these important wavelengths to predict adulteration. The MLR model resulted in a coefficient of determination (R(2)(cv)) of 0.98 and RMSECV of 1.45%. The developed MLR model was then applied to each pixel in the image to obtain prediction maps to visualize the distribution of adulteration of the tested samples. The results demonstrated that the laborious and time-consuming tradition analytical techniques could be replaced by spectral data in order to provide rapid, low cost and non-destructive testing technique for adulterate detection in minced lamb meat. Copyright © 2012 Elsevier B.V. All rights reserved.

A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis

PubMed Central

Mu, Lan; Wang, Fahui; Chen, Vivien W.; Wu, Xiao-Cheng

2015-01-01

Similar geographic areas often have great variations in population size. In health data management and analysis, it is desirable to obtain regions of comparable population by decomposing areas of large population (to gain more spatial variability) and merging areas of small population (to mask privacy of data). Based on the Peano curve algorithm and modified scale-space clustering, this research proposes a mixed-level regionalization (MLR) method to construct geographic areas with comparable population. The method accounts for spatial connectivity and compactness, attributive homogeneity, and exogenous criteria such as minimum (and approximately equal) population or disease counts. A case study using Louisiana cancer data illustrates the MLR method and its strengths and limitations. A major benefit of the method is that most upper level geographic boundaries can be preserved to increase familiarity of constructed areas. Therefore, the MLR method is more human-oriented and place-based than computer-oriented and space-based. PMID:26251551

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

USING HYDROGRAPHIC DATA AND THE EPA VIRTUAL BEACH MODEL TO TEST PREDICTIONS OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

A modeling study of 2006 Huntington Beach (Lake Erie) beach bacteria concentrations indicates multi-variable linear regression (MLR) can effectively estimate bacteria concentrations compared to the persistence model. Our use of the Virtual Beach (VB) model affirms that fact. VB i...

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Estimation of Aboveground Biomass Change for Tropical Deciduous Forest in Bago Yoma, Myanmar between year 2000 and 2014 using Landsat Images and Ground Measurements

NASA Astrophysics Data System (ADS)

Kim, H. S.; Wynn, K. Z.; Ryu, Y.

2015-12-01

Even with recently increased awareness of the environmental conservation, the degradation of tropical forests are still one of the major sources of global carbon emission. Especially in Myanmar, the pressure to develop natural forest is growing rapidly after the change from socialism to capitalism in 2010. As the initial step of the forest conservation, the aboveground biomass(AGB) of South Zarmani Reserved Forest in Bago Yoma region were estimated using Landsat 8 OLI after the evaluation with 100 sample plot measurements. Multiple linear regression (MLR) model of band values and their principal component analysis (PCA) model were developed to estimate the AGB using the spectral reflectance from Landsat images and elevation as the input variables. The MLR model had r2 = 0.43, RMSE = 60.2 tons/ha, relative RMSE = 70.1%, Bias = -9.1 tons/ha, Bias (%) = -10.6%, and p < 0.0001, while the PCA model showed r2 = 0.45, RMSE = 55.1 tons/ha, relative RMSE = 64.1%, Bias = -8.3 tons/ha, Bias (%) = -9.7%, and p < 0.0001. The AGB maps of the study area were generated based on both MLR and PCA models. The estimated mean AGB values were 74.74±22.3 tons/ha and 73.04±17.6 tons/ha and the total AGB of the study area are about 5.7 and 5.6 million tons from MLR and PCA, respectively. Then, Landsat 7 ETM+ image acquired on 2000 was also used to compare the changing of AGB between year 2000 and 2014. The estimated mean AGB value generated from the Landsat 7 ETM+ image was 78.9±16.9 tons/ha, which is substantially decreased about 7.5% compared to year 2014. The reduction of AGB increased with closeness to village, however AGB in distant areas showed steady increases. In conclusion, we were able to generate solid regression models from Landsat 8 OLI image after ground truth and two regression models gave us very similar AGB estimation (less than 2%) of the study area. We were also able to estimate the changing of AGB from year 2000 to 2014 of South Zarmani Reserved Forest, Bago Yoma, Myanmar.

Comparing two models for post-wildfire debris flow susceptibility mapping

NASA Astrophysics Data System (ADS)

Cramer, J.; Bursik, M. I.; Legorreta Paulin, G.

2017-12-01

Traditionally, probabilistic post-fire debris flow susceptibility mapping has been performed based on the typical method of failure for debris flows/landslides, where slip occurs along a basal shear zone as a result of rainfall infiltration. Recent studies have argued that post-fire debris flows are fundamentally different in their method of initiation, which is not infiltration-driven, but surface runoff-driven. We test these competing models by comparing the accuracy of the susceptibility maps produced by each initiation method. Debris flow susceptibility maps are generated according to each initiation method for a mountainous region of Southern California that recently experienced wildfire and subsequent debris flows. A multiple logistic regression (MLR), which uses the occurrence of past debris flows and the values of environmental parameters, was used to determine the probability of future debris flow occurrence. The independent variables used in the MLR are dependent on the initiation method; for example, depth to slip plane, and shear strength of soil are relevant to the infiltration initiation, but not surface runoff. A post-fire debris flow inventory serves as the standard to compare the two susceptibility maps, and was generated by LiDAR analysis and field based ground-truthing. The amount of overlap between the true locations where debris flow erosion can be documented, and where the MLR predicts high probability of debris flow initiation was statistically quantified. The Figure of Merit in Space (FMS) was used to compare the two models, and the results of the FMS comparison suggest that surface runoff-driven initiation better explains debris flow occurrence. Wildfire can breed conditions that induce debris flows in areas that normally would not be prone to them. Because of this, nearby communities at risk may not be equipped to protect themselves against debris flows. In California, there are just a few months between wildland fire season and the wet season to assess a community's risk and prepare. It is important, therefore, that researchers have a way to quickly and accurately assess the susceptibility for debris flows in recently burned areas.

The V-band Empirical Mass-luminosity Relation for Main Sequence Stars

NASA Astrophysics Data System (ADS)

Xia, Fang; Fu, Yan-Ning

2010-07-01

Stellar mass is an indispensable parameter in the studies of stellar physics and stellar dynamics. On the one hand, the most reliable way to determine the stellar dynamical mass is via orbital determinations of binaries. On the other hand, however, most stellar masses have to be estimated by using the mass luminosity relation (MLR). Therefore, it is important to obtain the empirical MLR through fitting the data of stellar dynamical mass and luminosity. The effect of metallicity can make this relation disperse in the V-band, but studies show that this is mainly limited to the case when the stellar mass is less than 0.6M⊙ Recently, many relevant data have been accumulated for main sequence stars with larger masses, which make it possible to significantly improve the corresponding MLR. Using a fitting method which can reasonably assign weights to the observational data including two quantities with different dimensions, we obtain a V-band MLR based on the dynamical masses and luminosities of 203 main sequence stars. In comparison with the previous work, the improved MLR is statistically significant, and the relative error of mass estimation reaches about 5%. Therefore, our MLR is useful not only in the studies of statistical nature, but also in the studies of concrete stellar systems, such as the long-term dynamical study and the short-term positioning study of a specific multiple star system.

The V Band Empirical Mass-Luminosity Relation for Main Sequence Stars

NASA Astrophysics Data System (ADS)

Xia, F.; Fu, Y. N.

2010-01-01

Stellar mass is an indispensable parameter in the studies of stellar physics and stellar dynamics. On the one hand, the most reliable way to determine the stellar dynamical mass is via orbital determination of binaries. On the other hand, however, most stellar masses have to be estimated by using the mass-luminosity relation (MLR). Therefore, it is important to obtain the empirical MLR through fitting the data of stellar dynamical mass and luminosity. The effect of metallicity can make this relation disperse in the V-band, but studies show that this is mainly limited to the case when the stellar mass is less than 0.6M⊙. Recently, many relevant data have been accumulated for main sequence stars with larger mass, which make it possible to significantly improve the corresponding MLR. Using a fitting method which can reasonably assign weight to the observational data including two quantities with different dimensions, we obtain a V-band MLR based on the dynamical masses and luminosities of 203 main sequence stars. Compared with the previous work, the improved MLR is statistically significant, and the relative error of mass estimation reaches about 5%. Therefore, our MLR is useful not only in studies of statistical nature, but also in studies of concrete stellar systems, such as the long-term dynamical study and the short-term positioning study of a specific multiple star system.

Characterization of Enzymatic Activity of MlrB and MlrC Proteins Involved in Bacterial Degradation of Cyanotoxins Microcystins.

PubMed

Dziga, Dariusz; Zielinska, Gabriela; Wladyka, Benedykt; Bochenska, Oliwia; Maksylewicz, Anna; Strzalka, Wojciech; Meriluoto, Jussi

2016-03-16

Bacterial degradation of toxic microcystins produced by cyanobacteria is a common phenomenon. However, our understanding of the mechanisms of these processes is rudimentary. In this paper several novel discoveries regarding the action of the enzymes of the mlr cluster responsible for microcystin biodegradation are presented using recombinant proteins. In particular, the predicted active sites of the recombinant MlrB and MlrC were analyzed using functional enzymes and their inactive muteins. A new degradation intermediate, a hexapeptide derived from linearized microcystins by MlrC, was discovered. Furthermore, the involvement of MlrA and MlrB in further degradation of the hexapeptides was confirmed and a corrected biochemical pathway of microcystin biodegradation has been proposed.

Identification of molecular descriptors for design of novel Isoalloxazine derivatives as potential Acetylcholinesterase inhibitors against Alzheimer's disease.

PubMed

Gurung, Arun Bahadur; Aguan, Kripamoy; Mitra, Sivaprasad; Bhattacharjee, Atanu

2017-06-01

In Alzheimer's disease (AD), the level of Acetylcholine (ACh) neurotransmitter is reduced. Since Acetylcholinesterase (AChE) cleaves ACh, inhibitors of AChE are very much sought after for AD treatment. The side effects of current inhibitors necessitate development of newer AChE inhibitors. Isoalloxazine derivatives have proved to be promising (AChE) inhibitors. However, their structure-activity relationship studies have not been reported till date. In the present work, various quantitative structure-activity relationship (QSAR) building methods such as multiple linear regression (MLR), partial least squares ,and principal component regression were employed to derive 3D-QSAR models using steric and electrostatic field descriptors. Statistically significant model was obtained using MLR coupled with stepwise selection method having r 2  = .9405, cross validated r 2 (q 2 ) = .6683, and a high predictability (pred_r 2  = .6206 and standard error, pred_r 2 se = .2491). Steric and electrostatic contribution plot revealed three electrostatic fields E_496, E_386 and E_577 and one steric field S_60 contributing towards biological activity. A ligand-based 3D-pharmacophore model was generated consisting of eight pharmacophore features. Isoalloxazine derivatives were docked against human AChE, which revealed critical residues implicated in hydrogen bonds as well as hydrophobic interactions. The binding modes of docked complexes (AChE_IA1 and AChE_IA14) were validated by molecular dynamics simulation which showed their stable trajectories in terms of root mean square deviation and molecular mechanics/Poisson-Boltzmann surface area binding free energy analysis revealed key residues contributing significantly to overall binding energy. The present study may be useful in the design of more potent Isoalloxazine derivatives as AChE inhibitors.

Predicting active-layer soil thickness using topographic variables at a small watershed scale

PubMed Central

Li, Aidi; Tan, Xing; Wu, Wei; Liu, Hongbin; Zhu, Jie

2017-01-01

Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters. PMID:28877196

Single-Dose Pharmacokinetics of Methylphenidate Extended-Release Multiple Layer Beads Administered as Intact Capsule or Sprinkles Versus Methylphenidate Immediate-Release Tablets (Ritalin®) in Healthy Adult Volunteers

PubMed Central

Teuscher, Nathan S.; Kupper, Robert J.; Chang, Wei-Wei; Greenhill, Laurence; Newcorn, Jeffrey H.; Connor, Daniel F.; Wigal, Sharon

2014-01-01

Abstract Objectives: The purpose of this study was to evaluate the relative bioavailability and safety of a multilayer extended-release bead methylphenidate (MPH) hydrochloride 80 mg (MPH-MLR) capsule or sprinkles (37% immediate-release [IR]) versus MPH hydrochloride IR(Ritalin®) tablets, and to develop a pharmacokinetic (PK) model simulating MPH concentration-time data for different MPH-MLR dosage strengths. Methods: This was a single-center, randomized, open-label, three-period crossover study conducted in 26 fasted healthy adults (mean weight±standard deviation, 70.4±11.7 kg) assigned to single-dose oral MPH-MLR 80 mg capsule or sprinkles with applesauce, or Ritalin IR 25 mg (1×5 mg and 1×20 mg tablet) administered at 0, 4, and 8 hours. Results: MPH-MLR 80 mg capsule and sprinkles were bioequivalent; ratios for maximum concentration (Cmax), area under plasma drug concentration versus time curve (AUC)0-t, and AUC0-inf were 1.04 (95% confidence interval [CI], 96.3–112.4), 0.99 (95% CI, 95.3–102.8), and 0.99 (95% CI, 95.4–103.0), respectively. MPH-MLR capsule/sprinkles produced highly comparable, biphasic profiles of plasma MPH concentrations characterized by rapid initial peak, followed by moderate decline until 5 hours postdose, and gradual increase until 7 hours postdose, culminating in an attenuated second peak. Based on 90% CIs, total systemic exposure to MPH-MLR 80 mg capsule/sprinkles was similar to that for Ritalin IR 25 mg three times daily, but marked differences in Cmax values indicated that MPH-MLR regimens were not bioequivalent to Ritalin. MPH Cmax and total systemic exposure over the first 4 hours postdose with MPH-MLR capsule/sprinkles was markedly higher than that associated with the first dose of Ritalin. All study drugs were safe and well tolerated. The PK modeling in adults suggested that differences in MPH pharmacokinetics between MPH-MLR and Ritalin are the result of dosage form design attributes and the associated absorption profiles of MPH. Conclusions: MPH-MLR 80 mg provides a long-acting biphasic pattern of plasma MPH concentrations with one less peak and trough than Ritalin IR. PMID:25514542

QSPR prediction of the hydroxyl radical rate constant of water contaminants.

PubMed

Borhani, Tohid Nejad Ghaffar; Saniedanesh, Mohammadhossein; Bagheri, Mehdi; Lim, Jeng Shiun

2016-07-01

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

β1-Integrin Deletion From the Lens Activates Cellular Stress Responses Leading to Apoptosis and Fibrosis

PubMed Central

Wang, Yichen; Terrell, Anne M.; Riggio, Brittany A.; Anand, Deepti; Lachke, Salil A.; Duncan, Melinda K.

2017-01-01

Purpose Previous research showed that the absence of β1-integrin from the mouse lens after embryonic day (E) 13.5 (β1MLR10) leads to the perinatal apoptosis of lens epithelial cells (LECs) resulting in severe microphthalmia. This study focuses on elucidating the molecular connections between β1-integrin deletion and this phenotype. Methods RNA sequencing was performed to identify differentially regulated genes (DRGs) in β1MLR10 lenses at E15.5. By using bioinformatics analysis and literature searching, Egr1 (early growth response 1) was selected for further study. The activation status of certain signaling pathways (focal adhesion kinase [FAK]/Erk, TGF-β, and Akt signaling) was studied via Western blot and immunohistochemistry. Mice lacking both β1-integrin and Egr1 genes from the lenses were created (β1MLR10/Egr1−/−) to study their relationship. Results RNA sequencing identified 120 DRGs that include candidates involved in the cellular stress response, fibrosis, and/or apoptosis. Egr1 was investigated in detail, as it mediates cellular stress responses in various cell types, and is recognized as an upstream regulator of numerous other β1MLR10 lens DRGs. In β1MLR10 mice, Egr1 levels are elevated shortly after β1-integrin loss from the lens. Further, pErk1/2 and pAkt are elevated in β1MLR10 LECs, thus providing the potential signaling mechanism that causes Egr1 upregulation in the mutant. Indeed, deletion of Egr1 from β1MLR10 lenses partially rescues the microphthalmia phenotype. Conclusions β1-integrin regulates the appropriate levels of Erk1/2 and Akt phosphorylation in LECs, whereas its deficiency results in the overexpression of Egr1, culminating in reduced cell survival. These findings provide insight into the molecular mechanism underlying the microphthalmia observed in β1MLR10 mice. PMID:28763805

Multi-linear regression models predict the effects of water chemistry on acute lead toxicity to Ceriodaphnia dubia and Pimephales promelas.

PubMed

Esbaugh, A J; Brix, K V; Mager, E M; Grosell, M

2011-09-01

The current study examined the acute toxicity of lead (Pb) to Ceriodaphnia dubia and Pimephales promelas in a variety of natural waters. The natural waters were selected to range in pertinent water chemistry parameters such as calcium, pH, total CO(2) and dissolved organic carbon (DOC). Acute toxicity was determined for C. dubia and P. promelas using standard 48h and 96h protocols, respectively. For both organisms acute toxicity varied markedly according to water chemistry, with C. dubia LC50s ranging from 29 to 180μg/L and P. promelas LC50s ranging from 41 to 3598μg/L. Additionally, no Pb toxicity was observed for P. promelas in three alkaline natural waters. With respect to water chemistry parameters, DOC had the strongest protective impact for both organisms. A multi-linear regression (MLR) approach combining previous lab data and the current data was used to identify the relative importance of individual water chemistry components in predicting acute Pb toxicity for both species. As anticipated, the P. promelas best-fit MLR model combined DOC, calcium and pH. Unexpectedly, in the C. dubiaMLR model the importance of pH, TCO(2) and calcium was minimal while DOC and ionic strength were the controlling water quality variables. Adjusted R(2) values of 0.82 and 0.64 for the P. promelas and C. dubia models, respectively, are comparable to previously developed biotic ligand models for other metals. Copyright © 2011 Elsevier Inc. All rights reserved.

Predicting biological condition in southern California streams

USGS Publications Warehouse

Brown, Larry R.; May, Jason T.; Rehn, Andrew C.; Ode, Peter R.; Waite, Ian R.; Kennen, Jonathan G.

2012-01-01

As understanding of the complex relations among environmental stressors and biological responses improves, a logical next step is predictive modeling of biological condition at unsampled sites. We developed a boosted regression tree (BRT) model of biological condition, as measured by a benthic macroinvertebrate index of biotic integrity (BIBI), for streams in urbanized Southern Coastal California. We also developed a multiple linear regression (MLR) model as a benchmark for comparison with the BRT model. The BRT model explained 66% of the variance in B-IBI, identifying watershed population density and combined percentage agricultural and urban land cover in the riparian buffer as the most important predictors of B-IBI, but with watershed mean precipitation and watershed density of manmade channels also important. The MLR model explained 48% of the variance in B-IBI and included watershed population density and combined percentage agricultural and urban land cover in the riparian buffer. For a verification data set, the BRT model correctly classified 75% of impaired sites (B-IBI < 40) and 78% of unimpaired sites (B-IBI = 40). For the same verification data set, the MLR model correctly classified 69% of impaired sites and 87% of unimpaired sites. The BRT model should not be used to predict B-IBI for specific sites; however, the model can be useful for general applications such as identifying and prioritizing regions for monitoring, remediation or preservation, stratifying new bioassessments according to anticipated biological condition, or assessing the potential for change in stream biological condition based on anticipated changes in population density and development in stream buffers.

A screening system for smear-negative pulmonary tuberculosis using artificial neural networks.

PubMed

de O Souza Filho, João B; de Seixas, José Manoel; Galliez, Rafael; de Bragança Pereira, Basilio; de Q Mello, Fernanda C; Dos Santos, Alcione Miranda; Kritski, Afranio Lineu

2016-08-01

Molecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed. The prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network. Model development considered data from 136 patients with suspected smear-negative PTB in a general hospital. MLP showed higher sensitivity (100%, 95% confidence interval (CI) 78-100%) than the other techniques, such as support vector machine (SVM) linear (86%; 95% CI 60-96%), multivariate logistic regression (MLR) (79%; 95% CI 53-93%), and classification and regression tree (CART) (71%; 95% CI 45-88%). MLR showed a slightly higher specificity (85%; 95% CI 59-96%) than MLP (80%; 95% CI 54-93%), SVM linear (75%, 95% CI 49-90%), and CART (65%; 95% CI 39-84%). In terms of the area under the receiver operating characteristic curve (AUC), the MLP model exhibited a higher value (0.918, 95% CI 0.824-1.000) than the SVM linear (0.796, 95% CI 0.651-0.970) and MLR (0.782, 95% CI 0.663-0.960) models. The significant signs and symptoms identified in risk groups are coherent with clinical practice. In settings with a high prevalence of smear-negative PTB, the system can be useful for screening and also to aid clinical practice in expediting complementary tests for higher risk patients. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Functional characterization of a regulatory human T-cell subpopulation increasing during autologous MLR.

PubMed Central

Cosulich, M E; Risso, A; Canonica, G W; Bargellesi, A

1986-01-01

The present study was undertaken to investigate the heterogeneity of helper T cells in humans using two different monoclonal antibodies: 5/9 and MLR4. The former identifies 15-20% of resting T lymphocytes from peripheral blood and corresponds to an anti-helper/inducer T cell. The second antibody, MLR4, recognizes 5% of total T lymphocytes and partially overlaps with the 5/9+ T cells. In order to investigate functional differences within the 5/9+ cells, we separated two different subsets (5/9+ MLR+ and 5/9+ MLR4-) by a rosetting technique. Although both subsets provide help for Ig synthesis in a PWM-stimulated culture, only the 5/9+ MLR4- fraction gave a proliferative response in both autologous and allogeneic MLR and to soluble protein antigens. The effect of radiation on the ability of the two subsets to provide help for Ig synthesis showed that the 5/9+ MLR4+ subset is highly radiation-sensitive, while 5/9+ MLR- is relatively radiation-resistant. In a further series of experiments, 5/9+ MLR4+ cells isolated after activation in an autologous MLR but not by Con A, were no longer able to induce T-cell differentiation but now showed a strong suppressor effect. The 5/9+ MLR4- subset separated from the same cultures did not display any suppressor function. These data demonstrate in fresh PBL the existence of a radiation-sensitive regulatory subset exerting a helper activity, and which acquires suppressor activity after activation in autologous MLR. PMID:2936679

Should a First Course in ANOVA Be Taught Through MLR?

ERIC Educational Resources Information Center

Williams, John D.

Before implementing a course in the analysis of variance (ANOVA) taught through multiple linear regression, several concerns must be addressed. Adequate computer facilities that are available to students on a low-cost or cost-free basis are necessary; also students must be able to meaningfully communicate with their major advisor regarding their…

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA.

PubMed

Heddam, Salim

2016-09-01

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

MLR-1023 is a potent and selective allosteric activator of Lyn kinase in vitro that improves glucose tolerance in vivo.

PubMed

Saporito, Michael S; Ochman, Alexander R; Lipinski, Christopher A; Handler, Jeffrey A; Reaume, Andrew G

2012-07-01

2(1H)-pyrimidinone,5-(3-methylphenoxy) (MLR-1023) is a candidate for the treatment of type 2 diabetes. The current studies were aimed at determining the mechanism by which MLR-1023 mediates glycemic control. In these studies, we showed that MLR-1023 reduced blood glucose levels without increasing insulin secretion in vivo. We have further determined that MLR-1023 did not activate peroxisome proliferator-activated α, δ, and γ receptors or glucagon-like peptide-1 receptors or inhibit dipeptidyl peptidase-4 or α-glucosidase enzyme activity. However, in an in vitro broad kinase screen MLR-1023 activated the nonreceptor-linked Src-related tyrosine kinase Lyn. MLR-1023 increased the V(max) of Lyn with an EC(50) of 63 nM. This Lyn kinase activation was ATP binding site independent, indicating that MLR-1023 regulated the kinase through an allosteric mechanism. We have established a link between Lyn activation and blood glucose lowering with studies showing that the glucose-lowering effects of MLR-1023 were abolished in Lyn knockout mice, consistent with existing literature linking Lyn kinase and the insulin-signaling pathway. In summary, these studies describe MLR-1023 as a unique blood glucose-lowering agent and show that MLR-1023-mediated blood glucose lowering depends on Lyn kinase activity. These results, coupled with other results (J Pharmacol Exp Ther 342:23-32, 2012), suggest that MLR-1023 and Lyn kinase activation may be a new treatment modality for type 2 diabetes.

A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

USGS Publications Warehouse

Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.

2015-01-01

We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.

Azathioprine and 6-mercaptopurine (6-MP) suppress the human mixed lymphocyte reaction (MLR) by different mechanisms.

PubMed Central

Al-Safi, S A; Maddocks, J L

1984-01-01

6-MP inhibitory effects on the MLR were reversed by AIC (46%), adenine (32%), hypoxanthine (89%), adenosine (86%) and inosine (93%). AIC, adenine, hypoxanthine and inosine had no effect on azathioprine inhibition of the MLR. Adenosine at 10 microM caused 29% reversal and had no effect at 100-400 microM on azathioprine inhibition of the MLR. Reversal of 6-MP suppression of the MLR was decreased with the delay of adenosine addition. Guanine, xanthine and guanosine caused no reversal of 6-MP or azathioprine inhibitory effects on the MLR. These results show that azathioprine and 6-MP suppress the MLR by different mechanisms. PMID:6232936

Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio in mood disorders: A meta-analysis.

PubMed

Mazza, Mario Gennaro; Lucchi, Sara; Tringali, Agnese Grazia Maria; Rossetti, Aurora; Botti, Eugenia Rossana; Clerici, Massimo

2018-06-08

The immune and inflammatory system is involved in the etiology of mood disorders. Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR) and monocyte/lymphocyte ratio (MLR) are inexpensive and reproducible biomarkers of inflammation. This is the first meta-analysis exploring the role of NLR and PLR in mood disorder. We identified 11 studies according to our inclusion criteria from the main Electronic Databases. Meta-analyses were carried out generating pooled standardized mean differences (SMDs) between index and healthy controls (HC). Heterogeneity was estimated. Relevant sensitivity and meta-regression analyses were conducted. Subjects with bipolar disorder (BD) had higher NLR and PLR as compared with HC (respectively SMD = 0.672; p < 0.001; I 2  = 82.4% and SMD = 0.425; p = 0.048; I 2  = 86.53%). Heterogeneity-based sensitivity analyses confirmed these findings. Subgroup analysis evidenced an influence of bipolar phase on the overall estimate whit studies including subjects in manic and any bipolar phase showing a significantly higher NLR and PLR as compared with HC whereas the effect was not significant among studies including only euthymic bipolar subjects. Meta-regression showed that age and sex influenced the relationship between BD and NLR but not the relationship between BD and PLR. Meta-analysis was not carried out for MLR because our search identified only one study when comparing BD to HC, and only one study when comparing MDD to HC. Subjects with major depressive disorder (MDD) had higher NLR as compared with HC (SMD = 0.670; p = 0.028; I 2  = 89.931%). Heterogeneity-based sensitivity analyses and meta-regression confirmed these findings. Our meta-analysis supports the hypothesis that an inflammatory activation occurs in mood disorders and NLR and PLR may be useful to detect this activation. More researches including comparison of NLR, PLR and MLR between different bipolar phases and between BD and MDD are needed. Copyright © 2018 Elsevier Inc. All rights reserved.

Prediction of persistent hemodynamic depression after carotid angioplasty and stenting using artificial neural network model.

PubMed

Jeon, Jin Pyeong; Kim, Chulho; Oh, Byoung-Doo; Kim, Sun Jeong; Kim, Yu-Seop

2018-01-01

To assess and compare predictive factors for persistent hemodynamic depression (PHD) after carotid artery angioplasty and stenting (CAS) using artificial neural network (ANN) and multiple logistic regression (MLR) or support vector machines (SVM) models. A retrospective data set of patients (n=76) who underwent CAS from 2007 to 2014 was used as input (training cohort) to a back-propagation ANN using TensorFlow platform. PHD was defined when systolic blood pressure was less than 90mmHg or heart rate was less 50 beats/min that lasted for more than one hour. The resulting ANN was prospectively tested in 33 patients (test cohort) and compared with MLR or SVM models according to accuracy and receiver operating characteristics (ROC) curve analysis. No significant difference in baseline characteristics between the training cohort and the test cohort was observed. PHD was observed in 21 (27.6%) patients in the training cohort and 10 (30.3%) patients in the test cohort. In the training cohort, the accuracy of ANN for the prediction of PHD was 98.7% and the area under the ROC curve (AUROC) was 0.961. In the test cohort, the number of correctly classified instances was 32 (97.0%) using the ANN model. In contrast, the accuracy rate of MLR or SVM model was both 75.8%. ANN (AUROC: 0.950; 95% CI [confidence interval]: 0.813-0.996) showed superior predictive performance compared to MLR model (AUROC: 0.796; 95% CI: 0.620-0.915, p<0.001) or SVM model (AUROC: 0.885; 95% CI: 0.725-0.969, p<0.001). The ANN model seems to have more powerful prediction capabilities than MLR or SVM model for persistent hemodynamic depression after CAS. External validation with a large cohort is needed to confirm our results. Copyright © 2017. Published by Elsevier B.V.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fiber-Content Measurement of Wool-Cashmere Blends Using Near-Infrared Spectroscopy.

PubMed

Zhou, Jinfeng; Wang, Rongwu; Wu, Xiongying; Xu, Bugao

2017-10-01

Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool-cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool-cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere-wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool-cashmere blend and have potential to be used for tackling the cashmere adulteration problem.

Multinomial Logistic Regression & Bootstrapping for Bayesian Estimation of Vertical Facies Prediction in Heterogeneous Sandstone Reservoirs

NASA Astrophysics Data System (ADS)

Al-Mudhafar, W. J.

2013-12-01

Precisely prediction of rock facies leads to adequate reservoir characterization by improving the porosity-permeability relationships to estimate the properties in non-cored intervals. It also helps to accurately identify the spatial facies distribution to perform an accurate reservoir model for optimal future reservoir performance. In this paper, the facies estimation has been done through Multinomial logistic regression (MLR) with respect to the well logs and core data in a well in upper sandstone formation of South Rumaila oil field. The entire independent variables are gamma rays, formation density, water saturation, shale volume, log porosity, core porosity, and core permeability. Firstly, Robust Sequential Imputation Algorithm has been considered to impute the missing data. This algorithm starts from a complete subset of the dataset and estimates sequentially the missing values in an incomplete observation by minimizing the determinant of the covariance of the augmented data matrix. Then, the observation is added to the complete data matrix and the algorithm continues with the next observation with missing values. The MLR has been chosen to estimate the maximum likelihood and minimize the standard error for the nonlinear relationships between facies & core and log data. The MLR is used to predict the probabilities of the different possible facies given each independent variable by constructing a linear predictor function having a set of weights that are linearly combined with the independent variables by using a dot product. Beta distribution of facies has been considered as prior knowledge and the resulted predicted probability (posterior) has been estimated from MLR based on Baye's theorem that represents the relationship between predicted probability (posterior) with the conditional probability and the prior knowledge. To assess the statistical accuracy of the model, the bootstrap should be carried out to estimate extra-sample prediction error by randomly drawing datasets with replacement from the training data. Each sample has the same size of the original training set and it can be conducted N times to produce N bootstrap datasets to re-fit the model accordingly to decrease the squared difference between the estimated and observed categorical variables (facies) leading to decrease the degree of uncertainty.

A fast and direct spectrophotometric method for the simultaneous determination of methyl paraben and hydroquinone in cosmetic products using successive projections algorithm.

PubMed

Esteki, M; Nouroozi, S; Shahsavari, Z

2016-02-01

To develop a simple and efficient spectrophotometric technique combined with chemometrics for the simultaneous determination of methyl paraben (MP) and hydroquinone (HQ) in cosmetic products, and specifically, to: (i) evaluate the potential use of successive projections algorithm (SPA) to derivative spectrophotometric data in order to provide sufficient accuracy and model robustness and (ii) determine MP and HQ concentration in cosmetics without tedious pre-treatments such as derivatization or extraction techniques which are time-consuming and require hazardous solvents. The absorption spectra were measured in the wavelength range of 200-350 nm. Prior to performing chemometric models, the original and first-derivative absorption spectra of binary mixtures were used as calibration matrices. Variable selected by successive projections algorithm was used to obtain multiple linear regression (MLR) models based on a small subset of wavelengths. The number of wavelengths and the starting vector were optimized, and the comparison of the root mean square error of calibration (RMSEC) and cross-validation (RMSECV) was applied to select effective wavelengths with the least collinearity and redundancy. Principal component regression (PCR) and partial least squares (PLS) were also developed for comparison. The concentrations of the calibration matrix ranged from 0.1 to 20 μg mL(-1) for MP, and from 0.1 to 25 μg mL(-1) for HQ. The constructed models were tested on an external validation data set and finally cosmetic samples. The results indicated that successive projections algorithm-multiple linear regression (SPA-MLR), applied on the first-derivative spectra, achieved the optimal performance for two compounds when compared with the full-spectrum PCR and PLS. The root mean square error of prediction (RMSEP) was 0.083, 0.314 for MP and HQ, respectively. To verify the accuracy of the proposed method, a recovery study on real cosmetic samples was carried out with satisfactory results (84-112%). The proposed method, which is an environmentally friendly approach, using minimum amount of solvent, is a simple, fast and low-cost analysis method that can provide high accuracy and robust models. The suggested method does not need any complex extraction procedure which is time-consuming and requires hazardous solvents. © 2015 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms.

PubMed

Motamarri, Srinivas; Boccelli, Dominic L

2012-09-15

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.

Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand Model.

PubMed

Brix, Kevin V; DeForest, David K; Tear, Lucinda; Grosell, Martin; Adams, William J

2017-05-02

Biotic Ligand Models (BLMs) for metals are widely applied in ecological risk assessments and in the development of regulatory water quality guidelines in Europe, and in 2007 the United States Environmental Protection Agency (USEPA) recommended BLM-based water quality criteria (WQC) for Cu in freshwater. However, to-date, few states have adopted BLM-based Cu criteria into their water quality standards on a state-wide basis, which appears to be due to the perception that the BLM is too complicated or requires too many input variables. Using the mechanistic BLM framework to first identify key water chemistry parameters that influence Cu bioavailability, namely dissolved organic carbon (DOC), pH, and hardness, we developed Cu criteria using the same basic methodology used by the USEPA to derive hardness-based criteria but with the addition of DOC and pH. As an initial proof of concept, we developed stepwise multiple linear regression (MLR) models for species that have been tested over wide ranges of DOC, pH, and hardness conditions. These models predicted acute Cu toxicity values that were within a factor of ±2 in 77% to 97% of tests (5 species had adequate data) and chronic Cu toxicity values that were within a factor of ±2 in 92% of tests (1 species had adequate data). This level of accuracy is comparable to the BLM. Following USEPA guidelines for WQC development, the species data were then combined to develop a linear model with pooled slopes for each independent parameter (i.e., DOC, pH, and hardness) and species-specific intercepts using Analysis of Covariance. The pooled MLR and BLM models predicted species-specific toxicity with similar precision; adjusted R 2 and R 2 values ranged from 0.56 to 0.86 and 0.66-0.85, respectively. Graphical exploration of relationships between predicted and observed toxicity, residuals and observed toxicity, and residuals and concentrations of key input parameters revealed many similarities and a few key distinctions between the performances of the two models. The pooled MLR model was then applied to the species sensitivity distribution to derive acute and chronic criteria equations similar in form to the USEPA's current hardness-based criteria equations but with DOC, pH, and hardness as the independent variables. Overall, the MLR is less responsive to DOC than the BLM across a range of hardness and pH conditions but more responsive to hardness than the BLM. Additionally, at low and intermediate hardness, the MLR model is less responsive than the BLM to pH, but the two models respond comparably at high hardness. The net effect of these different response profiles is that under many typical water quality conditions, MLR- and BLM-based criteria are quite comparable. Indeed, conditions where the two models differ most (high pH/low hardness and low pH/high hardness) are relatively rare in natural aquatic systems. We suggest that this MLR-based approach, which includes the mechanistic foundation of the BLM but is also consistent with widely accepted hardness-dependent WQC in terms of development and form, may facilitate adoption of updated state-wide Cu criteria that more accurately account for the parameters influencing Cu bioavailability than current hardness-based criteria.

Testing an Online English Course: Lessons Learned from an Analysis of Postcourse Proficiency Change Scores

ERIC Educational Resources Information Center

Jee, Rebecca Y.

2015-01-01

Voxy, an English-language-learning company, has developed a custom, in-house proficiency exam, the Voxy Proficiency Assessment (VPA), which is given to all learners at the beginning and end of their courses. Using Multinomial Logistic Regression (MLR), the impact of covariates, such as total learning activities completed and total number of…

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

QSRR modeling for the chromatographic retention behavior of some β-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression.

PubMed

Fouad, Marwa A; Tolba, Enas H; El-Shal, Manal A; El Kerdawy, Ahmed M

2018-05-11

The justified continuous emerging of new β-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 β-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student's t-test showed that there is no statistical significant difference between the models' results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes' chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of β-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones. Copyright © 2018 Elsevier B.V. All rights reserved.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.

PubMed

Alam, Md Saniul; McNabola, Aonghus

2015-05-01

Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance. The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

Detection of cancer in cervical tissue biopsies using mobile lipid resonances measured with diffusion-weighted (1)H magnetic resonance spectroscopy.

PubMed

Zietkowski, D; Davidson, R L; Eykyn, T R; De Silva, S S; Desouza, N M; Payne, G S

2010-05-01

The purpose of this study was to implement a diffusion-weighted sequence for visualisation of mobile lipid resonances (MLR) using high resolution magic angle spinning (HR-MAS) (1)H MRS and to evaluate its use in establishing differences between tissues from patients with cervical carcinoma that contain cancer from those that do not. A stimulated echo sequence with bipolar gradients was modified to allow T(1) and T(2) measurements and optimised by recording signal loss in HR-MAS spectra as a function of gradient strength in model lipids and tissues. Diffusion coefficients, T(1) and apparent T(2) relaxation times were measured in model lipid systems. MLR profiles were characterised in relation to T(1) and apparent T(2) relaxation in human cervical cancer tissue samples. Diffusion-weighted (DW) spectra of cervical biopsies were quantified and peak areas analysed using linear discriminant analysis (LDA). The optimised sequence reduced spectral overlap by suppressing signals originating from low molecular weight metabolites and non-lipid contributions. Significantly improved MLR visualisation allowed visualisation of peaks at 0.9, 1.3, 1.6, 2.0, 2.3, 2.8, 4.3 and 5.3 ppm. MLR analysis of DW spectra showed at least six peaks arising from saturated and unsaturated lipids and those arising from triglycerides. Significant differences in samples containing histologically confirmed cancer were seen for peaks at 0.9 (p < 0.006), 1.3 (p < 0.04), 2.0 (p < 0.03), 2.8 (p < 0.003) and 4.3 ppm (p < 0.0002). LDA analysis of MLR peaks from DW spectra almost completely separated two clusters of cervical biopsies (cancer, 'no-cancer'), reflecting underlying differences in MLR composition. Generated Receiver Operating Characteristic (ROC) curves and calculated area under the curve (0.962) validated high sensitivity and specificity of the technique. Diffusion-weighting of HR-MAS spectroscopic sequences is a useful method for characterising MLR in cancer tissues and displays an accumulation of lipids arising during tumourigenesis and an increase in the unsaturated lipid and triglyceride peaks with respect to saturated MLR. Copyright © 2009 John Wiley & Sons, Ltd.

Development of daily temperature scenarios and their impact on paddy crop evapotranspiration in Kangsabati command area

NASA Astrophysics Data System (ADS)

Dhage, P. M.; Raghuwanshi, N. S.; Singh, R.; Mishra, A.

2017-05-01

Production of the principal paddy crop in West Bengal state of India is vulnerable to climate change due to limited water resources and strong dependence on surface irrigation. Therefore, assessment of impact of temperature scenarios on crop evapotranspiration (ETc) is essential for irrigation management in Kangsabati command (West Bengal). In the present study, impact of the projected temperatures on ETc was studied under climate change scenarios. Further, the performance of the bias correction and spatial downscaling (BCSD) technique was compared with the two well-known downscaling techniques, namely, multiple linear regression (MLR) and Kernel regression (KR), for the projections of daily maximum and minimum air temperatures for four stations, namely, Purulia, Bankura, Jhargram, and Kharagpur. In National Centers for Environmental Prediction (NCEP) and General Circulation Model (GCM), 14 predictors were used in MLR and KR techniques, whereas maximum and minimum surface air temperature predictor of CanESM2 GCM was used in BCSD technique. The comparison results indicated that the performance of the BCSD technique was better than the MLR and KR techniques. Therefore, the BCSD technique was used to project the future temperatures of study locations with three Representative Concentration Pathway (RCP) scenarios for the period of 2006-2100. The warming tendencies of maximum and minimum temperatures over the Kangsabati command area were projected as 0.013 and 0.014 °C/year under RCP 2.6, 0.015 and 0.023 °C/year under RCP 4.5, and 0.056 and 0.061 °C/year under RCP 8.5 for 2011-2100 period, respectively. As a result, kharif (monsoon) crop evapotranspiration demand of Kangsabati reservoir command (project area) will increase by approximately 10, 8, and 18 % over historical demand under RCP 2.6, 4.5, and 8.5 scenarios, respectively.

Reconstruction of Local Sea Levels at South West Pacific Islands—A Multiple Linear Regression Approach (1988-2014)

NASA Astrophysics Data System (ADS)

Kumar, V.; Melet, A.; Meyssignac, B.; Ganachaud, A.; Kessler, W. S.; Singh, A.; Aucan, J.

2018-02-01

Rising sea levels are a critical concern in small island nations. The problem is especially serious in the western south Pacific, where the total sea level rise over the last 60 years has been up to 3 times the global average. In this study, we aim at reconstructing sea levels at selected sites in the region (Suva, Lautoka—Fiji, and Nouméa—New Caledonia) as a multilinear regression (MLR) of atmospheric and oceanic variables. We focus on sea level variability at interannual-to-interdecadal time scales, and trend over the 1988-2014 period. Local sea levels are first expressed as a sum of steric and mass changes. Then a dynamical approach is used based on wind stress curl as a proxy for the thermosteric component, as wind stress curl anomalies can modulate the thermocline depth and resultant sea levels via Rossby wave propagation. Statistically significant predictors among wind stress curl, halosteric sea level, zonal/meridional wind stress components, and sea surface temperature are used to construct a MLR model simulating local sea levels. Although we are focusing on the local scale, the global mean sea level needs to be adjusted for. Our reconstructions provide insights on key drivers of sea level variability at the selected sites, showing that while local dynamics and the global signal modulate sea level to a given extent, most of the variance is driven by regional factors. On average, the MLR model is able to reproduce 82% of the variance in island sea level, and could be used to derive local sea level projections via downscaling of climate models.

Presence or Absence of mlr Genes and Nutrient Concentrations Co-Determine the Microcystin Biodegradation Efficiency of a Natural Bacterial Community.

PubMed

Lezcano, María Ángeles; Morón-López, Jesús; Agha, Ramsy; López-Heras, Isabel; Nozal, Leonor; Quesada, Antonio; El-Shehawy, Rehab

2016-11-03

The microcystin biodegradation potential of a natural bacterial community coexisting with a toxic cyanobacterial bloom was investigated in a water reservoir from central Spain. The biodegradation capacity was confirmed in all samples during the bloom and an increase of mlr A gene copies was found with increasing microcystin concentrations. Among the 24 microcystin degrading strains isolated from the bacterial community, only 28% showed presence of mlr A gene, strongly supporting the existence and abundance of alternative microcystin degradation pathways in nature. In vitro degradation assays with both mlr ⁺ and mlr - bacterial genotypes (with presence and absence of the complete mlr gene cluster, respectively) were performed with four isolated strains ( Sphingopyxis sp. IM-1, IM-2 and IM-3; Paucibacter toxinivorans IM-4) and two bacterial degraders from the culture collection ( Sphingosinicella microcystinivorans Y2; Paucibacter toxinivorans 2C20). Differences in microcystin degradation efficiencies between genotypes were found under different total organic carbon and total nitrogen concentrations. While mlr ⁺ strains significantly improved microcystin degradation rates when exposed to other carbon and nitrogen sources, mlr - strains showed lower degradation efficiencies. This suggests that the presence of alternative carbon and nitrogen sources possibly competes with microcystins and impairs putative non- mlr microcystin degradation pathways. Considering the abundance of the mlr - bacterial population and the increasing frequency of eutrophic conditions in aquatic systems, further research on the diversity of this population and the characterization and conditions affecting non- mlr degradation pathways deserves special attention.

Artificial neural network modeling of dissolved oxygen in reservoir.

PubMed

Chen, Wei-Bo; Liu, Wen-Cheng

2014-02-01

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Source apportionment of PM2.5 at the Lin'an regional background site in China with three receptor models

NASA Astrophysics Data System (ADS)

Deng, Junjun; Zhang, Yanru; Qiu, Yuqing; Zhang, Hongliang; Du, Wenjiao; Xu, Lingling; Hong, Youwei; Chen, Yanting; Chen, Jinsheng

2018-04-01

Source apportionment of fine particulate matter (PM2.5) were conducted at the Lin'an Regional Atmospheric Background Station (LA) in the Yangtze River Delta (YRD) region in China from July 2014 to April 2015 with three receptor models including principal component analysis combining multiple linear regression (PCA-MLR), UNMIX and Positive Matrix Factorization (PMF). The model performance, source identification and source contribution of the three models were analyzed and inter-compared. Source apportionment of PM2.5 was also conducted with the receptor models. Good correlations between the reconstructed and measured concentrations of PM2.5 and its major chemical species were obtained for all models. PMF resolved almost all masses of PM2.5, while PCA-MLR and UNMIX explained about 80%. Five, four and seven sources were identified by PCA-MLR, UNMIX and PMF, respectively. Combustion, secondary source, marine source, dust and industrial activities were identified by all the three receptor models. Combustion source and secondary source were the major sources, and totally contributed over 60% to PM2.5. The PMF model had a better performance on separating the different combustion sources. These findings improve the understanding of PM2.5 sources in background region.

The Lyn kinase activator MLR-1023 is a novel insulin receptor potentiator that elicits a rapid-onset and durable improvement in glucose homeostasis in animal models of type 2 diabetes.

PubMed

Ochman, Alexander R; Lipinski, Christopher A; Handler, Jeffrey A; Reaume, Andrew G; Saporito, Michael S

2012-07-01

MLR-1023 [Tolimidone; CP-26154; 2(1H)-pyrimidinone, 5-(3-methylphenoxy)] is an allosteric Lyn kinase activator that reduces blood glucose levels in mice subjected to an oral glucose tolerance test (J Pharmacol Exp Ther 342:15-22, 2012). The current studies were designed to define the role of insulin in MLR-1023-mediated blood glucose lowering, to evaluate it in animal models of type 2 diabetes, and to compare it to the activities of selected existing diabetes therapeutics. Results from these studies show that in an acute oral glucose tolerance test MLR-1023 evoked a dose-dependent blood glucose-lowering response that was equivalent in magnitude to that of metformin without eliciting a hypoglycemic response. In streptozotocin-treated, insulin-depleted mice, MLR-1023 administration did not affect blood glucose levels. However, MLR-1023 potentiated the glucose-lowering activity of exogenously administered insulin, showing that MLR-1023-mediated blood glucose lowering was insulin-dependent. In a hyperinsulinemic/euglycemic clamp study, orally administered MLR-1023 increased the glucose infusion rate required to sustain blood glucose levels, demonstrating that MLR-1023 increased insulin receptor sensitivity. In chronically treated db/db mice, MLR-1023 elicited a dose-dependent and durable glucose-lowering effect, reduction in HbA1c levels and preservation of pancreatic β-cells. The magnitude of effect was equivalent to that seen with rosiglitazone but with a faster onset of action and without causing weight gain. These studies show that MLR-1023 is an insulin receptor-potentiating agent that produces a rapid-onset and durable blood glucose-lowering activity in diabetic animals.

Polyphenolic, polysaccharide and oligosaccharide composition of Tempranillo red wines and their relationship with the perceived astringency.

PubMed

Quijada-Morín, Natalia; Williams, Pascale; Rivas-Gonzalo, Julián C; Doco, Thierry; Escribano-Bailón, M Teresa

2014-07-01

The influence of the proanthocyanidic, polysaccharide and oligosaccharide composition on astringency perception of Tempranillo wines has been evaluated. Statistical analyses revealed the existence of relationships between chemical composition and perceived astringency. Proanthocyanidic subunit distribution had the strongest contribution to the multiple linear regression (MLR) model. Polysaccharide families showed clear opposition to astringency perception according to principal component analysis (PCA) results, being stronger for mannoproteins and rhamnogalacturonan-II (RG-II), but only Polysaccharides Rich in Arabinose and Galactose (PRAGs) were considered in the final fitted MLR model, which explained 96.8% of the variability observed in the data. Oligosaccharides did not show a clear opposition, revealing that structure and size of carbohydrates are important for astringency perception. Mannose and galactose residues in the oligosaccharide fraction are positively related to astringency perception, probably because its presence is consequence of the degradation of polysaccharides. Copyright © 2014 Elsevier Ltd. All rights reserved.

2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

NASA Astrophysics Data System (ADS)

Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

2017-09-01

The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

Prediction of Caffeine Content in Java Preanger Coffee Beans by NIR Spectroscopy Using PLS and MLR Method

NASA Astrophysics Data System (ADS)

Budiastra, I. W.; Sutrisno; Widyotomo, S.; Ayu, P. C.

2018-05-01

Caffeine is one of important components in coffee that contributes to the coffee beverages flavor. Caffeine concentration in coffee bean is usually determined by chemical method which is time consuming and destructive method. A nondestructive method using NIR spectroscopy was successfully applied to determine the caffeine concentration of Arabica gayo coffee bean. In this study, NIR Spectroscopy was assessed to determine the caffeine concentration of java preanger coffee bean. A hundred samples, each consist of 96 g coffee beans were prepared for reflectance and chemical measurement. Reflectance of the sample was measured by FT-NIR spectrometer in the wavelength of 1000-2500 nm (10000-4000 cm-1) followed by determination of caffeine content using LCMS method. Calibration of NIR spectra and the caffeine content was carried out using PLS and MLR methods. Several spectra data processing was conducted to increase the accuracy of prediction. The result of the study showed that caffeine content could be determined by PLS model using 7 factors and spectra data processing of combination of the first derivative and MSC of spectra absorbance (r = 0.946; CV = 1.54 %; RPD = 2.28). A lower accuracy was obtained by MLR model consisted of three caffeine and other four absorption wavelengths (r = 0.683; CV = 3.31%; RPD = 1.18).

Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China.

PubMed

Yang, Liping; Mei, Kun; Liu, Xingmei; Wu, Laosheng; Zhang, Minghua; Xu, Jianming; Wang, Fan

2013-08-01

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion the sources of pollutants in Wen-Rui-Tang (WRT) river watershed, near the East China Sea. The 12 monitoring sites were divided into three administrative zones of urban, suburban, and rural zones considering differences in land use and population density. Multivariate statistical methods [one-way analysis of variance, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR) methods] were used to investigate the spatial distribution of water quality and to apportion the pollution sources. Results showed that most water quality parameters had no significant difference between the urban and suburban zones, whereas these two zones showed worse water quality than the rural zone. Based on PCA and APCS-MLR analysis, urban domestic sewage and commercial/service pollution, suburban domestic sewage along with fluorine point source pollution, and agricultural nonpoint source pollution with rural domestic sewage pollution were identified to the main pollution sources in urban, suburban, and rural zones, respectively. Understanding the water pollution characteristics of different administrative zones could put insights into effective water management policy-making especially in the area across various administrative zones.

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients.

PubMed

Kohrt, Holbrook E; Olshen, Richard A; Bermas, Honnie R; Goodson, William H; Wood, Douglas J; Henry, Solomon; Rouse, Robert V; Bailey, Lisa; Philben, Vicki J; Dirbas, Frederick M; Dunn, Jocelyn J; Johnson, Denise L; Wapnir, Irene L; Carlson, Robert W; Stockdale, Frank E; Hansen, Nora M; Jeffrey, Stefanie S

2008-03-04

Current practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model. We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University. 285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size. We present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets.

Formative Assessment by First-Year Chemistry Students as Predictor of Success in Summative Assessment at a South African University

ERIC Educational Resources Information Center

Siweya, Hlengani J.; Letsoalo, Peter

2014-01-01

This study investigated whether formative assessment is a predictor of summative assessment in a university first-year chemistry class. The sample comprised a total of 1687 first-year chemistry students chosen from the 2011 and 2012 cohorts. Both simple and multiple linear regression (SLR and MLR) techniques were applied to perform the primary aim…

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Heterologous expression of mlrA in a photoautotrophic host - Engineering cyanobacteria to degrade microcystins.

PubMed

Dexter, Jason; Dziga, Dariusz; Lv, Jing; Zhu, Junqi; Strzalka, Wojciech; Maksylewicz, Anna; Maroszek, Magdalena; Marek, Sylwia; Fu, Pengcheng

2018-06-01

In this report, we establish proof-of-principle demonstrating for the first time genetic engineering of a photoautotrophic microorganism for bioremediation of naturally occurring cyanotoxins. In model cyanobacterium Synechocystis sp. PCC 6803 we have heterologously expressed Sphingopyxis sp. USTB-05 microcystinase (MlrA) bearing a 23 amino acid N-terminus secretion peptide from native Synechocystis sp. PCC 6803 PilA (sll1694). The resultant whole cell biocatalyst displayed about 3 times higher activity against microcystin-LR compared to a native MlrA host (Sphingomonas sp. ACM 3962), normalized for optical density. In addition, MlrA activity was found to be almost entirely located in the cyanobacterial cytosolic fraction, despite the presence of the secretion tag, with crude cellular extracts showing MlrA activity comparable to extracts from MlrA expressing E. coli. Furthermore, despite approximately 9.4-fold higher initial MlrA activity of a whole cell E. coli biocatalyst, utilization of a photoautotrophic chassis resulted in prolonged stability of MlrA activity when cultured under semi-natural conditions (using lake water), with the heterologous MlrA biocatalytic activity of the E. coli culture disappearing after 4 days, while the cyanobacterial host displayed activity (3% of initial activity) after 9 days. In addition, the cyanobacterial cell density was maintained over the duration of this experiment while the cell density of the E. coli culture rapidly declined. Lastly, failure to establish a stable cyanobacterial isolate expressing native MlrA (without the N-terminus tag) via the strong cpcB560 promoter draws attention to the use of peptide tags to positively modulate expression of potentially toxic proteins. Copyright © 2018 Elsevier Ltd. All rights reserved.

Colorimetric determinations of lithium levels in drop-volumes of human plasma for monitoring patients with bipolar mood disorder.

PubMed

Qassem, M; Hickey, M; Kyriacou, P A

2016-08-01

Lithium preparations are considered the most reliable form of mood stabilizing medication for patients with Bipolar disorder. Nevertheless, lithium is a toxic element and its therapeutic range is extremely narrow, with levels of 0.61.0 mEq considered normal, whereas levels above 1.5 mEq are toxic. Thus unfortunately, many patients reach toxic levels that lead to unnecessary complications. It is believed that personal monitoring of blood lithium levels would benefit patients taking lithium medication. Therefore, our aim is to develop a personal lithium blood level analyzer for patients with bipolar mood disorder, and we report here our initial results of a colorimetric-based method used to test drop-volumes of human plasma that had been spiked with lithium. It was possible to validate results with standard flame photometry readings. Applying the Partial Least Squares (PLS) method on preprocessed spectra, therapeutic concentrations of lithium in a single drop can be predicted in a rapid manner, and furthermore, the calibration results were used to select effective wavelengths which were employed as inputs in Multiple Linear Regression (MLR). The simplified algorithms of this would prove useful when developing a personal lithium analyzer. Overall, both calibration methods gave high correlation and small error outputs with a R2= 0.99036 and RMSEC = 0.03778, and R2= 0.994148 and RMSEC= 0.0294404, for PLS and MLR methods, respectively. The results show that the spectrophotometric determination of blood lithium levels can be extended beyond laboratory applications and indicate the capability of this testing principle to be employed in a personal monitoring device. Future work will now focus on the technical development of a miniaturized system for measurement of lithium levels in blood with an acceptable level of accuracy and sensitivity.

Decision making for best cogeneration power integration into a grid

NASA Astrophysics Data System (ADS)

Al Asmar, Joseph; Zakhia, Nadim; Kouta, Raed; Wack, Maxime

2016-07-01

Cogeneration systems are known to be efficient power systems for their ability to reduce pollution. Their integration into a grid requires simultaneous consideration of the economic and environmental challenges. Thus, an optimal cogeneration power are adopted to face such challenges. This work presents a novelty in selectinga suitable solution using heuristic optimization method. Its aim is to optimize the cogeneration capacity to be installed according to the economic and environmental concerns. This novelty is based on the sensitivity and data analysis method, namely, Multiple Linear Regression (MLR). This later establishes a compromise between power, economy, and pollution, which leads to find asuitable cogeneration power, and further, to be integrated into a grid. The data exploited were the results of the Genetic Algorithm (GA) multi-objective optimization. Moreover, the impact of the utility's subsidy on the selected power is shown.

Rapid measurement of methyl cellulose precipitable tannins using ultraviolet spectroscopy with chemometrics: application to red wine and inter-laboratory calibration transfer.

PubMed

Dambergs, Robert G; Mercurio, Meagan D; Kassara, Stella; Cozzolino, Daniel; Smith, Paul A

2012-06-01

Information relating to tannin concentration in grapes and wine is not currently available simply and rapidly enough to inform decision-making by grape growers, winemakers, and wine researchers. Spectroscopy and chemometrics have been implemented for the analysis of critical grape and wine parameters and offer a possible solution for rapid tannin analysis. We report here the development and validation of an ultraviolet (UV) spectral calibration for the prediction of tannin concentration in red wines. Such spectral calibrations reduce the time and resource requirements involved in measuring tannins. A diverse calibration set (n = 204) was prepared with samples of Australian wines of five varieties (Cabernet Sauvignon, Shiraz, Merlot, Pinot Noir, and Durif), from regions spanning the wine grape growing areas of Australia, with varying climate and soils, and with vintages ranging from 1991 to 2007. The relationship between tannin measured by the methyl cellulose precipitation (MCP) reference method at 280 nm and tannin predicted with a multiple linear regression (MLR) calibration, using ultraviolet (UV) absorbance at 250, 270, 280, 290, and 315 nm, was strong (r(2)val = 0.92; SECV = 0.20 g/L). An independent validation set (n = 85) was predicted using the MLR algorithm developed with the calibration set and gave confidence in the ability to predict new samples, independent of the samples used to prepare the calibration (r(2)val = 0.94; SEP = 0.18 g/L). The MLR algorithm could also predict tannin in fermenting wines (r(2)val = 0.76; SEP = 0.18 g/L), but worked best from the second day of ferment on. This study also explored instrument-to-instrument transfer of a spectral calibration for MCP tannin. After slope and bias adjustments of the calibration, efficient calibration transfer to other laboratories was clearly demonstrated, with all instruments in the study effectively giving identical results on a transfer set.

Comparison of Satellite Data with Ground-Based Measurements for Assessing Local Distributions of PM2.5 in Northeast Mexico.

NASA Astrophysics Data System (ADS)

Carmona, J.; Mendoza, A.; Lozano, D.; Gupta, P.; Mejia, G.; Rios, J.; Hernández, I.

2017-12-01

Estimating ground-level PM2.5 from satellite-derived Aerosol Optical Depth (AOD) through statistical models is a promising method to evaluate the spatial and temporal distribution of PM2.5 in regions where there are no or few ground-based observations, i.e. Latin America. Although PM concentrations are most accurately measured using ground-based instrumentation, the spatial coverage is too sparse to determine local and regional variations in PM. AOD satellite data offer the opportunity to overcome the spatial limitation of ground-based measurements. However, estimating PM surface concentrations from AOD satellite data is challenging, since multiple factors can affect the relationship between the total-column of AOD and the surface-concentration of PM. In this study, an Assembled Multiple Linear Regression Model (MLR) and a Neural Network Model (NN) were performed to estimate the relationship between the AOD and ground-concentrations of PM2.5 within the Monterrey Metropolitan Area (MMA). The MMA is located in northeast Mexico and is the third most populated urban area in the country. Episodes of high PM pollution levels are frequent throughout the year at the MMA. Daily averages of meteorological and air quality parameters were determined from data recorded at 5 monitoring sites of the MMA air quality monitoring network. Daily AOD data were retrieved from the MODIS sensor onboard the Aqua satellite. Overall, the best performance of the models was obtained using an AOD at 550 µm from the MYD04_3k product in combination with Temperature, Relative Humidity, Wind Speed and Wind Direction ground-based data. For the MLR performed, a correlation coefficient of R 0.6 and % bias of -6% were obtained. The NN showed a better performance than the MLR, with a correlation coefficient of R 0.75 and % bias -4%. The results obtained confirmed that satellite-derived AOD in combination with meteorological fields may allow to estimate PM2.5 local distributions.

Nailfold Videocapillaroscopic Features and Other Clinical Risk Factors for Digital Ulcers in Systemic Sclerosis: A Multicenter, Prospective Cohort Study

PubMed Central

Herrick, Ariane L.; Distler, Oliver; Becker, Mike O.; Beltran, Emma; Carpentier, Patrick; Ferri, Clodoveo; Inanç, Murat; Vlachoyiannopoulos, Panayiotis; Chadha‐Boreham, Harbajan; Cottreel, Emmanuelle; Pfister, Thomas; Rosenberg, Daniel; Torres, Juan V.; Cutolo, Maurizio; Herrick, Ariane L.; Distler, Oliver; Becker, Mike; Beltran, Emma; Carpentier, Patrick; Ferri, Clodoveo; Inanç, Murat; Vlachoyiannopoulos, Panayiotis; Smith, Vanessa; Erlacher, L; Hirschl, M; Kiener, HP; Pilger, E; Smith, V; Blockmans, D; Wautrecht, J‐C; Becvár, R; Carpentier, P; Frances, C; Lok, C; Sparsa, A; Hachulla, E; Quere, I; Allanore, Y; Agard, C; Riemekasten, G; Hunzelmann, N; Stücker, M; Ahmadi‐Simab, K; Sunderkötter, C; Wohlrab, J; Müller‐Ladner, U; Schneider, M; Vlachoyianopoulos, P; Vassilopoulos, D; Drosos, A; Antonopoulos, A; Balbir‐Gurman, A; Langevitz, P; Rosner, I; Levy, Y; Cutolo, M; Bombardieri, S; Ferraccioli, G; Mazzuca, S; Grassi, W; Lunardi, C; Airó, P; Riccieri, V; Voskuyl, AE; Schuerwegh, A; Santos, L; Rodrigues, AC; Grilo, A; Amaral, MC; Román Ivorra, JA; Castellvi, I; Distler, O; Spertini, F; Müller, R; Inanç, M; Oksel, F; Turkcapar, N; Herrick, A; Denton, C; McHugh, N; Chattopadhyay, C; Hall, F; Buch, M

2016-01-01

Objective To identify nailfold videocapillaroscopic features and other clinical risk factors for new digital ulcers (DUs) during a 6‐month period in patients with systemic sclerosis (SSc). Methods In this multicenter, prospective, observational cohort study, the videoCAPillaroscopy (CAP) study, we evaluated 623 patients with SSc from 59 centers (14 countries). Patients were stratified into 2 groups: a DU history group and a no DU history group. At enrollment, patients underwent detailed nailfold videocapillaroscopic evaluation and assessment of demographic characteristics, DU status, and clinical and SSc characteristics. Risk factors for developing new DUs were assessed using univariable and multivariable logistic regression (MLR) analyses. Results Of the 468 patients in the DU history group (mean ± SD age 54.0 ± 13.7 years), 79.5% were female, 59.8% had limited cutaneous SSc, and 22% developed a new DU during follow‐up. The strongest risk factors for new DUs identified by MLR in the DU history group included the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs (categorized as 0, 1, 2, or ≥3), and the presence of critical digital ischemia. The receiver operating characteristic (ROC) of the area under the curve (AUC) of the final MLR model was 0.738 (95% confidence interval [95% CI] 0.681–0.795). Internal validation through bootstrap generated a ROC AUC of 0.633 (95% CI 0.510–0.756). Conclusion This international prospective study, which included detailed nailfold videocapillaroscopic evaluation and extensive clinical characterization of patients with SSc, identified the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs at enrollment, and the presence of critical digital ischemia at enrollment as risk factors for the development of new DUs. PMID:27111549

The quantitative structure-insecticidal activity relationships from plant derived compounds against chikungunya and zika Aedes aegypti (Diptera:Culicidae) vector.

PubMed

Saavedra, Laura M; Romanelli, Gustavo P; Rozo, Ciro E; Duchowicz, Pablo R

2018-01-01

The insecticidal activity of a series of 62 plant derived molecules against the chikungunya, dengue and zika vector, the Aedes aegypti (Diptera:Culicidae) mosquito, is subjected to a Quantitative Structure-Activity Relationships (QSAR) analysis. The Replacement Method (RM) variable subset selection technique based on Multivariable Linear Regression (MLR) proves to be successful for exploring 4885 molecular descriptors calculated with Dragon 6. The predictive capability of the obtained models is confirmed through an external test set of compounds, Leave-One-Out (LOO) cross-validation and Y-Randomization. The present study constitutes a first necessary computational step for designing less toxic insecticides. Copyright © 2017 Elsevier B.V. All rights reserved.

Response of dissolved trace metals to land use/land cover and their source apportionment using a receptor model in a subtropic river, China.

PubMed

Li, Siyue; Zhang, Quanfa

2011-06-15

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegetation along river networks could better explain metals, and agriculture, regardless of its relative location, however slightly explained metal variables in the upper Han River. FA-MLR analysis identified five source types of metals, and mining, fossil fuel combustion, and vehicle exhaust were the dominant pollutions in the surface waters. The results demonstrated great impacts of human activities on metal concentrations in the subtropical river of China. Copyright © 2011 Elsevier B.V. All rights reserved.

Validation of a light-scattering PM2.5 sensor monitor based on the long-term gravimetric measurements in field tests

PubMed Central

Shi, Jingjin; Chen, Fei’er; Cai, Yunfei; Fan, Shichen; Cai, Jing; Chen, Renjie; Kan, Haidong; Lu, Yihan

2017-01-01

Background Portable direct-reading instruments by light-scattering method are increasingly used in airborne fine particulate matter (PM2.5) monitoring. However, there are limited calibration studies on such instruments by applying the gravimetric method as reference method in field tests. Methods An 8-month sampling was performed and 96 pairs of PM2.5 data by both the gravimetric method and the simultaneous light-scattering real-time monitoring (QT-50) were obtained from July, 2015 to February, 2016 in Shanghai. Temperature and relative humidity (RH) were recorded. Mann-Whitney U nonparametric test and Spearman correlation were used to investigate the differences between the two measurements. Multiple linear regression (MLR) model was applied to set up the calibration model for the light-scattering device. Results The average PM2.5 concentration (median) was 48.1μg/m3 (min-max 10.4–95.8μg/m3) by the gravimetric method and 58.1μg/m3 (19.2–315.9μg/m3) by the light-scattering method, respectively. By time trend analyses, they were significantly correlated with each other (Spearman correlation coefficient 0.889, P<0.01). By MLR, the calibration model for the light-scattering instrument was Y(calibrated) = 57.45 + 0.47 × X(the QT – 50 measurements) – 0.53 × RH – 0.41 × Temp with both RH and temperature adjusted. The 10-fold cross-validation R2 and the root mean squared error of the calibration model were 0.79 and 11.43 μg/m3, respectively. Conclusion Light-scattering measurements of PM2.5 by QT-50 instrument overestimated the concentration levels and were affected by temperature and RH. The calibration model for QT-50 instrument was firstly set up against the gravimetric method with temperature and RH adjusted. PMID:29121101

Artificial Intelligence (AI) Center of Excellence at the University of Pennsylvania

DTIC Science & Technology

1995-07-01

that controls impact forces. Robust Location Estimation for MLR and Non-MLR Distributions (Dissertation Proposal) Gerda L. Kamberova MS-CIS-92-28...Bayesian Approach To Computer Vision Problems Gerda L. Kamberova MS-CIS-92-29 GRASP LAB 310 The object of our study is the Bayesian approach in...Estimation for MLR and Non-MLR Distributions (Dissertation) Gerda L. Kamberova MS-CIS-92-93 GRASP LAB 340 We study the problem of estimating an unknown

Columbia, OV-102, forward middeck locker experiments and meal tray assemblies

NASA Technical Reports Server (NTRS)

1982-01-01

Overall view of forward middeck locker shows Continuous Flow Electrophoresis System (CFES) experiment control and monitoring module and sample storage module (on port side) and Monodisperse Latex Reactor (MLR) (on starboard side). Water Dispenser Kit water gun (above CFES module) and meal tray assemblies covered with snack food packages and beverage containers appear around the two experiments. Thanks to a variety of juices and other food items, this array in the middeck probably represents the most colorful area onboard the Earth-orbiting Columbia, Orbiter Vehicle (OV) 102. Most of the meal items have been carefully fastened to meal tray assemblies (foodtrays) and locker doors (or both). What has not been attached by conventional methods has been safely 'tucked' under something heavy (note jacket shoved into space occupied MLR). MLR is making its second flight and is designed to test the flexibility of making large-size, monodisperse (same size), polystyrene latex micro-spheres using

Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia F. S.; Russo, Ana; Gouveia, Célia M.; Páscoa, Patrícia

2018-04-01

The response of two rainfed winter cereal yields (wheat and barley) to drought conditions in the Iberian Peninsula (IP) was investigated for a long period (1986-2012). Drought hazard was evaluated based on the multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) and three remote sensing indices, namely the Vegetation Condition (VCI), the Temperature Condition (TCI), and the Vegetation Health (VHI) Indices. A correlation analysis between the yield and the drought indicators was conducted, and multiple linear regression (MLR) and artificial neural network (ANN) models were established to estimate yield at the regional level. The correlation values suggested that yield reduces with moisture depletion (low values of VCI) during early-spring and with too high temperatures (low values of TCI) close to the harvest time. Generally, all drought indicators displayed greatest influence during the plant stages in which the crop is photosynthetically more active (spring and summer), rather than the earlier moments of plants life cycle (autumn/winter). Our results suggested that SPEI is more relevant in the southern sector of the IP, while remote sensing indices are rather good in estimating cereal yield in the northern sector of the IP. The strength of the statistical relationships found by MLR and ANN methods is quite similar, with some improvements found by the ANN. A great number of true positives (hits) of occurrence of yield-losses exhibiting hit rate (HR) values higher than 69% was obtained.

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

PubMed Central

2012-01-01

Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management. PMID:23369363

Dietary predictors of childhood obesity in a representative sample of children in north east of Iran.

PubMed

Baygi, Fereshteh; Qorbani, Mostafa; Dorosty, Ahmad Reza; Kelishadi, Roya; Asayesh, Hamid; Rezapour, Aziz; Mohammadi, Younes; Mohammadi, Fatemeh

2013-07-01

The prevalence of obesity is increasing in Iranian youngsters. This study aimed to assess some dietary determinants of obesity in a representative sample of children in Neishabour, a city in northeastern, Iran. This case-control study was conducted among 114 school students, aged 6-12 years, with a body mass index (BMI) ≥95th (based on percentile of Iranian children) as the case group and 102 age- and gender-matched controls, who were selected from their non-obese classmates. Nutrient intake data were collected by trained nutritionists by using two 24-hour-dietary recalls through maternal interviews in the presence of their child. A food frequency questionnaire was used for detecting the snack consumption patterns. Statistical analysis was done using univariate and multivariate logistic regression (MLR) by SPSS version 16. In univariate logistic regression, total energy, protein, carbohydrate, fat (including saturated, mono- and poly-unsaturated fat), and dietary fiber were the positive predictors of obesity in studied children. The estimated crude ORs for frequency of corn-based extruded snacks, carbonated beverages, potato chips, fast foods, and chocolate consumption were statistically significant. After MLR analysis, the association of obesity remained significant with energy intake (OR = 2.489, 95%CI: 1.667-3.716), frequency of corn-based extruded snacks (OR = 1.122, 95%CI: 1.007-1.250), and potato chips (OR = 1.143, 95%CI:1.024-1.276). The MLR analysis showed that dietary fiber (OR = 0.601, 95%CI: 0.368-0.983) and natural fruit juice intake (OR = 0.909, 95%CI: 0.835-0.988) were protective factors against obesity. The findings serve to confirm the role of an unhealthy diet, notably calorie-dense snacks, in childhood obesity. Healthy dietary habits, such as the consumption of high-fiber foods, should be encouraged among children.

Validation of a light-scattering PM2.5 sensor monitor based on the long-term gravimetric measurements in field tests.

PubMed

Shi, Jingjin; Chen, Fei'er; Cai, Yunfei; Fan, Shichen; Cai, Jing; Chen, Renjie; Kan, Haidong; Lu, Yihan; Zhao, Zhuohui

2017-01-01

Portable direct-reading instruments by light-scattering method are increasingly used in airborne fine particulate matter (PM2.5) monitoring. However, there are limited calibration studies on such instruments by applying the gravimetric method as reference method in field tests. An 8-month sampling was performed and 96 pairs of PM2.5 data by both the gravimetric method and the simultaneous light-scattering real-time monitoring (QT-50) were obtained from July, 2015 to February, 2016 in Shanghai. Temperature and relative humidity (RH) were recorded. Mann-Whitney U nonparametric test and Spearman correlation were used to investigate the differences between the two measurements. Multiple linear regression (MLR) model was applied to set up the calibration model for the light-scattering device. The average PM2.5 concentration (median) was 48.1μg/m3 (min-max 10.4-95.8μg/m3) by the gravimetric method and 58.1μg/m3 (19.2-315.9μg/m3) by the light-scattering method, respectively. By time trend analyses, they were significantly correlated with each other (Spearman correlation coefficient 0.889, P<0.01). By MLR, the calibration model for the light-scattering instrument was Y(calibrated) = 57.45 + 0.47 × X(the QT - 50 measurements) - 0.53 × RH - 0.41 × Temp with both RH and temperature adjusted. The 10-fold cross-validation R2 and the root mean squared error of the calibration model were 0.79 and 11.43 μg/m3, respectively. Light-scattering measurements of PM2.5 by QT-50 instrument overestimated the concentration levels and were affected by temperature and RH. The calibration model for QT-50 instrument was firstly set up against the gravimetric method with temperature and RH adjusted.

Chemical structure-based predictive model for the oxidation of trace organic contaminants by sulfate radical.

PubMed

Ye, Tiantian; Wei, Zongsu; Spinney, Richard; Tang, Chong-Jian; Luo, Shuang; Xiao, Ruiyang; Dionysiou, Dionysios D

2017-06-01

Second-order rate constants [Formula: see text] for the reaction of sulfate radical anion (SO 4 •- ) with trace organic contaminants (TrOCs) are of scientific and practical importance for assessing their environmental fate and removal efficiency in water treatment systems. Here, we developed a chemical structure-based model for predicting [Formula: see text] using 32 molecular fragment descriptors, as this type of model provides a quick estimate at low computational cost. The model was constructed using the multiple linear regression (MLR) and artificial neural network (ANN) methods. The MLR method yielded adequate fit for the training set (R training 2 =0.88,n=75) and reasonable predictability for the validation set (R validation 2 =0.62,n=38). In contrast, the ANN method produced a more statistical robustness but rather poor predictability (R training 2 =0.99andR validation 2 =0.42). The reaction mechanisms of SO 4 •- reactivity with TrOCs were elucidated. Our result shows that the coefficients of functional groups reflect their electron donating/withdrawing characters. For example, electron donating groups typically exhibit positive coefficients, indicating enhanced SO 4 •- reactivity. Electron withdrawing groups exhibit negative values, indicating reduced reactivity. With its quick and accurate features, we applied this structure-based model to 55 discrete TrOCs culled from the Contaminant Candidate List 4, and quantitatively compared their removal efficiency with SO 4 •- and OH in the presence of environmental matrices. This high-throughput model helps prioritize TrOCs that are persistent to SO 4 •- based oxidation technologies at the screening level, and provide diagnostics of SO 4 •- reaction mechanisms. Copyright © 2017 Elsevier Ltd. All rights reserved.

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Presence or Absence of mlr Genes and Nutrient Concentrations Co-Determine the Microcystin Biodegradation Efficiency of a Natural Bacterial Community

PubMed Central

Lezcano, María Ángeles; Morón-López, Jesús; Agha, Ramsy; López-Heras, Isabel; Nozal, Leonor; Quesada, Antonio; El-Shehawy, Rehab

2016-01-01

The microcystin biodegradation potential of a natural bacterial community coexisting with a toxic cyanobacterial bloom was investigated in a water reservoir from central Spain. The biodegradation capacity was confirmed in all samples during the bloom and an increase of mlrA gene copies was found with increasing microcystin concentrations. Among the 24 microcystin degrading strains isolated from the bacterial community, only 28% showed presence of mlrA gene, strongly supporting the existence and abundance of alternative microcystin degradation pathways in nature. In vitro degradation assays with both mlr+ and mlr− bacterial genotypes (with presence and absence of the complete mlr gene cluster, respectively) were performed with four isolated strains (Sphingopyxis sp. IM-1, IM-2 and IM-3; Paucibacter toxinivorans IM-4) and two bacterial degraders from the culture collection (Sphingosinicella microcystinivorans Y2; Paucibacter toxinivorans 2C20). Differences in microcystin degradation efficiencies between genotypes were found under different total organic carbon and total nitrogen concentrations. While mlr+ strains significantly improved microcystin degradation rates when exposed to other carbon and nitrogen sources, mlr− strains showed lower degradation efficiencies. This suggests that the presence of alternative carbon and nitrogen sources possibly competes with microcystins and impairs putative non-mlr microcystin degradation pathways. Considering the abundance of the mlr− bacterial population and the increasing frequency of eutrophic conditions in aquatic systems, further research on the diversity of this population and the characterization and conditions affecting non-mlr degradation pathways deserves special attention. PMID:27827872

Simultaneous determination of hydroquinone, resorcinol, phenol, m-cresol and p-cresol in untreated air samples using spectrofluorimetry and a custom multiple linear regression-successive projection algorithm.

PubMed

Pistonesi, Marcelo F; Di Nezio, María S; Centurión, María E; Lista, Adriana G; Fragoso, Wallace D; Pontes, Márcio J C; Araújo, Mário C U; Band, Beatriz S Fernández

2010-12-15

In this study, a novel, simple, and efficient spectrofluorimetric method to determine directly and simultaneously five phenolic compounds (hydroquinone, resorcinol, phenol, m-cresol and p-cresol) in air samples is presented. For this purpose, variable selection by the successive projections algorithm (SPA) is used in order to obtain simple multiple linear regression (MLR) models based on a small subset of wavelengths. For comparison, partial least square (PLS) regression is also employed in full-spectrum. The concentrations of the calibration matrix ranged from 0.02 to 0.2 mg L(-1) for hydroquinone, from 0.05 to 0.6 mg L(-1) for resorcinol, and from 0.05 to 0.4 mg L(-1) for phenol, m-cresol and p-cresol; incidentally, such ranges are in accordance with the Argentinean environmental legislation. To verify the accuracy of the proposed method a recovery study on real air samples of smoking environment was carried out with satisfactory results (94-104%). The advantage of the proposed method is that it requires only spectrofluorimetric measurements of samples and chemometric modeling for simultaneous determination of five phenols. With it, air is simply sampled and no pre-treatment sample is needed (i.e., separation steps and derivatization reagents are avoided) that means a great saving of time. Copyright © 2010 Elsevier B.V. All rights reserved.

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

PubMed

Heddam, Salim

2014-11-01

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).

75 FR 82277 - Health Insurance Issuers Implementing Medical Loss Ratio (MLR) Requirements Under the Patient...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-30

...-AA06 Health Insurance Issuers Implementing Medical Loss Ratio (MLR) Requirements Under the Patient... Register (FR Doc 2010-29596 (75 FR 74864)) entitled ``Health Insurance Issuers Implementing Medical Loss... request for comments entitled ``Health Insurance Issuers Implementing Medical Loss Ratio (MLR...

A Randomized Placebo-Controlled Double-Blind Study Evaluating the Time Course of Response to Methylphenidate Hydrochloride Extended-Release Capsules in Children with Attention-Deficit/Hyperactivity Disorder

PubMed Central

Greenhill, Laurence L.; Nordbrock, Earl; Connor, Daniel F.; Kollins, Scott H.; Adjei, Akwete; Childress, Ann; Stehli, Annamarie; Kupper, Robert J.

2014-01-01

Abstract Objective: The purpose of this study was to assess the time of onset and time course of efficacy over 12.0 hours of extended-release multilayer bead formulation of methylphenidate (MPH-MLR) compared with placebo in children 6–12 years of age with attention-deficit/hyperactivity disorder (ADHD) in a laboratory school setting. Methods: This randomized double-blind placebo-controlled study included children 6–12 years of age with ADHD. Enrolled children went through four study phases: 1) Screening period (≤4 weeks) and a 2 day medication washout period; 2) open-label period with dose initiation of MPH-MLR 15 mg daily and individual dose optimization treatment period (2–4 weeks); 3) double-blind crossover period in which participants were randomized to sequences (1 week each) of placebo and the optimized MPH-MLR dose given daily; and 4) follow-up safety call. Analog classroom time course evaluations were performed at the end of each double-blind week. The primary efficacy end-point was the mean of the on-treatment/postdose Swanson, Kotkin, Agler, M-Flynn, and Pelham (SKAMP)-Total scores over time points collected 1.0–12.0 hours after dosing. End-points were evaluated using a mixed-effects analysis of covariance. Results: The evaluable population included 20 participants. The least-squares mean postdose SKAMP-Total score was higher for placebo than for MPH-MLR (2.18 vs. 1.32, respectively; p=0.0001), indicating fewer symptoms with MPH-MLR therapy than with placebo. No difference in SKAMP-Total score between participants who received sequence 1 or sequence 2 was noted. From each of hours 1.0–12.0, least-squares mean SKAMP-Total score was significantly lower for those receiving MPH-MLR than for those receiving placebo (p≤0.0261). Neither serious adverse events nor new or unexpected safety findings were noted during the study. Conclusions: MPH-MLR showed a significant decrease in SKAMP scores compared with placebo in children with ADHD 6–12 years of age, indicating a decrease in ADHD symptoms. The estimated onset was observed within 1.0 hour, and duration was measured to 12.0 hours postdose. Trial registration: ClinicalTrials.gov Identifier: NCT01269463 PMID:25470572

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

NASA Astrophysics Data System (ADS)

Citakoglu, Hatice

2017-10-01

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient ( R 2 )) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

NASA Astrophysics Data System (ADS)

Ausati, Shadi; Amanollahi, Jamil

2016-10-01

Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

Constructing high-accuracy intermolecular potential energy surface with multi-dimension Morse/Long-Range model

NASA Astrophysics Data System (ADS)

Zhai, Yu; Li, Hui; Le Roy, Robert J.

2018-04-01

Spectroscopically accurate Potential Energy Surfaces (PESs) are fundamental for explaining and making predictions of the infrared and microwave spectra of van der Waals (vdW) complexes, and the model used for the potential energy function is critically important for providing accurate, robust and portable analytical PESs. The Morse/Long-Range (MLR) model has proved to be one of the most general, flexible and accurate one-dimensional (1D) model potentials, as it has physically meaningful parameters, is flexible, smooth and differentiable everywhere, to all orders and extrapolates sensibly at both long and short ranges. The Multi-Dimensional Morse/Long-Range (mdMLR) potential energy model described herein is based on that 1D MLR model, and has proved to be effective and accurate in the potentiology of various types of vdW complexes. In this paper, we review the current status of development of the mdMLR model and its application to vdW complexes. The future of the mdMLR model is also discussed. This review can serve as a tutorial for the construction of an mdMLR PES.

Effect of Aptensio XR (Methylphenidate HCl Extended-Release) Capsules on Sleep in Children with Attention-Deficit/Hyperactivity Disorder.

PubMed

Owens, Judith; Weiss, Margaret; Nordbrock, Earl; Mattingly, Greg; Wigal, Sharon; Greenhill, Laurence L; Chang, Wei-Wei; Childress, Ann; Kupper, Robert J; Adjei, Akwete

2016-12-01

To evaluate measures of sleep (exploratory endpoints) in two pivotal studies of a multilayer bead extended-release methylphenidate (MPH-MLR) treatment of attention-deficit/hyperactivity disorder in children. Study 1 evaluated the time course of response to MPH-MLR (n = 26) patients in an analog classroom setting through four phases: screening (≤28 days), open label (OL) dose optimization (4 weeks), double-blind (DB) crossover (2 weeks; placebo vs. optimized dose), and follow-up call. Study 2 was a forced-dose parallel evaluation of MPH-MLR (n = 230) in four phases: screening (≤28 days), DB (1 week; placebo or MPH-MLR 10, 15, 20, or 40 mg/day), OL dose optimization (11 weeks), and follow-up call. Sleep was evaluated by parents using the Children's or Adolescent Sleep Habits Questionnaire (CSHQ or ASHQ) during the DB and OL phases. DB analysis: Study 1 (crossover), analysis of variance; Study 2, analysis of covariance. OL analysis: paired t-test. DB: treatments were significantly different in Study 1 only for CSHQ Sleep Onset Delay (MPH-MLR, 1.90 vs. placebo, 1.34; p = 0.0046, placebo was better), and Study 2 for CSHQ Parasomnias (treatment, p = 0.0295), but no MPH-MLR treatment was different from placebo (pairwise MPH-MLR treatment to placebo, all p ≥ 0.170). OL: CSHQ total and Bedtime Resistance, Sleep Duration, Sleep Anxiety, Night Wakings, Parasomnias, and Sleep-disordered Breathing subscales decreased (improved, Study 1) significant only for CSHQ Night Wakings (p < 0.05); in Study 2 CSHQ total and Bedtime Resistance, Sleep Duration, Night Wakings, Parasomnias, and Daytime Sleepiness, and ASHQ total, Bedtime, Sleep Behavior, and Morning Waking all significantly improved (p < 0.05). In both studies, there was minimal negative impact of MPH-MLR on sleep during the brief DB phase and none during the longer duration OL phase. Some measures of sleep improved with optimized MPH-MLR dose.

Microfiber Structures for Sensor Applications

NASA Astrophysics Data System (ADS)

Harun, S. W.; Lim, K. S.; Ahmad, H.

Microfiber loop resonator (MLR) and microfiber knot resonator (MKR) are fabricated using melt-stretching method for applications in temperature and current sensor, respectively. The MLR is embedded into low refractive index polymer for robustness. Although the spacing of the transmission comb spectrum of the MLR is unchanged with temperature, the extinction ratio of the spectrum is observed to decrease linearly with temperature due to induced changes in the material's refractive index. The slope of the extinction ratio reduction against temperature is about 0.043dB/°C. With the assistance of a copper wire that is wrapped by the MKR, resonant wavelength can be tuned by varying the electric current delivered to the wire. The resonant wavelength change is based on the thermally induced optical phase shift in the MKR due to the heat produced by the flow of electric current over a short transit length. It is shown that the wavelength shift is linearly proportional to the square of current in the copper wire with a tuning slope of 46 pm/A2.

QSAR Study of p56lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

PubMed Central

Fassihi, Afshin; Sabet, Razieh

2008-01-01

Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously. PMID:19325836

Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

NASA Astrophysics Data System (ADS)

Snedden, Gregg A.; Steyer, Gregory D.

2013-02-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-06-28

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is superior by almost an order of magnitude over the ANN method in terms of the stability, generality, and robustness of the final model. The LS-SVM model needs a much smaller numbers of samples (a much smaller sample set) to make accurate prediction results. Potential energy surface (PES) approximations for molecular dynamics (MD) studies are discussed as a promising application for the LS-SVM calibration approach. This journal is © the Owner Societies 2011

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

Application of Electronic Nose for Measuring Total Volatile Basic Nitrogen and Total Viable Counts in Packaged Pork During Refrigerated Storage.

PubMed

Li, Miaoyun; Wang, Haibiao; Sun, Lingxia; Zhao, Gaiming; Huang, Xianqing

2016-04-01

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB-N) in pork using an electronic nose (E-nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O2 /40% CO2 /20% N2 ). Principal component analysis (PCA) was used to analyze the E-nose signals, and the results showed that the relationships between the freshness of chilled pork and E-nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB-N to E-nose signals. High F and R2 values were obtained in the MLR output of TVB-N (F = 32.1, 21.6, and 24.2 for PP [R2 = 0.93], VP [R2 = 0.94], and MAP [R2 = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R2 = 0.98], VP [R2 = 0.89], and MAP [R2 = 0.85], respectively). The results of this study suggest that it is possible to use the E-nose technology to predict TVB-N and TVC for assessing the freshness of chilled pork during storage. © 2016 Institute of Food Technologists®

Extraction of indirectly captured information for use in a comparison of offline pH measurement technologies.

PubMed

Ritchie, Elspeth K; Martin, Elaine B; Racher, Andy; Jaques, Colin

2017-06-10

Understanding the causes of discrepancies in pH readings of a sample can allow more robust pH control strategies to be implemented. It was found that 59.4% of differences between two offline pH measurement technologies for an historical dataset lay outside an expected instrument error range of ±0.02pH. A new variable, Osmo Res , was created using multiple linear regression (MLR) to extract information indirectly captured in the recorded measurements for osmolality. Principal component analysis and time series analysis were used to validate the expansion of the historical dataset with the new variable Osmo Res . MLR was used to identify variables strongly correlated (p<0.05) with differences in pH readings by the two offline pH measurement technologies. These included concentrations of specific chemicals (e.g. glucose) and Osmo Res, indicating culture medium and bolus feed additions as possible causes of discrepancies between the offline pH measurement technologies. Temperature was also identified as statistically significant. It is suggested that this was a result of differences in pH-temperature compensations employed by the pH measurement technologies. In summary, a method for extracting indirectly captured information has been demonstrated, and it has been shown that competing pH measurement technologies were not necessarily interchangeable at the desired level of control (±0.02pH). Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming.

PubMed

Barmpalexis, Panagiotis; Grypioti, Agni; Eleftheriadis, Georgios K; Fatouros, Dimitris G

2018-02-01

In the present study, liquisolid formulations were developed for improving dissolution profile of aprepitant (APT) in a solid dosage form. Experimental studies were complemented with artificial neural networks and genetic programming. Specifically, the type and concentration of liquid vehicle was evaluated through saturation-solubility studies, while the effect of the amount of viscosity increasing agent (HPMC), the type of wetting (Soluplus® vs. PVP) and solubilizing (Poloxamer®407 vs. Kolliphor®ELP) agents, and the ratio of solid coating (microcrystalline cellulose) to carrier (colloidal silicon dioxide) were evaluated based on in vitro drug release studies. The optimum liquisolid formulation exhibited improved dissolution characteristics compared to the marketed product Emend®. X-ray diffraction (XRD), scanning electron microscopy (SEM) and a novel method combining particle size analysis by dynamic light scattering (DLS) and HPLC, revealed that the increase in dissolution rate of APT in the optimum liquisolid formulation was due to the formation of stable APT nanocrystals. Differential scanning calorimetry (DSC) and attenuated total reflection FTIR spectroscopy (ATR-FTIR) revealed the presence of intermolecular interactions between APT and liquisolid formulation excipients. Multilinear regression analysis (MLR), artificial neural networks (ANNs), and genetic programming (GP) were used to correlate several formulation variables with dissolution profile parameters (Y 15min and Y 30min ) using a full factorial experimental design. Results showed increased correlation efficacy for ANNs and GP (RMSE of 0.151 and 0.273, respectively) compared to MLR (RMSE = 0.413).

Linkages between denitrification and dissolved organicmatter quality, Boulder Creek watershed, Colorado

USGS Publications Warehouse

Barnes, Rebecca T.; Smith, Richard L.; Aiken, George R.

2012-01-01

Dissolved organic matter (DOM) fuels the majority of in-stream microbial processes, including the removal of nitrate via denitrification. However, little is known about how the chemical composition of DOM influences denitrification rates. Water and sediment samples were collected across an ecosystem gradient, spanning the alpine to plains, in central Colorado to determine whether the chemical composition of DOM was related to denitrification rates. Laboratory bioassays measured denitrification potentials using the acetylene block technique and carbon mineralization via aerobic bioassays, while organic matter characteristics were evaluated using spectroscopic and fractionation methods. Denitrification potentials under ambient and elevated nitrate concentrations were strongly correlated with aerobic respiration rates and the percent mineralized carbon, suggesting that information about the aerobic metabolism of a system can provide valuable insight regarding the ability of the system to additionally reduce nitrate. Multiple linear regressions (MLR) revealed that under elevated nitrate concentrations denitrification potentials were positively related to the presence of protein-like fluorophores and negatively related to more aromatic and oxidized fractions of the DOM pool. Using MLR, the chemical composition of DOM, carbon, and nitrate concentrations explained 70% and 78% of the observed variability in denitrification potential under elevated and ambient nitrate conditions, respectively. Thus, it seems likely that DOM optical properties could help to improve predictions of nitrate removal in the environment. Finally, fluorescence measurements revealed that bacteria used both protein and humic-like organic molecules during denitrification providing further evidence that larger, more aromatic molecules are not necessarily recalcitrant in the environment.

[Prognostic value of three different staging schemes based on pN, MLR and LODDS in patients with T3 esophageal cancer].

PubMed

Wang, L; Cai, L; Chen, Q; Jiang, Y H

2017-10-23

Objective: To evaluate the prognostic value of three different staging schemes based on positive lymph nodes (pN), metastatic lymph nodes ratio (MLR) and log odds of positive lymph nodes (LODDS) in patients with T3 esophageal cancer. Methods: From 2007 to 2014, clinicopathological characteristics of 905 patients who were pathologically diagnosed as T3 esophageal cancer and underwent radical esophagectomy in Zhejiang Cancer Hospital were retrospectively analyzed. Kaplan-Meier curves and Multivariate Cox proportional hazards models were used to evaluate the independent prognostic factors. The values of three lymph node staging schemes for predicting 5-year survival were analyzed by using receiver operating characteristic (ROC) curves. Results: The 1-, 3- and 5-year overall survival rates of patients with T3 esophageal cancer were 80.9%, 50.0% and 38.4%, respectively. Multivariate analysis showed that MLR stage, LODDS stage and differentiation were independent prognostic survival factors ( P <0.05 for all). ROC curves showed that the area under the curve of pN stage, MLR stage, LODDS stage was 0.607, 0.613 and 0.618, respectively. However, the differences were not statistically significant ( P >0.05). Conclusions: LODDS is an independent prognostic factor for patients with T3 esophageal cancer. The value of LODDS staging system may be superior to pN staging system for evaluating the prognosis of these patients.

45 CFR 158.330 - Criteria for assessing request for adjustment to the medical loss ratio.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Potential Adjustment to the MLR for a State's Individual Market § 158.330 Criteria for assessing request for... individual market in a State that has requested an adjustment to the 80 percent MLR: (a) The number of... adjustment to the 80 percent MLR and the resulting impact on competition in the State. In making this...

45 CFR 158.330 - Criteria for assessing request for adjustment to the medical loss ratio.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Potential Adjustment to the MLR for a State's Individual Market § 158.330 Criteria for assessing request for... individual market in a State that has requested an adjustment to the 80 percent MLR: (a) The number of... adjustment to the 80 percent MLR and the resulting impact on competition in the State. In making this...

45 CFR 158.330 - Criteria for assessing request for adjustment to the medical loss ratio.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Potential Adjustment to the MLR for a State's Individual Market § 158.330 Criteria for assessing request for... individual market in a State that has requested an adjustment to the 80 percent MLR: (a) The number of... adjustment to the 80 percent MLR and the resulting impact on competition in the State. In making this...

45 CFR 158.330 - Criteria for assessing request for adjustment to the medical loss ratio.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Potential Adjustment to the MLR for a State's Individual Market § 158.330 Criteria for assessing request for... individual market in a State that has requested an adjustment to the 80 percent MLR: (a) The number of... adjustment to the 80 percent MLR and the resulting impact on competition in the State. In making this...

Stx1 prophage excision in Escherichia coli strain PA20 confers strong curli and biofilm formation by restoring native mlrA

USDA-ARS?s Scientific Manuscript database

Prophage insertions in Escherichia coli O157:H7 mlrA contribute to the low expression of curli fimbriae and biofilm observed in many clinical isolates. Varying levels of CsgD-dependent curli/biofilm expression are restored to strains bearing prophage insertions in mlrA by mutation of regulatory gene...

Single-dose pharmacokinetics of methylphenidate extended-release multiple layer beads administered as intact capsule or sprinkles versus methylphenidate immediate-release tablets (Ritalin(®)) in healthy adult volunteers.

PubMed

Adjei, Akwete; Teuscher, Nathan S; Kupper, Robert J; Chang, Wei-Wei; Greenhill, Laurence; Newcorn, Jeffrey H; Connor, Daniel F; Wigal, Sharon

2014-12-01

The purpose of this study was to evaluate the relative bioavailability and safety of a multilayer extended-release bead methylphenidate (MPH) hydrochloride 80 mg (MPH-MLR) capsule or sprinkles (37% immediate-release [IR]) versus MPH hydrochloride IR(Ritalin(®)) tablets, and to develop a pharmacokinetic (PK) model simulating MPH concentration-time data for different MPH-MLR dosage strengths. This was a single-center, randomized, open-label, three-period crossover study conducted in 26 fasted healthy adults (mean weight±standard deviation, 70.4±11.7 kg) assigned to single-dose oral MPH-MLR 80 mg capsule or sprinkles with applesauce, or Ritalin IR 25 mg (1×5 mg and 1×20 mg tablet) administered at 0, 4, and 8 hours. MPH-MLR 80 mg capsule and sprinkles were bioequivalent; ratios for maximum concentration (Cmax), area under plasma drug concentration versus time curve (AUC)0-t, and AUC0-inf were 1.04 (95% confidence interval [CI], 96.3-112.4), 0.99 (95% CI, 95.3-102.8), and 0.99 (95% CI, 95.4-103.0), respectively. MPH-MLR capsule/sprinkles produced highly comparable, biphasic profiles of plasma MPH concentrations characterized by rapid initial peak, followed by moderate decline until 5 hours postdose, and gradual increase until 7 hours postdose, culminating in an attenuated second peak. Based on 90% CIs, total systemic exposure to MPH-MLR 80 mg capsule/sprinkles was similar to that for Ritalin IR 25 mg three times daily, but marked differences in Cmax values indicated that MPH-MLR regimens were not bioequivalent to Ritalin. MPH Cmax and total systemic exposure over the first 4 hours postdose with MPH-MLR capsule/sprinkles was markedly higher than that associated with the first dose of Ritalin. All study drugs were safe and well tolerated. The PK modeling in adults suggested that differences in MPH pharmacokinetics between MPH-MLR and Ritalin are the result of dosage form design attributes and the associated absorption profiles of MPH. MPH-MLR 80 mg provides a long-acting biphasic pattern of plasma MPH concentrations with one less peak and trough than Ritalin IR.

Detection of Virus-Specific CD8+ T Cells With Cross-Reactivity Against Alloantigens: Potency and Flaws of Present Experimental Methods

PubMed Central

van den Heuvel, Heleen; Heutinck, Kirstin M.; van der Meer-Prins, Ellen P.M.W.; Yong, Si La; Claas, Frans H.J.; ten Berge, Ineke J.M.

2015-01-01

Background Virus-specific T cells have the intrinsic capacity to cross-react against allogeneic HLA antigens, a phenomenon known as heterologous immunity. In transplantation, these cells may contribute to the alloimmune response and negatively impact graft outcome. This study describes the various techniques that can be used to detect heterologous immune responses of virus-specific CD8+ T cells against allogeneic HLA antigens. The strengths and weaknesses of the different approaches are discussed and illustrated by experimental data. Methods Mixed-lymphocyte reactions (MLRs) were performed to detect allo-HLA cross-reactivity of virus-specific CD8+ T cells in total peripheral blood mononuclear cells. T-cell lines and clones were generated to confirm allo-HLA cross-reactivity by IFNγ production and cytotoxicity. In addition, the conventional MLR protocol was adjusted by introducing a 3-day resting phase and subsequent short restimulation with alloantigen or viral peptide, whereupon the expression of IFNγ, IL-2, CD107a, and CD137 was determined. Results The accuracy of conventional MLR is challenged by potential bystander activation. T-cell lines and clones can circumvent this issue, yet their generation is laborious and time-consuming. Using the adjusted MLR and restimulation protocol, we found that only truly cross-reactive T cells responded to re-encounter of alloantigen and viral peptide, whereas bystander-activated cells did not. Conclusions The introduction of a restimulation phase improved the accuracy of the MLR as a screening tool for the detection of allo-HLA cross-reactivity by virus-specific CD8+ T cells at bulk level. For detailed characterization of cross-reactive cells, T-cell lines and clones remain the golden standard. PMID:27500209

Prediction of Ba, Mn and Zn for tropical soils using iron oxides and magnetic susceptibility

NASA Astrophysics Data System (ADS)

Marques Júnior, José; Arantes Camargo, Livia; Reynaldo Ferracciú Alleoni, Luís; Tadeu Pereira, Gener; De Bortoli Teixeira, Daniel; Santos Rabelo de Souza Bahia, Angelica

2017-04-01

Agricultural activity is an important source of potentially toxic elements (PTEs) in soil worldwide but particularly in heavily farmed areas. Spatial distribution characterization of PTE contents in farming areas is crucial to assess further environmental impacts caused by soil contamination. Designing prediction models become quite useful to characterize the spatial variability of continuous variables, as it allows prediction of soil attributes that might be difficult to attain in a large number of samples through conventional methods. This study aimed to evaluate, in three geomorphic surfaces of Oxisols, the capacity for predicting PTEs (Ba, Mn, Zn) and their spatial variability using iron oxides and magnetic susceptibility (MS). Soil samples were collected from three geomorphic surfaces and analyzed for chemical, physical, mineralogical properties, as well as magnetic susceptibility (MS). PTE prediction models were calibrated by multiple linear regression (MLR). MLR calibration accuracy was evaluated using the coefficient of determination (R2). PTE spatial distribution maps were built using the values calculated by the calibrated models that reached the best accuracy by means of geostatistics. The high correlations between the attributes clay, MS, hematite (Hm), iron oxides extracted by sodium dithionite-citrate-bicarbonate (Fed), and iron oxides extracted using acid ammonium oxalate (Feo) with the elements Ba, Mn, and Zn enabled them to be selected as predictors for PTEs. Stepwise multiple linear regression showed that MS and Fed were the best PTE predictors individually, as they promoted no significant increase in R2 when two or more attributes were considered together. The MS-calibrated models for Ba, Mn, and Zn prediction exhibited R2 values of 0.88, 0.66, and 0.55, respectively. These are promising results since MS is a fast, cheap, and non-destructive tool, allowing the prediction of a large number of samples, which in turn enables detailed mapping of large areas. MS predicted values enabled the characterization and the understanding of spatial variability of the studied PTEs.

Commander Lousma adjusts MLR controls on middeck

NASA Image and Video Library

1982-03-30

STS003-22-127 (22-30 March 1982) --- Astronaut Jack R. Lousma, STS-3 commander, wearing communications kit assembly (assy) mini-headset, adjusts controls on Monodisperse Latex Reactor (MLR) experiment located in forward middeck lockers MF57H and MF57K. To reach MLR support electronics assy controls, Lousma squeezes in between forward lockers and Development Flight Instrument (DFI) unit on starboard bulkhead. Photo credit: NASA

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

The Analysis of Constitutions of Traditional Chinese Medicine in Relation to Cerebral Infarction in a Chinese Sample.

PubMed

Liu, Jiaqi; Xu, Fei; Mohammadtursun, Nabijan; Lv, Yubao; Tang, Zihui; Dong, Jingcheng

2018-05-01

To investigate the relationships between the constitutions of Traditional Chinese Medicine (TCM) and patients with cerebral infarction (CI) in a Chinese sample. A total of 3748 participants with complete data were available for data analysis. All study subjects underwent complete clinical baseline characteristics' evaluation, including a physical examination and response to a structured, nurse-assisted, self-administrated questionnaire. A population of 2010 neutral participants were used as the control group. Multiple variable regression (MLR) were employed to estimate the relationship between constitutions of TCM and the outcome. A cross-sectional study was conducted to evaluate the association of body constitution of TCM and CI. Communications and healthcare centers in Shanghai. A total of 3748 participants with complete data were available for data analysis. All study subjects underwent complete clinical baseline characteristics' evaluation, including a physical examination and response to a structured, nurse-assisted, self-administrated questionnaire. A population of 2010 neutral participants were used as the control group. MLR were employed to estimate the relationship between constitutions of TCM and the outcome. The prevalence of CI was 2.84% and 4.66% in neutral participants and yang-deficient participants (p = 0.012), respectively. Univariate analysis demonstrated a positive correlation between yang deficiency and CI. After adjustment for relevant potential confounding factors, the MLR detected significant associations between yang deficiency and CI (odds ratio = 1.44, p = 0.093). A yang-deficient constitution was significantly and independently associated with CI. A higher prevalence of CI was found in yang-deficient participants as compared with neutral participants.

Should studies of risk factors for musculoskeletal disorders be stratified by gender? Lessons from the 1998 Québec Health and Social Survey.

PubMed

Messing, Karen; Stock, Susan R; Tissot, France

2009-03-01

Several studies have reported male-female differences in the prevalence of symptoms of work-related musculoskeletal disorders (MSD), some arising from workplace exposure differences. The objective of this paper was to compare two strategies analyzing a single dataset for the relationships between risk factors and MSD in a population-based sample with a wide range of exposures. The 1998 Québec Health and Social Survey surveyed 11 735 respondents in paid work and reported "significant" musculoskeletal pain in 11 body regions during the previous 12 months and a range of personal, physical, and psychosocial risk factors. Five studies concerning risk factors for four musculoskeletal outcomes were carried out on these data. Each included analyses with multiple logistic regression (MLR) performed separately for women, men, and the total study population. The results from these gender-stratified and unstratified analyses were compared. In the unstratified MLR models, gender was significantly associated with musculoskeletal pain in the neck and lower extremities, but not with low-back pain. The gender-stratified MLR models identified significant associations between each specific musculoskeletal outcome and a variety of personal characteristics and physical and psychosocial workplace exposures for each gender. Most of the associations, if present for one gender, were also found in the total population. But several risk factors present for only one gender could be detected only in a stratified analysis, whereas the unstratified analysis added little information. Stratifying analyses by gender is necessary if a full range of associations between exposures and MSD is to be detected and understood.

Atom-type-based AI topological descriptors: application in structure-boiling point correlations of oxo organic compounds.

PubMed

Ren, Biye

2003-01-01

Structure-boiling point relationships are studied for a series of oxo organic compounds by means of multiple linear regression (MLR) analysis. Excellent MLR models based on the recently introduced Xu index and the atom-type-based AI indices are obtained for the two subsets containing respectively 77 ethers and 107 carbonyl compounds and a combined set of 184 oxo compounds. The best models are tested using the leave-one-out cross-validation and an external test set, respectively. The MLR model produces a correlation coefficient of r = 0.9977 and a standard error of s = 3.99 degrees C for the training set of 184 compounds, and r(cv) = 0.9974 and s(cv) = 4.16 degrees C for the cross-validation set, and r(pred) = 0.9949 and s(pred) = 4.38 degrees C for the prediction set of 21 compounds. For the two subsets containing respectively 77 ethers and 107 carbonyl compounds, the quality of the models is further improved. The standard errors are reduced to 3.30 and 3.02 degrees C, respectively. Furthermore, the results obtained from this study indicate that the boiling points of the studied oxo compound dominantly depend on molecular size and also depend on individual atom types, especially oxygen heteroatoms in molecules due to strong polar interactions between molecules. These excellent structure-boiling point models not only provide profound insights into the role of structural features in a molecule but also illustrate the usefulness of these indices in QSPR/QSAR modeling of complex compounds.

Factors affecting mortality in elderly patients who underwent surgery for gastric cancer.

PubMed

Kayılıoglu, Selami Ilgaz; Göktug, Ufuk Utku; Dinc, Tolga; Sozen, Isa; Yavuz, Zeynep; Coskun, Faruk

2018-03-05

The aim of this study was to determine factors affecting overall mortality in patients over 60 years of age who underwent surgery for gastric cancer in our clinic. Data on histopathological diagnosis (tumor size, lymph node status, and number), pathological stage, serum albumin level, tumor markers, complete blood count, and demographic information of 109 patients over 60 years of age who had surgery for gastric cancer between January 2011 and July 2016 were obtained retrospectively from the patient files. In addition, the survival status of all patients were examined and recorded. Metastatic lymph node ratio (MLR), red cell distribution width platelet ratio (RPR), neutrophil-lymphocyte ratio (NLR), plateletlymphocyte ratio (PLR), and prognostic nutritional index (PNI) were calculated. On univariate analysis of independent parameters, pathological LN number (p = 0.001), MLR (p <0.001), T3 (p = 0.001) or T4 (p = 0,006) tumor stage according to TNM system, the presence of metastasis (p = 0.063), and male gender (p = 0.066) were found to affect overall mortality (OM). On multivariable Cox regression analysis of these results, MLR (p = 0.005) and T stage (p = 0.006) was determined to be a statistically significant and independent prognostic value. In patients over 60 years of age who underwent surgery for gastric cancer, the factors affecting mortality were determined to be the presence of metastases, number of pathological lymph nodes, and male gender. Metastatic lymph node ratio and T1&T2 stage were determined to be independent prognostic factors. Elderly, Gastric cancer, Mortality, Prognostic factor.

[Transfer characteristic and source identification of soil heavy metals from water-level-fluctuating zone along Xiangxi River, three-Gorges Reservoir area].

PubMed

Xu, Tao; Wang, Fei; Guo, Qiang; Nie, Xiao-Qian; Huang, Ying-Ping; Chen, Jun

2014-04-01

Transfer characteristics of heavy metals and their evaluation of potential risk were studied based on determining concentration of heavy metal in soils from water-level-fluctuating zone (altitude:145-175 m) and bank (altitude: 175-185 m) along Xiangxi River, Three Gorges Reservoir area. Factor analysis-multiple linear regression (FA-MLR) was employed for heavy metal source identification and source apportionment. Results demonstrate that, during exposing season, the concentration of soil heavy metals in water-level-fluctuation zone and bank showed the variation, and the concentration of soil heavy metals reduced in shallow soil, but increased in deep soil at water-level-fluctuation zone. However, the concentration of soil heavy metals reduced in both shallow and deep soil at bank during the same period. According to the geoaccumulation index,the pollution extent of heavy metals followed the order: Cd > Pb > Cu > Cr, Cd is the primary pollutant. FA and FA-MLR reveal that in soils from water-level-fluctuation zone, 75.60% of Pb originates from traffic, 62.03% of Cd is from agriculture, 64.71% of Cu and 75.36% of Cr are from natural rock. In soils from bank, 82.26% of Pb originates from traffic, 68.63% of Cd is from agriculture, 65.72% of Cu and 69.33% of Cr are from natural rock. In conclusion, FA-MLR can successfully identify source of heavy metal and compute source apportionment of heavy metals, meanwhile the transfer characteristic is revealed. All these information can be a reference for heavy metal pollution control.

Event-based soil loss models for construction sites

NASA Astrophysics Data System (ADS)

Trenouth, William R.; Gharabaghi, Bahram

2015-05-01

The elevated rates of soil erosion stemming from land clearing and grading activities during urban development, can result in excessive amounts of eroded sediments entering waterways and causing harm to the biota living therein. However, construction site event-based soil loss simulations - required for reliable design of erosion and sediment controls - are one of the most uncertain types of hydrologic models. This study presents models with improved degree of accuracy to advance the design of erosion and sediment controls for construction sites. The new models are developed using multiple linear regression (MLR) on event-based permutations of the Universal Soil Loss Equation (USLE) and artificial neural networks (ANN). These models were developed using surface runoff monitoring datasets obtained from three sites - Greensborough, Cookstown, and Alcona - in Ontario and datasets mined from the literature for three additional sites - Treynor, Iowa, Coshocton, Ohio and Cordoba, Spain. The predictive MLR and ANN models can serve as both diagnostic and design tools for the effective sizing of erosion and sediment controls on active construction sites, and can be used for dynamic scenario forecasting when considering rapidly changing land use conditions during various phases of construction.

Source Apportionment and Risk Assessment of Emerging Contaminants: An Approach of Pharmaco-Signature in Water Systems

PubMed Central

Jiang, Jheng Jie; Lee, Chon Lin; Fang, Meng Der; Boyd, Kenneth G.; Gibb, Stuart W.

2015-01-01

This paper presents a methodology based on multivariate data analysis for characterizing potential source contributions of emerging contaminants (ECs) detected in 26 river water samples across multi-scape regions during dry and wet seasons. Based on this methodology, we unveil an approach toward potential source contributions of ECs, a concept we refer to as the “Pharmaco-signature.” Exploratory analysis of data points has been carried out by unsupervised pattern recognition (hierarchical cluster analysis, HCA) and receptor model (principal component analysis-multiple linear regression, PCA-MLR) in an attempt to demonstrate significant source contributions of ECs in different land-use zone. Robust cluster solutions grouped the database according to different EC profiles. PCA-MLR identified that 58.9% of the mean summed ECs were contributed by domestic impact, 9.7% by antibiotics application, and 31.4% by drug abuse. Diclofenac, ibuprofen, codeine, ampicillin, tetracycline, and erythromycin-H2O have significant pollution risk quotients (RQ>1), indicating potentially high risk to aquatic organisms in Taiwan. PMID:25874375

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

PubMed

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

Prediction of winter precipitation over northwest India using ocean heat fluxes

NASA Astrophysics Data System (ADS)

Nageswararao, M. M.; Mohanty, U. C.; Osuri, Krishna K.; Ramakrishna, S. S. V. S.

2016-10-01

The winter precipitation (December-February) over northwest India (NWI) is highly variable in terms of time and space. The maximum precipitation occurs over the Himalaya region and decreases towards south of NWI. The winter precipitation is important for water resources and agriculture sectors over the region and for the economy of the country. It is an exigent task to the scientific community to provide a seasonal outlook for the regional scale precipitation. The oceanic heat fluxes are known to have a strong linkage with the ocean and atmosphere. Henceforth, in this study, we obtained the relationship of NWI winter precipitation with total downward ocean heat fluxes at the global ocean surface, 15 regions with significant correlations are identified from August to November at 90 % confidence level. These strong relations encourage developing an empirical model for predicting winter precipitation over NWI. The multiple linear regression (MLR) and principal component regression (PCR) models are developed and evaluated using leave-one-out cross-validation. The developed regression models are able to predict the winter precipitation patterns over NWI with significant (99 % confidence level) index of agreement and correlations. Moreover, these models capture the signals of extremes, but could not reach the peaks (excess and deficit) of the observations. PCR performs better than MLR for predicting winter precipitation over NWI. Therefore, the total downward ocean heat fluxes at surface from August to November are having a significant impact on seasonal winter precipitation over the NWI. It concludes that these interrelationships are more useful for the development of empirical models and feasible to predict the winter precipitation over NWI with sufficient lead-time (in advance) for various risk management sectors.

Stimulation of the mesencephalic locomotor region for gait recovery after stroke.

PubMed

Fluri, Felix; Malzahn, Uwe; Homola, György A; Schuhmann, Michael K; Kleinschnitz, Christoph; Volkmann, Jens

2017-11-01

One-third of all stroke survivors are unable to walk, even after intensive physiotherapy. Thus, other concepts to restore walking are needed. Because electrical stimulation of the mesencephalic locomotor region (MLR) is known to elicit gait movements, this area might be a promising target for restorative neurostimulation in stroke patients with gait disability. The present study aims to delineate the effect of high-frequency stimulation of the MLR (MLR-HFS) on gait impairment in a rodent stroke model. Male Wistar rats underwent photothrombotic stroke of the right sensorimotor cortex and chronic implantation of a stimulating electrode into the right MLR. Gait was assessed using clinical scoring of the beam-walking test and video-kinematic analysis (CatWalk) at baseline and on days 3 and 4 after experimental stroke with and without MLR-HFS. Kinematic analysis revealed significant changes in several dynamic and static gait parameters resulting in overall reduced gait velocity. All rats exhibited major coordination deficits during the beam-walking challenge and were unable to cross the beam. Simultaneous to the onset of MLR-HFS, a significantly higher walking speed and improvements in several dynamic gait parameters were detected by the CatWalk system. Rats regained the ability to cross the beam unassisted, showing a reduced number of paw slips and misses. MLR-HFS can improve disordered locomotor function in a rodent stroke model. It may act by shielding brainstem and spinal locomotor centers from abnormal cortical input after stroke, thus allowing for compensatory and independent action of these circuits. Ann Neurol 2017;82:828-840. © 2017 American Neurological Association.

A comparison of the neutrophil-lymphocyte, platelet-lymphocyte and monocyte-lymphocyte ratios in schizophrenia and bipolar disorder patients - a retrospective file review.

PubMed

Özdin, Selçuk; Sarisoy, Gökhan; Böke, Ömer

2017-10-01

Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and monocyte-lymphocyte ratio (MLR) have recently been used as indicators of inflammation. Higher MLR and PLR values have been determined in the euthymic and manic periods in patients with bipolar disorder compared to a control group. High NLR values were determined in the only study investigating this ratio in schizophrenia patients. The purpose of this study was to compare NLR, PLR and MLR values and complete blood count elements in patients receiving treatment and hospitalized due to schizophrenic psychotic episode and bipolar disorder manic episode. All patients meeting the inclusion criteria among subjects receiving treatment and hospitalized due to schizophrenia-psychotic episode and bipolar affective disorder-manic episode at the Ondokuz Mayıs University Medical Faculty Psychiatry Department, Turkey, in 2012-2016 were included in our study. A total of 157 healthy donors were included as a control group. White blood cell (WBC), neutrophil, lymphocyte, platelet and monocyte numbers were noted retrospectively from complete blood counts at time of admission, and NLR, PLR and MLR were calculated from these. NLR, PLR and MLR values and platelet numbers in this study were higher and lymphocyte numbers were lower in bipolar disorder patients compared to the controls. Elevation in NLR, MLR and PLR values and neutrophil numbers and lower lymphocyte numbers were determined in schizophrenia patients compared to the controls. Higher NLR and MLR values were found in schizophrenia patients compared to bipolar disorder. Findings of our study supported the inflammation hypothesis for schizophrenia and bipolar disorder.

Lack of autologous mixed lymphocyte reaction in patients with chronic lymphocytic leukemia: evidence for autoreactive T-cell dysfunction not correlated with phenotype, karyotype, or clinical status

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, T.; Bloom, M.L.; Dadey, B.

In the present study, there was a complete lack of autologous MLR between responding T cells or T subsets and unirradiated or irradiated leukemic B cells or monocytes in all 20 patients with CLL, regardless of disease status, stage, phenotype, or karyotype of the disease. The stimulating capacity of unirradiated CLL B cells and CLL monocytes or irradiated CLL B cells was significantly depressed as compared to that of respective normal B cells and monocytes in allogeneic MLR. The responding capacity of CLL T cells was also variably lower than that of normal T cells against unirradiated or irradiated normalmore » allogeneic B cells and monocytes. The depressed allogeneic MLR between CLL B cells or CLL monocytes and normal T cells described in the present study could be explained on the basis of a defect in the stimulating antigens of leukemic B cells or monocytes. The decreased allogeneic MLR of CLL T cells might simply be explained by a defect in the responsiveness of T lymphocytes from patients with CLL. However, these speculations do not adequately explain the complete lack of autologous MLR in these patients. When irradiated CLL B cells or irradiated CLL T cells were cocultured with normal T cells and irradiated normal B cells, it was found that there was no suppressor cell activity of CLL B cells or CLL T cells on normal autologous MLR. Our data suggest that the absence or dysfunction of autoreactive T cells within the Tnon-gamma subset account for the lack of autologous MLR in patients with CLL. The possible significance of the autologous MLR, its relationship to in vivo immunoregulatory mechanisms, and the possible role of breakdown of autoimmunoregulation in the oncogenic process of certain lymphoproliferative and autoimmune diseases in man are discussed.« less

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

PubMed Central

2011-01-01

Background This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. Results Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. Conclusions Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR. PMID:22372859

Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine.

PubMed

Giovenzana, Valentina; Civelli, Raffaele; Beghi, Roberto; Oberti, Roberto; Guidetti, Riccardo

2015-11-01

The aim of this work was to test a simplified optical prototype for a rapid estimation of the ripening parameters of white grape for Franciacorta wine directly in field. Spectral acquisition based on reflectance at four wavelengths (630, 690, 750 and 850 nm) was proposed. The integration of a simple processing algorithm in the microcontroller software would allow to visualize real time values of spectral reflectance. Non-destructive analyses were carried out on 95 grape bunches for a total of 475 berries. Samplings were performed weekly during the last ripening stages. Optical measurements were carried out both using the simplified system and a portable commercial vis/NIR spectrophotometer, as reference instrument for performance comparison. Chemometric analyses were performed in order to extract the maximum useful information from optical data. Principal component analysis (PCA) was performed for a preliminary evaluation of the data. Correlations between the optical data matrix and ripening parameters (total soluble solids content, SSC; titratable acidity, TA) were carried out using partial least square (PLS) regression for spectra and using multiple linear regression (MLR) for data from the simplified device. Classification analysis were also performed with the aim of discriminate ripe and unripe samples. PCA, MLR and classification analyses show the effectiveness of the simplified system in separating samples among different sampling dates and in discriminating ripe from unripe samples. Finally, simple equations for SSC and TA prediction were calculated. Copyright © 2015 Elsevier B.V. All rights reserved.

Climate patterns as predictors of amphibians species richness and indicators of potential stress

USGS Publications Warehouse

Battaglin, W.; Hay, L.; McCabe, G.; Nanjappa, P.; Gallant, Alisa L.

2005-01-01

Amphibians occupy a range of habitats throughout the world, but species richness is greatest in regions with moist, warm climates. We modeled the statistical relations of anuran and urodele species richness with mean annual climate for the conterminous United States, and compared the strength of these relations at national and regional levels. Model variables were calculated for county and subcounty mapping units, and included 40-year (1960-1999) annual mean and mean annual climate statistics, mapping unit average elevation, mapping unit land area, and estimates of anuran and urodele species richness. Climate data were derived from more than 7,500 first-order and cooperative meteorological stations and were interpolated to the mapping units using multiple linear regression models. Anuran and urodele species richness were calculated from the United States Geological Survey's Amphibian Research and Monitoring Initiative (ARMI) National Atlas for Amphibian Distributions. The national multivariate linear regression (MLR) model of anuran species richness had an adjusted coefficient of determination (R2) value of 0.64 and the national MLR model for urodele species richness had an R2 value of 0.45. Stratifying the United States by coarse-resolution ecological regions provided models for anUrans that ranged in R2 values from 0.15 to 0.78. Regional models for urodeles had R2 values. ranging from 0.27 to 0.74. In general, regional models for anurans were more strongly influenced by temperature variables, whereas precipitation variables had a larger influence on urodele models.

Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

USGS Publications Warehouse

Snedden, Gregg A.; Steyer, Gregory D.

2013-01-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Indicators of science, technology, engineering, and math (STEM) career interest among middle school students in the USA

NASA Astrophysics Data System (ADS)

Mills, Leila A.

This study examines middle school students' perceptions of a future career in a science, math, engineering, or technology (STEM) career field. Gender, grade, predispositions to STEM contents, and learner dispositions are examined for changing perceptions and development in career-related choice behavior. Student perceptions as measured by validated measurement instruments are analyzed pre and post participation in a STEM intervention energy-monitoring program that was offered in several U.S. middle schools during the 2009-2010, 2010-2011 school years. A multiple linear regression (MLR) model, developed by incorporating predictors identified by an examination of the literature and a hypothesis-generating pilot study for prediction of STEM career interest, is introduced. Theories on the career choice development process from authors such as Ginzberg, Eccles, and Lent are examined as the basis for recognition of career concept development among students. Multiple linear regression statistics, correlation analysis, and analyses of means are used to examine student data from two separate program years. Study research questions focus on predictive ability, RSQ, of MLR models by gender/grade, and significance of model predictors in order to determine the most significant predictors of STEM career interest, and changes in students' perceptions pre and post program participation. Analysis revealed increases in the perceptions of a science career, decreases in perceptions of a STEM career, increase of the significance of science and mathematics to predictive models, and significant increases in students' perceptions of creative tendencies.

Liquid chromatography coupled to quadrupole-time of flight tandem mass spectrometry based quantitative structure-retention relationships of amino acid analogues derivatized via n-propyl chloroformate mediated reaction.

PubMed

Kritikos, Nikolaos; Tsantili-Kakoulidou, Anna; Loukas, Yannis L; Dotsikas, Yannis

2015-07-17

In the current study, quantitative structure-retention relationships (QSRR) were constructed based on data obtained by a LC-(ESI)-QTOF-MS/MS method for the determination of amino acid analogues, following their derivatization via chloroformate esters. Molecules were derivatized via n-propyl chloroformate/n-propanol mediated reaction. Derivatives were acquired through a liquid-liquid extraction procedure. Chromatographic separation is based on gradient elution using methanol/water mixtures from a 70/30% composition to an 85/15% final one, maintaining a constant rate of change. The group of examined molecules was diverse, including mainly α-amino acids, yet also β- and γ-amino acids, γ-amino acid analogues, decarboxylated and phosphorylated analogues and dipeptides. Projection to latent structures (PLS) method was selected for the formation of QSRRs, resulting in a total of three PLS models with high cross-validated coefficients of determination Q(2)Y. For this reason, molecular structures were previously described through the use of descriptors. Through stratified random sampling procedures, 57 compounds were split to a training set and a test set. Model creation was based on multiple criteria including principal component significance and eigenvalue, variable importance, form of residuals, etc. Validation was based on statistical metrics Rpred(2),QextF2(2),QextF3(2) for the test set and Roy's metrics rm(Av)(2) and rm(δ)(2), assessing both predictive stability and internal validity. Based on aforementioned models, simplified equivalent were then created using a multi-linear regression (MLR) method. MLR models were also validated with the same metrics. The suggested models are considered useful for the estimation of retention times of amino acid analogues for a series of applications. Copyright © 2015 Elsevier B.V. All rights reserved.

A Survey on Next-Generation Mixed Line Rate (MLR) and Energy-Driven Wavelength-Division Multiplexed (WDM) Optical Networks

NASA Astrophysics Data System (ADS)

Iyer, Sridhar

2015-06-01

With the ever-increasing traffic demands, infrastructure of the current 10 Gbps optical network needs to be enhanced. Further, since the energy crisis is gaining increasing concerns, new research topics need to be devised and technological solutions for energy conservation need to be investigated. In all-optical mixed line rate (MLR) network, feasibility of a lightpath is determined by the physical layer impairment (PLI) accumulation. Contrary to PLI-aware routing and wavelength assignment (PLIA-RWA) algorithm applicable for a 10 Gbps wavelength-division multiplexed (WDM) network, a new Routing, Wavelength, Modulation format assignment (RWMFA) algorithm is required for the MLR optical network. With the rapid growth of energy consumption in Information and Communication Technologies (ICT), recently, lot of attention is being devoted toward "green" ICT solutions. This article presents a review of different RWMFA (PLIA-RWA) algorithms for MLR networks, and surveys the most relevant research activities aimed at minimizing energy consumption in optical networks. In essence, this article presents a comprehensive and timely survey on a growing field of research, as it covers most aspects of MLR and energy-driven optical networks. Hence, the author aims at providing a comprehensive reference for the growing base of researchers who will work on MLR and energy-driven optical networks in the upcoming years. Finally, the article also identifies several open problems for future research.

Deviance-Related Responses along the Auditory Hierarchy: Combined FFR, MLR and MMN Evidence.

PubMed

Shiga, Tetsuya; Althen, Heike; Cornella, Miriam; Zarnowiec, Katarzyna; Yabe, Hirooki; Escera, Carles

2015-01-01

The mismatch negativity (MMN) provides a correlate of automatic auditory discrimination in human auditory cortex that is elicited in response to violation of any acoustic regularity. Recently, deviance-related responses were found at much earlier cortical processing stages as reflected by the middle latency response (MLR) of the auditory evoked potential, and even at the level of the auditory brainstem as reflected by the frequency following response (FFR). However, no study has reported deviance-related responses in the FFR, MLR and long latency response (LLR) concurrently in a single recording protocol. Amplitude-modulated (AM) sounds were presented to healthy human participants in a frequency oddball paradigm to investigate deviance-related responses along the auditory hierarchy in the ranges of FFR, MLR and LLR. AM frequency deviants modulated the FFR, the Na and Nb components of the MLR, and the LLR eliciting the MMN. These findings demonstrate that it is possible to elicit deviance-related responses at three different levels (FFR, MLR and LLR) in one single recording protocol, highlight the involvement of the whole auditory hierarchy in deviance detection and have implications for cognitive and clinical auditory neuroscience. Moreover, the present protocol provides a new research tool into clinical neuroscience so that the functional integrity of the auditory novelty system can now be tested as a whole in a range of clinical populations where the MMN was previously shown to be defective.

Deviance-Related Responses along the Auditory Hierarchy: Combined FFR, MLR and MMN Evidence

PubMed Central

Shiga, Tetsuya; Althen, Heike; Cornella, Miriam; Zarnowiec, Katarzyna; Yabe, Hirooki; Escera, Carles

2015-01-01

The mismatch negativity (MMN) provides a correlate of automatic auditory discrimination in human auditory cortex that is elicited in response to violation of any acoustic regularity. Recently, deviance-related responses were found at much earlier cortical processing stages as reflected by the middle latency response (MLR) of the auditory evoked potential, and even at the level of the auditory brainstem as reflected by the frequency following response (FFR). However, no study has reported deviance-related responses in the FFR, MLR and long latency response (LLR) concurrently in a single recording protocol. Amplitude-modulated (AM) sounds were presented to healthy human participants in a frequency oddball paradigm to investigate deviance-related responses along the auditory hierarchy in the ranges of FFR, MLR and LLR. AM frequency deviants modulated the FFR, the Na and Nb components of the MLR, and the LLR eliciting the MMN. These findings demonstrate that it is possible to elicit deviance-related responses at three different levels (FFR, MLR and LLR) in one single recording protocol, highlight the involvement of the whole auditory hierarchy in deviance detection and have implications for cognitive and clinical auditory neuroscience. Moreover, the present protocol provides a new research tool into clinical neuroscience so that the functional integrity of the auditory novelty system can now be tested as a whole in a range of clinical populations where the MMN was previously shown to be defective. PMID:26348628

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement.

PubMed

Giovenzana, Valentina; Beghi, Roberto; Parisi, Simone; Brancadoro, Lucio; Guidetti, Riccardo

2018-03-01

Increasing attention is being paid to non-destructive methods for water status real time monitoring as a potential solution to replace the tedious conventional techniques which are time consuming and not easy to perform directly in the field. The objective of this study was to test the potential effectiveness of two portable optical devices (visible/near infrared (vis/NIR) and near infrared (NIR) spectrophotometers) for the rapid and non-destructive evaluation of the water status of grapevine leaves. Moreover, a variable selection methodology was proposed to determine a set of candidate variables for the prediction of water potential (Ψ, MPa) related to leaf water status in view of a simplified optical device. The statistics of the partial least square (PLS) models showed in validation R 2 between 0.67 and 0.77 for models arising from vis/NIR spectra, and R 2 ranged from 0.77 to 0.85 for the NIR region. The overall performance of the multiple linear regression (MLR) models from selected wavelengths was slightly worse than that of the PLS models. Regarding the NIR range, acceptable MLR models were obtained only using 14 effective variables (R 2 range 0.63-0.69). To address the market demand for portable optical devices and heading towards the trend of miniaturization and low cost of the devices, individual wavelengths could be useful for the design of a simplified and low-cost handheld system providing useful information for better irrigation scheduling. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Abstinence Rates among College Cigarette Smokers Enrolled in A Randomized Clinical Trial Evaluating Quit and Win Contests: The Impact of Concurrent Hookah Use

PubMed Central

Bengtson, J.E.; Wang, Q.; Luo, X.; Marigi, Erick; Winta, Ghidei; Ahluwalia, J.S.

2015-01-01

Objective To examine baseline characteristics and biochemically verified 1-, 4-, and 6-month tobacco quit rates among college students enrolled in a Quit and Win cessation trial, comparing those who concurrently smoke both hookah and cigarettes with those who deny hookah use. Methods Analyses were conducted on data from 1,217 college students enrolled in a Quit and Win tobacco cessation randomized clinical trial from 2010–2012. Multivariable logistic regression (MLR) analyses examined group differences in baseline characteristics and cotinine verified 30-day abstinence at 1, 4, and 6-month follow-up, adjusting for baseline covariates. Results Participants smoked 11.5(±8.1) cigarettes per day on 28.5(±3.8) days/month, and 22% smoked hookah in the past 30 days. Hookah smokers (n=270) were more likely to be male (p<0.0001), younger (p<0.0001), report more binge drinking (p<0.0001) and score higher on impulsivity (p<0.001). MLR results indicate that hookah users, when compared to non-users, had a 36% decrease in odds of self-reported 30-day abstinence at 4-months (OR= 0.64, 95% CI=0.45–0.93, p=0.02) and a 63% decrease in odds in biochemically verified continuous abstinence at 6-months (OR = 0.37, CI=0.14–0.99, p=0.05). Conclusion College cigarette smokers who concurrently use hookah display several health risk factors and demonstrate lower short and long-term tobacco abstinence rates. PMID:25773472

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

DOE PAGES

Li, Muyang; Williams, Daniel L.; Heckwolf, Marlies; ...

2016-10-04

In this paper, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These weremore » compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.« less

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Muyang; Williams, Daniel L.; Heckwolf, Marlies

In this paper, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These weremore » compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.« less

Prediction of oxidation parameters of purified Kilka fish oil including gallic acid and methyl gallate by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network.

PubMed

Asnaashari, Maryam; Farhoosh, Reza; Farahmandfar, Reza

2016-10-01

As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR). The results showed ANFIS was the best model with high coefficient of determination (R(2)  = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models. Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Influence of the Bermuda High on interannual variability of summertime ozone in the Houston-Galveston-Brazoria region

NASA Astrophysics Data System (ADS)

Wang, Yuxuan; Jia, Beixi; Wang, Sing-Chun; Estes, Mark; Shen, Lu; Xie, Yuanyu

2016-12-01

The Bermuda High (BH) quasi-permanent pressure system is the key large-scale circulation pattern influencing summertime weather over the eastern and southern US. Here we developed a multiple linear regression (MLR) model to characterize the effect of the BH on year-to-year changes in monthly-mean maximum daily 8 h average (MDA8) ozone in the Houston-Galveston-Brazoria (HGB) metropolitan region during June, July, and August (JJA). The BH indicators include the longitude of the BH western edge (BH-Lon) and the BH intensity index (BHI) defined as the pressure gradient along its western edge. Both BH-Lon and BHI are selected by MLR as significant predictors (p < 0.05) of the interannual (1990-2015) variability of the HGB-mean ozone throughout JJA, while local-scale meridional wind speed is selected as an additional predictor for August only. Local-scale temperature and zonal wind speed are not identified as important factors for any summer month. The best-fit MLR model can explain 61-72 % of the interannual variability of the HGB-mean summertime ozone over 1990-2015 and shows good performance in cross-validation (R2 higher than 0.48). The BH-Lon is the most important factor, which alone explains 38-48 % of such variability. The location and strength of the Bermuda High appears to control whether or not low-ozone maritime air from the Gulf of Mexico can enter southeastern Texas and affect air quality. This mechanism also applies to other coastal urban regions along the Gulf Coast (e.g., New Orleans, LA, Mobile, AL, and Pensacola, FL), suggesting that the BH circulation pattern can affect surface ozone variability through a large portion of the Gulf Coast.

Flood Identification from Satellite Images Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Chang, L.; Kao, I.; Shih, K.

2011-12-01

Typhoons and storms hit Taiwan several times every year and they cause serious flood disasters. Because the rivers are short and steep, and their flows are relatively fast with floods lasting only few hours and usually less than one day. Flood identification can provide the flood disaster and extent information to disaster assistance and recovery centers. Due to the factors of the weather, it is not suitable for aircraft or traditional multispectral satellite; hence, the most appropriate way for investigating flooding extent is to use Synthetic Aperture Radar (SAR) satellite. In this study, back-propagation neural network (BPNN) model and multivariate linear regression (MLR) model are built to identify the flooding extent from SAR satellite images. The input variables of the BPNN model are Radar Cross Section (RCS) value and mean of the pixel, standard deviation, minimum and maximum of RCS values among its adjacent 3×3 pixels. The MLR model uses two images of the non-flooding and flooding periods, and The inputs are the difference between the RCS values of two images and the variances among its adjacent 3×3 pixels. The results show that the BPNN model can perform much better than the MLR model. The correct percentages are more than 80% and 73% in training and testing data, respectively. Many misidentified areas are very fragmented and unrelated. In order to reinforce the correct percentage, morphological image analysis is used to modify the outputs of these identification models. Through morphological operations, most of the small, fragmented and misidentified areas can be correctly assigned to flooding or non-flooding areas. The final results show that the flood identification of satellite images has been improved a lot and the correct percentages increases up to more than 90%.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

PubMed

Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

2006-11-30

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

PubMed Central

Sachindra, D. A.; Perera, B. J. C.

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). PMID:27997609

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships.

PubMed

Sachindra, D A; Perera, B J C

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950-2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950-2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950-69, 1970-89 and 1990-99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).

A hybrid algorithm for selecting head-related transfer function based on similarity of anthropometric structures

NASA Astrophysics Data System (ADS)

Zeng, Xiang-Yang; Wang, Shu-Guang; Gao, Li-Ping

2010-09-01

As the basic data for virtual auditory technology, head-related transfer function (HRTF) has many applications in the areas of room acoustic modeling, spatial hearing and multimedia. How to individualize HRTF fast and effectively has become an opening problem at present. Based on the similarity and relativity of anthropometric structures, a hybrid HRTF customization algorithm, which has combined the method of principal component analysis (PCA), multiple linear regression (MLR) and database matching (DM), has been presented in this paper. The HRTFs selected by both the best match and the worst match have been applied into obtaining binaurally auralized sounds, which are then used for subjective listening experiments and the results are compared. For the area in the horizontal plane, the localization results have shown that the selection of HRTFs can enhance the localization accuracy and can also abate the problem of front-back confusion.

In-line moisture monitoring in fluidized bed granulation using a novel multi-resonance microwave sensor.

PubMed

Peters, Johanna; Bartscher, Kathrin; Döscher, Claas; Taute, Wolfgang; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg

2017-08-01

Microwave resonance technology (MRT) is known as a process analytical technology (PAT) tool for moisture measurements in fluid-bed granulation. It offers a great potential for wet granulation processes even where the suitability of near-infrared (NIR) spectroscopy is limited, e.g. colored granules, large variations in bulk density. However, previous sensor systems operating around a single resonance frequency showed limitations above approx. 7.5% granule moisture. This paper describes the application of a novel sensor working with four resonance frequencies. In-line data of all four resonance frequencies were collected and further processed. Based on calculation of density-independent microwave moisture values multiple linear regression (MLR) models using Karl-Fischer titration (KF) as well as loss on drying (LOD) as reference methods were build. Rapid, reliable in-process moisture control (RMSEP≤0.5%) even at higher moisture contents was achieved. Copyright © 2017 Elsevier B.V. All rights reserved.

Liquid electrolyte informatics using an exhaustive search with linear regression.

PubMed

Sodeyama, Keitaro; Igarashi, Yasuhiko; Nakayama, Tomofumi; Tateyama, Yoshitaka; Okada, Masato

2018-06-14

Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the "prediction accuracy" and "calculation cost" of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the "accuracy" and "cost" when the search of a huge amount of new materials was carried out.

LFP Oscillations in the Mesencephalic Locomotor Region during Voluntary Locomotion

PubMed Central

Noga, Brian R.; Sanchez, Francisco J.; Villamil, Luz M.; O’Toole, Christopher; Kasicki, Stefan; Olszewski, Maciej; Cabaj, Anna M.; Majczyński, Henryk; Sławińska, Urszula; Jordan, Larry M.

2017-01-01

Oscillatory rhythms in local field potentials (LFPs) are thought to coherently bind cooperating neuronal ensembles to produce behaviors, including locomotion. LFPs recorded from sites that trigger locomotion have been used as a basis for identification of appropriate targets for deep brain stimulation (DBS) to enhance locomotor recovery in patients with gait disorders. Theta band activity (6–12 Hz) is associated with locomotor activity in locomotion-inducing sites in the hypothalamus and in the hippocampus, but the LFPs that occur in the functionally defined mesencephalic locomotor region (MLR) during locomotion have not been determined. Here we record the oscillatory activity during treadmill locomotion in MLR sites effective for inducing locomotion with electrical stimulation in rats. The results show the presence of oscillatory theta rhythms in the LFPs recorded from the most effective MLR stimulus sites (at threshold ≤60 μA). Theta activity increased at the onset of locomotion, and its power was correlated with the speed of locomotion. In animals with higher thresholds (>60 μA), the correlation between locomotor speed and theta LFP oscillations was less robust. Changes in the gamma band (previously recorded in vitro in the pedunculopontine nucleus (PPN), thought to be a part of the MLR) were relatively small. Controlled locomotion was best achieved at 10–20 Hz frequencies of MLR stimulation. Our results indicate that theta and not delta or gamma band oscillation is a suitable biomarker for identifying the functional MLR sites. PMID:28579945

A robust empirical seasonal prediction of winter NAO and surface climate.

PubMed

Wang, L; Ting, M; Kushner, P J

2017-03-21

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.

77 FR 28790 - Medical Loss Ratio Requirements Under the Patient Protection and Affordable Care Act

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-16

... information will be available on the HHS Web site, HealthCare.gov , providing an efficient method of public... Sources, Methods, and Limitations On December 1, 2010, we published an interim final rule (75 FR 74864... impacts of the MLR rule, the data contain certain limitations; we developed imputation methods to account...

A new spectrophotometric method for determination of EDTA in water using its complex with Mn(III)

NASA Astrophysics Data System (ADS)

Andrade, Carlos Eduardo O.; Oliveira, André F.; Neves, Antônio A.; Queiroz, Maria Eliana L. R.

2016-11-01

EDTA is an important ligand used in many industrial products as well as in agriculture, where it is employed to assist in phytoextraction procedures and the absorption of nutrients by plants. Due to its intensive use and recalcitrance, it is now considered an emerging pollutant in water, so there is great interest in techniques suitable for its monitoring. This work proposes a method based on formation of the Mn(III)-EDTA complex after oxidation of the Mn(II)-EDTA complex by PbO2 immobilized on cyanoacrylate spheres. A design of experiments (DOE) based on the Doehlert matrix was used to determine the optimum conditions of the method, and the influence of the variables was evaluated using a multiple linear regression (MLR) model. The optimized method presented a linear response in the range from 0.77 to 100.0 μmol L- 1, with analytical sensitivity of 7.7 × 103 L mol- 1, a coefficient of determination of 0.999, and a limit of detection of 0.23 μmol L- 1. The method was applied using samples fortified at different concentration levels, and the recoveries achieved were between 97.0 and 104.9%.

Medical loss ratio as a potential regulatory tool in the Israeli healthcare system.

PubMed

Simon-Tuval, Tzahit; Horev, Tuvia; Kaplan, Giora

2015-01-01

The growth of the private health insurance sector in Western countries, which is characterized by information deficiencies and limited competition, necessitates the implementation of effective regulatory tools. One measure which is widely used is the medical loss ratio (MLR). Our objective was to analyze how MLR is applied as a regulatory measure in the Israeli voluntary health insurance (VHI) market in order to promote the protection of beneficiaries. The study will examine MLR values and the use of this tool by regulators of VHI in Israel. Descriptive analysis using 2005-2012 data from public reports of the Ministry of Health and the Ministry of Finance on VHI plans in three market segments: nonprofit health plans, group (collective) policies offered by commercial insurance companies and individual policies offered by commercial insurance companies. In 2012, 74% of the Israeli population owned VHI provided by nonprofit health plans and 43% owned VHI offered by for-profit commercial companies. At that time the MLRs of three nonprofit health plans were significantly lower than 80%, mostly in the upper layers of coverage. The MLR in the individual commercial segment was consistently low (38% in 2012). The use of MLR as a regulation tool was, and continues to be, relatively limited in all segments. The VHI in Israel covers several essential services that are not covered by the statutory benefits package as a result of budget constraints. Thus, due to the high penetration rate of VHI in Israel compared to European countries and the lower levels of MLR, in order to assure the protection of beneficiaries it may be warranted to increase the extent of regulation and adjust it to the nature of the services covered. This may include distinguishing between essential and nonessential coverages and implementation of the most suitable regulatory measures (such as an MLR threshold, limitation of services covered and adjusting the actuarial models to the beneficiaries' behavior), rather than focusing only on assuring solvency.

The Mass-Luminosity-Metallicity Relation for M Dwarfs

NASA Astrophysics Data System (ADS)

Mann, Andrew; Dupuy, Trent; Rizzuto, Aaron; Kraus, Adam; Gaidos, Eric; Ansdell, Megan

2018-01-01

One of the most powerful tools for stellar characterization is the mass-luminosity relation (MLR). In addition to its use for characterizing exoplanet hosts, the MLR for late-type stars is critical to measuring the stellar IMF, testing isochrones, and studies of Galactic archeology. However, existing MLRs do not fully account for metallicity effects, do not extend down to the substellar boundary, and are not precise enough to take full advantage of the impending arrival of Gaia parallaxes for millions of late-type stars. For two years we monitored 72 nearby M-dwarf astrometric binaries using adaptive optics and non-redundant aperture masking, with the goal of better constraining the MLR. We combined our astrometry with measurements from the literature and Keck archive to measure orbits, masses, and flux ratios of all binaries in JHK bands. In parallel, we obtained moderate-resolution NIR spectra of all binaries, from which we determine empirical metallicities for each system. We derived an updated MLR-metallicity relation that spans most of the M dwarf sequence (K5 to M7) and the metallicity range expected in the solar neighborhood (-0.5 < [M/H] +0.4). With this we explored the role metallicity plays in the MLR. With our revised relation and Gaia-precision parallaxes, it will soon be possible to calculate empirical masses of nearby M dwarfs to better than 2%, and future studies will enable us to extend our relation to more metal-poor stars and explore the role of youth and evolution of the MLR for M dwarfs.

Elevated dry-season malaria prevalence associated with fine-scale spatial patterns of environmental risk: a case-control study of children in rural Malawi.

PubMed

Townes, Lindsay R; Mwandama, Dyson; Mathanga, Don P; Wilson, Mark L

2013-11-11

Understanding the role of local environmental risk factors for malaria in holo-endemic, poverty-stricken settings will be critical to more effectively implement- interventions aimed at eventual elimination. Household-level environmental drivers of malaria risk during the dry season were investigated in rural southern Malawi among children < five years old in two neighbouring rural Traditional Authority (TA) regions dominated by small-scale agriculture. Ten villages were randomly selected from TA Sitola (n = 6) and Nsamala (n = 4). Within each village, during June to August 2011, a census was conducted of all households with children under-five and recorded their locations with a geographic position system (GPS) device. At each participating house, a nurse administered a malaria rapid diagnostic test (RDT) to children under five years of age, and a questionnaire to parents. Environmental data were collected for each house, including land cover within 50-m radius. Variables found to be significantly associated with P. falciparum infection status in bivariate analysis were included in generalized linear models, including multivariate logistic regression (MLR) and multi-level multivariate logistic regression (MLLR). Spatial clustering of RDT status, environmental factors, and Pearson residuals from MLR and MLLR were analysed using the Getis-Ord Gi* statistic. Of 390 children enrolled from six villages in Sitola (n = 162) and four villages in Nsamala (n = 228), 45.6% tested positive (n = 178) for Plasmodium infection by RDT. The MLLR modelled the statistical relationship of Plasmodium positives and household proximity to agriculture (<25-m radius), controlling for the child sex and age (in months), bed net ownership, elevation, and random effects intercepts for village and TA-level unmeasured factors. After controlling for area affects in MLLR, proximity to active agriculture remained a significant predictor of positive RDT result (OR 2.80, 95% CI 1.41-5.55). Mapping of Pearson residuals from MLR showed significant clustering (Gi* z > 2.58, p < 0.01) predominantly within TA Sitola, while residuals from MLLR showed no such clustering. This study provides evidence for significant, dry-season heterogeneity of malaria prevalence strongly linked to peridomestic land use, and particularly of elevated risk associated with nearby crop production.

Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

PubMed

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-10-23

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

PubMed Central

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-01-01

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector. PMID:25341439

Highly sensitive current sensor based on an optical microfiber loop resonator incorporating low index polymer

NASA Astrophysics Data System (ADS)

Yoon, Min-Seok; Han, Young-Geun

2014-05-01

A highly sensitive current sensor based on an optical microfiber loop resonator (MLR) incorporating low index polymer is proposed and experimentally demonstrated. The microfiber with a waist diameter of 1 μm is wrapped around the nicrhrome wire with low index polymer coating and the optical MLR is realized. The use of the microfiber and low index polymer with high thermal property can effectively improve the current sensitivity of the proposed MLR-based sensing probe to be 437.9 pm/A2, which is ~10 times higher than the previous result.

A transgenic animal model of osmotic cataract. Part 1: over-expression of bovine Na+/myo-inositol cotransporter in lens fibers.

PubMed

Cammarata, P R; Zhou, C; Chen, G; Singh, I; Reeves, R E; Kuszak, J R; Robinson, M L

1999-07-01

Intracellular osmotic stress is believed to be linked to the advancement of diabetic cataract. Although the accumulation of organic osmolytes (myo-inositol, sorbitol, taurine) is thought to protect the lens by maintaining osmotic homeostasis, the physiologic implication of osmotic imbalance (i.e., hyperosmotic stress caused by intracellular over-accumulation of organic osmolytes) on diabetic cataract formation is not clearly understood. Studies from this laboratory have identified several osmotic compensatory mechanisms thought to afford the lens epithelium, but not the lens fibers, protection from water stress during intervals of osmotic crisis. This model is founded on the supposition that the fibers of the lens are comparatively more susceptible to damage by osmotic insult than is the lens epithelium. To test this premise, several transgenic mouse lines were developed that over-express the bovine sodium/myo-inositol cotransporter (bSMIT) gene in lens fiber cells. Of the several transgenic mouse lines generated, two, MLR14 and MLR21, were analyzed in detail. Transgenic mRNA expression was analyzed in adult and embryonic transgenic mice by a coupled reverse transcriptase-polymerase chain reaction (RT-PCR) and in situ hybridization on embryonic tissue sections, respectively. Intralenticular myo-inositol content from individual mouse lenses was quantified by anion exchange chromatography and pulsed electrochemical detection. Ocular histology of embryonic day 15.5 (E15.5) embryos from both transgenic (TG) families was analyzed and compared to their respective nontransgenic (NTG) littermates. Both RT-PCR and in situ hybridization determined that transgene expression was higher in line MLR21 than in line MLR14. Consistent with this, intralenticular myo-inositol from MLR21 TG mice was markedly higher compared with NTG littermates or MLR14 TG mice. Histologic analysis of E15.5 MLR21 TG embryos disclosed a marked swelling in the differentiating fibers of the bow region and subcapsular fibers of the central zone, whereas the lens epithelium appeared morphologically normal. The lenticular changes, initiated early during lens development in TG MLR21 embryos, result in severe bilateral nuclear cataracts readily observable in neonates under normal rearing and dietary conditions. In contrast, TG MLR14 pups reared under standard conditions produced no lens opacity. Lens fiber swelling and related cataractous outgrowth positively correlated to the degree of lens bSMIT gene expression and intralenticular myo-inositol content. The affected (i.e., swollen) lens fibers appeared to be unable to cope with the water stress generated by the transgene-induced over-accumulation of myo-inositol and, as a result of this inability to osmoregulate, suffered osmotic damage due to water influx.

Source contributions to total concentrations and carcinogenic potencies of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) in ambient air: a case study in Suzhou City, China.

PubMed

Xuan, Zhiqiang; Bi, Chenglu; Li, Jiafu; Nie, Jihua; Chen, Zhihai

2017-10-01

The potential source categories and source contributions of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) in ambient air from Suzhou City, China, were performed by principal component analysis-multiple linear regression (PCA-MLR) and positive matrix factorization (PMF). The carcinogenic potencies of PCDD/Fs were quantitatively apportioned based on the positive matrix factorization-toxic equivalent concentration (PMF-TEQ) method. The results of the present study were summarized as follows. (1) The total concentrations and toxic equivalent concentrations of PCDD/Fs (∑PCDD/Fs and TEQ) in ambient air from Suzhou City were 1.34-42.80 pg N m -3 and 0.081-1.22 pg I-TEQ N m -3 , respectively. (2) PCA-MLR suggested that industrial combustion (IC), electric arc furnaces (EAFs) and secondary aluminum smelters (ALSs), unleaded gas-fueled vehicle sources (UGFVs), ALSs, and hazardous solid waste incinerators (HSWIs) could be the primary PCDD/F contributors, accounting for 13.2, 16.7, 35.5, 19.4, and 15.2% of ∑PCDD/Fs, respectively. (3) PMF and PMF-TEQ indicated that EAFs (carbon steel), UGFVs, IC, ALSs, municipal solid waste incinerators (MSWIs) and hospital waste incinerators (HWIs), and HSWIs contributed 10.9, 10.9, 42.8, 11.3, 10.7, and 13.4% to ∑PCDD/Fs, but contributed 8.3, 12.3, 50.3, 12.7, 6.0, and 10.4% to carcinogenic potencies of PCDD/Fs. This study was the first attempt to quantitatively apportion the source-specific carcinogenic potencies of PCDD/Fs in ambient air.

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

Characterization of skin reactions and pain reported by patients receiving radiation therapy for cancer at different sites

PubMed Central

Gewandter, Jennifer S.; Walker, Joanna; Heckler, Charles E.; Morrow, Gary R.; Ryan, Julie L.

2015-01-01

Background Skin reactions and pain are commonly reported side effects of radiation therapy (RT). Objective To characterize RT-induced symptoms according to treatment site subgroups and identify skin symptoms that correlate with pain. Methods A self-report survey, adapted from the MD Anderson Symptom Inventory and the McGill Pain Questionnaire, assessed RT-induced skin problems, pain, and specific skin symptoms. Wilcoxon Sign Ranked tests compared mean severity of pre- and post-RT pain and skin problems within each RT-site subgroup. Multiple linear regression (MLR) investigated associations between skin symptoms and pain. Results Survey respondents (n=106) were 58% female and on average 64 years old. RT sites included lung, breast, lower abdomen, head/neck/brain, and upper abdomen. Only patients receiving breast RT reported significant increases in treatment site pain and skin problems (p≤0.007). Patients receiving head/neck/brain RT reported increased skin problems (p<0.0009). MLR showed that post-RT skin tenderness and tightness were most strongly associated with post-RT pain (p=0.066 and p=0.122, respectively). Limitations Small sample size, exploratory analyses, and non-validated measure. Conclusions Only patients receiving breast RT reported significant increases in pain and skin problems at the RT site, while patients receiving head/neck/brain RT had increased skin problems, but not pain. These findings suggest that the severity of skin problems is not the only factor that contributes to pain, and interventions should be tailored to specifically target pain at the RT site, possibly by targeting tenderness and tightness. These findings should be confirmed in a larger sampling of RT patients. PMID:24645338

Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks

PubMed Central

Caywood, Matthew S.; Roberts, Daniel M.; Colombe, Jeffrey B.; Greenwald, Hal S.; Weiland, Monica Z.

2017-01-01

There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as “black boxes” that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model’s predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy. PMID:28123359

Comparison of the prognostic value of pretreatment measurements of systemic inflammatory response in patients undergoing curative resection of clear cell renal cell carcinoma.

PubMed

Lucca, Ilaria; de Martino, Michela; Hofbauer, Sebastian L; Zamani, Nura; Shariat, Shahrokh F; Klatte, Tobias

2015-12-01

Pretreatment measurements of systemic inflammatory response, including the Glasgow prognostic score (GPS), the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR) and the prognostic nutritional index (PNI) have been recognized as prognostic factors in clear cell renal cell carcinoma (CCRCC), but there is at present no study that compared these markers. We evaluated the pretreatment GPS, NLR, MLR, PLR and PNI in 430 patients, who underwent surgery for clinically localized CCRCC (pT1-3N0M0). Associations with disease-free survival were assessed with Cox models. Discrimination was measured with the C-index, and a decision curve analysis was used to evaluate the clinical net benefit. On multivariable analyses, all measures of systemic inflammatory response were significant prognostic factors. The increase in discrimination compared with the stage, size, grade and necrosis (SSIGN) score alone was 5.8 % for the GPS, 1.1-1.4 % for the NLR, 2.9-3.4 % for the MLR, 2.0-3.3 % for the PLR and 1.4-3.0 % for the PNI. On the simultaneous multivariable analysis of all candidate measures, the final multivariable model contained the SSIGN score (HR 1.40, P < 0.001), the GPS (HR 2.32, P < 0.001) and the MLR (HR 5.78, P = 0.003) as significant variables. Adding both the GPS and the MLR increased the discrimination of the SSIGN score by 6.2 % and improved the clinical net benefit. In patients with clinically localized CCRCC, the GPS and the MLR appear to be the most relevant prognostic measures of systemic inflammatory response. They may be used as an adjunct for patient counseling, tailoring management and clinical trial design.

Models based on ultraviolet spectroscopy, polyphenols, oligosaccharides and polysaccharides for prediction of wine astringency.

PubMed

Boulet, Jean-Claude; Trarieux, Corinne; Souquet, Jean-Marc; Ducasse, Maris-Agnés; Caillé, Soline; Samson, Alain; Williams, Pascale; Doco, Thierry; Cheynier, Véronique

2016-01-01

Astringency elicited by tannins is usually assessed by tasting. Alternative methods involving tannin precipitation have been proposed, but they remain time-consuming. Our goal was to propose a faster method and investigate the links between wine composition and astringency. Red wines covering a wide range of astringency intensities, assessed by sensory analysis, were selected. Prediction models based on multiple linear regression (MLR) were built using UV spectrophotometry (190-400 nm) and chemical analysis (enological analysis, polyphenols, oligosaccharides and polysaccharides). Astringency intensity was strongly correlated (R(2) = 0.825) with tannin precipitation by bovine serum albumin (BSA). Wine absorbances at 230 nm (A230) proved more suitable for astringency prediction (R(2) = 0.705) than A280 (R(2) = 0.56) or tannin concentration estimated by phloroglucinolysis (R(2) = 0.59). Three variable models built with A230, oligosaccharides and polysaccharides presented high R(2) and low errors of cross-validation. These models confirmed that polysaccharides decrease astringency perception and indicated a positive relationship between oligosaccharides and astringency. Copyright © 2015 Elsevier Ltd. All rights reserved.

Determination and importance of temperature dependence of retention coefficient (RPHPLC) in QSAR model of nitrazepams' partition coefficient in bile acid micelles.

PubMed

Posa, Mihalj; Pilipović, Ana; Lalić, Mladena; Popović, Jovan

2011-02-15

Linear dependence between temperature (t) and retention coefficient (k, reversed phase HPLC) of bile acids is obtained. Parameters (a, intercept and b, slope) of the linear function k=f(t) highly correlate with bile acids' structures. Investigated bile acids form linear congeneric groups on a principal component (calculated from k=f(t)) score plot that are in accordance with conformations of the hydroxyl and oxo groups in a bile acid steroid skeleton. Partition coefficient (K(p)) of nitrazepam in bile acids' micelles is investigated. Nitrazepam molecules incorporated in micelles show modified bioavailability (depo effect, higher permeability, etc.). Using multiple linear regression method QSAR models of nitrazepams' partition coefficient, K(p) are derived on the temperatures of 25°C and 37°C. For deriving linear regression models on both temperatures experimentally obtained lipophilicity parameters are included (PC1 from data k=f(t)) and in silico descriptors of the shape of a molecule while on the higher temperature molecular polarisation is introduced. This indicates the fact that the incorporation mechanism of nitrazepam in BA micelles changes on the higher temperatures. QSAR models are derived using partial least squares method as well. Experimental parameters k=f(t) are shown to be significant predictive variables. Both QSAR models are validated using cross validation and internal validation method. PLS models have slightly higher predictive capability than MLR models. Copyright © 2010 Elsevier B.V. All rights reserved.

Evaluation of epidural analgesia for open major liver resection surgery from a US inpatient sample.

PubMed

Rosero, Eric B; Cheng, Gloria S; Khatri, Kinnari P; Joshi, Girish P

2014-10-01

The aim of this study was to assess the nationwide use of epidural analgesia (EA) and the incidence of postoperative complications in patients undergoing major liver resections (MLR) with and without EA in the United States. The 2001 to 2010 Nationwide Inpatient Sample was queried to identify adult patients undergoing MLR. A 1:1 matched cohort of patients having MLR with and without EA was assembled using propensity-score matching techniques. Differences in the rate of postoperative complications were compared between the matched groups. We identified 68,028 MLR. Overall, 5.9% of patients in the database had procedural codes for postoperative EA. A matched cohort of 802 patients per group was derived from the propensity-matching algorithm. Although use of EA was associated with more blood transfusions (relative risk, 1.36; 95% confidence interval, 1.12-1.65; P = 0.001) and longer hospital stay (median [interquartile range], 6 [5-8] vs 6 [4-8] days), the use of coagulation factors and the incidence of postoperative hemorrhage/hematomas or other postoperative complications were not higher in patients receiving EA. In conclusion, the use of EA for MLR is low, and EA does not seem to influence the incidence of postoperative complications. EA, however, was associated with an increased use of blood transfusions and a longer hospital stay.

Evaluation of epidural analgesia for open major liver resection surgery from a US inpatient sample

PubMed Central

Cheng, Gloria S.; Khatri, Kinnari P.; Joshi, Girish P.

2014-01-01

The aim of this study was to assess the nationwide use of epidural analgesia (EA) and the incidence of postoperative complications in patients undergoing major liver resections (MLR) with and without EA in the United States. The 2001 to 2010 Nationwide Inpatient Sample was queried to identify adult patients undergoing MLR. A 1:1 matched cohort of patients having MLR with and without EA was assembled using propensity-score matching techniques. Differences in the rate of postoperative complications were compared between the matched groups. We identified 68,028 MLR. Overall, 5.9% of patients in the database had procedural codes for postoperative EA. A matched cohort of 802 patients per group was derived from the propensity-matching algorithm. Although use of EA was associated with more blood transfusions (relative risk, 1.36; 95% confidence interval, 1.12–1.65; P = 0.001) and longer hospital stay (median [interquartile range], 6 [5–8] vs 6 [4–8] days), the use of coagulation factors and the incidence of postoperative hemorrhage/hematomas or other postoperative complications were not higher in patients receiving EA. In conclusion, the use of EA for MLR is low, and EA does not seem to influence the incidence of postoperative complications. EA, however, was associated with an increased use of blood transfusions and a longer hospital stay. PMID:25484494

Lifestyle and oral facial disorders associated with sleep bruxism in children.

PubMed

Alencar, Nashalie Andrade de; Fernandes, Alline Birra Nolasco; Souza, Margareth Maria Gomes de; Luiz, Ronir Raggio; Fonseca-Gonçalves, Andréa; Maia, Lucianne Cople

2017-05-01

The aim of the study was to investigate the routine, sleep history, and orofacial disorders associated with children aged 3-7 years with nocturnal bruxism. Children (n = 66) were divided into groups of parent reported nocturnal bruxism (n = 34) and those without the disorder (n = 32). Data about the child's routine during the day, during sleep and awakening, headache frequency, temporomandibular joint (TMJ), and hearing impairments were obtained through interviews with parents/caregivers. Electromyography examination was used to assess the activity of facial muscles. Multiple logistic regression (MLR), chi-square test, and t-test analyses were performed. MLR revealed association of nightmares (p = 0.002; OR = 18.09) and snoring (p = 0.013; OR = 0.14) with bruxism. Variables related to awakening revealed an association with bruxism (p < 0.05). Parents of the main group (children with nocturnal bruxism) reported more complaints of orofacial pain, facial appearance, and headache occurrence (p < 0.05). Auditory and muscle disorders were not significant variables (p > 0.05). Nightmares and snoring are associated with nocturnal bruxism in children. Bruxism in children elicits consequences such as headache, orofacial pain, and pain related to awakening.

Differential gene expression profiles of peripheral blood mononuclear cells in childhood asthma.

PubMed

Kong, Qian; Li, Wen-Jing; Huang, Hua-Rong; Zhong, Ying-Qiang; Fang, Jian-Pei

2015-05-01

Asthma is a common childhood disease with strong genetic components. This study compared whole-genome expression differences between asthmatic young children and healthy controls to identify gene signatures of childhood asthma. Total RNA extracted from peripheral blood mononuclear cells (PBMC) was subjected to microarray analysis. QRT-PCR was performed to verify the microarray results. Classification and functional characterization of differential genes were illustrated by hierarchical clustering and gene ontology analysis. Multiple logistic regression (MLR) analysis, receiver operating characteristic (ROC) curve analysis, and discriminate power were used to scan asthma-specific diagnostic markers. For fold-change>2 and p < 0.05, there were 758 named differential genes. The results of QRT-PCR confirmed successfully the array data. Hierarchical clustering divided 29 highly possible genes into seven categories and the genes in the same cluster were likely to possess similar expression patterns or functions. Gene ontology analysis presented that differential genes primarily enriched in immune response, response to stress or stimulus, and regulation of apoptosis in biological process. MLR and ROC curve analysis revealed that the combination of ADAM33, Smad7, and LIGHT possessed excellent discriminating power. The combination of ADAM33, Smad7, and LIGHT would be a reliable and useful childhood asthma model for prediction and diagnosis.

Musculoskeletal Injuries and Training Patterns in Junior Elite Orienteering Athletes

PubMed Central

Taube, Wolfgang; Zuest, Peter; Clénin, German; Wyss, Thomas

2015-01-01

Findings about the relation between musculoskeletal injuries and training patterns in orienteering athletes are sparse. Therefore, the musculoskeletal injuries and training patterns of 31 Swiss elite orienteering athletes aged 18-19 years were analyzed in a retrospective study. Individual training diaries and medical records were used to assess training data and injury history, respectively. Group comparisons and a multiple linear regression (MLR) were performed for statistical analysis. The junior elite orienteering athletes performed 7.38 ± 2.00 training sessions weekly, with a total duration of 455.75 ± 98.22 minutes. An injury incidence rate (IIR) of 2.18 ± 2.13 injuries per 1000 hours of training was observed. The lower extremity was affected in 93% of all injuries, and the knee (33%) was the most commonly injured location. The MLR revealed that gender and six training variables explained 60% of the variance in the injury severity index in this study. Supported by the low IIR in the observed age group, the training protocol of the junior elite orienteering athletes was generally adequate. In comparison to elite track, marathon, and orienteering athletes, the junior elite athletes performed less high-intensity interval training (HIIT). However, more frequent HIIT seems to be a protective factor against injuries. PMID:26258134

Comparison of empirical and data driven hydrometeorological hazard models on coastal cities of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Koga-Vicente, A.; Friedel, M. J.

2010-12-01

Every year thousands of people are affected by floods and landslide hazards caused by rainstorms. The problem is more serious in tropical developing countries because of the susceptibility as a result of the high amount of available energy to form storms, and the high vulnerability due to poor economic and social conditions. Predictive models of hazards are important tools to manage this kind of risk. In this study, a comparison of two different modeling approaches was made for predicting hydrometeorological hazards in 12 cities on the coast of São Paulo, Brazil, from 1994 to 2003. In the first approach, an empirical multiple linear regression (MLR) model was developed and used; the second approach used a type of unsupervised nonlinear artificial neural network called a self-organized map (SOM). By using twenty three independent variables of susceptibility (precipitation, soil type, slope, elevation, and regional atmospheric system scale) and vulnerability (distribution and total population, income and educational characteristics, poverty intensity, human development index), binary hazard responses were obtained. Model performance by cross-validation indicated that the respective MLR and SOM model accuracy was about 67% and 80%. Prediction accuracy can be improved by the addition of information, but the SOM approach is preferred because of sparse data and highly nonlinear relations among the independent variables.

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

NASA Astrophysics Data System (ADS)

Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

2018-03-01

Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.

Comparison of perioperative outcomes between open and robotic radical cystectomy: a population based analysis.

PubMed

Nazzani, Sebastiano; Mazzone, Elio; Preisser, Felix; Bandini, Marco; Tian, Zhe; Marchioni, Michele; Ratti, Dario; Motta, Gloria; Zorn, Kevin Christopher; Briganti, Alberto; Shariat, Shahrokh F; Montanari, Emanuele; Carmignani, Luca; Karakiewicz, Pierre I

2018-05-30

Radical cystectomy represents the standard of care for muscle invasive bladder cancer (MIBC). Due to its novelty the use of robotic radical cystectomy (RARC) is still under debate. We examined intraoperative and postoperative morbidity and mortality as well as impact on length of stay (LOS) and total hospital charges (THCGs) of RARC compared to open radical cystectomy (ORC). Within National Inpatient Sample (NIS) (2008-2013), we identified patients with non-metastatic bladder cancer treated with either ORC or RARC. We relied on inverse probability of treatment weighting (IPTW) to reduce the effect of inherent differences between ORC vs. RARC. Multivariable logistic regression (MLR) and multivariable Poisson regression models (MPR) were used. Of all 10 027 patients, 12.6% underwent RARC. Between 2008 and 2013, RARC rates increased from 0.8 to 20.4% [Estimated annual percentage change (EAPC): +26.5%, CI: +11.1 to +48.3; p=0.035] and RARC THCGs decreased from 45 981 to 31 749 United States Dollars (EAPC: -6.8%, CI: -9.6 to -3.9; p=0.01). In MLR models RARC resulted in lower rates of overall complications (OR: 0.6; p <0.001) and transfusions (OR: 0.44; p <0.001). In MPR models, RARC was associated with shorter LOS [relative risk(RR)0.91 ; p <0.001]. Finally, higher THCGs (OR: 1.09; p <0.001) were recorded for RARC. Data are retrospective and no tumor characteristics were available. RARC is related to lower rates of overall complications and transfusions rates. In consequence, RARC is a safe and feasible technique in select muscle invasive bladder cancer patients. Moreover, RARC is associated with shorter LOS albeit higher THCGs.  .

Correlation of porous and functional properties of food materials by NMR relaxometry and multivariate analysis.

PubMed

Haiduc, Adrian Marius; van Duynhoven, John

2005-02-01

The porous properties of food materials are known to determine important macroscopic parameters such as water-holding capacity and texture. In conventional approaches, understanding is built from a long process of establishing macrostructure-property relations in a rational manner. Only recently, multivariate approaches were introduced for the same purpose. The model systems used here are oil-in-water emulsions, stabilised by protein, and form complex structures, consisting of fat droplets dispersed in a porous protein phase. NMR time-domain decay curves were recorded for emulsions with varied levels of fat, protein and water. Hardness, dry matter content and water drainage were determined by classical means and analysed for correlation with the NMR data with multivariate techniques. Partial least squares can calibrate and predict these properties directly from the continuous NMR exponential decays and yields regression coefficients higher than 82%. However, the calibration coefficients themselves belong to the continuous exponential domain and do little to explain the connection between NMR data and emulsion properties. Transformation of the NMR decays into a discreet domain with non-negative least squares permits the use of multilinear regression (MLR) on the resulting amplitudes as predictors and hardness or water drainage as responses. The MLR coefficients show that hardness is highly correlated with the components that have T2 distributions of about 20 and 200 ms whereas water drainage is correlated with components that have T2 distributions around 400 and 1800 ms. These T2 distributions very likely correlate with water populations present in pores with different sizes and/or wall mobility. The results for the emulsions studied demonstrate that NMR time-domain decays can be employed to predict properties and to provide insight in the underlying microstructural features.

Urbanization effects on fishes and habitat quality in a southern Piedmont river basin

USGS Publications Warehouse

Walters, D.M.; Freeman, Mary C.; Leigh, D.S.; Freeman, B.J.; Pringle, C.P.; Brown, Larry R.; Gray, Robert H.; Hughes, Robert H.; Meador, Michael

2005-01-01

We quantified the relationships among urban land cover, fishes, and habitat quality to determine how fish assemblages respond to urbanization and if a habitat index can be used as an indirect measure of urban effects on stream ecosystems. We sampled 30 wadeable streams along an urban gradient (5?37% urban land cover) in the Etowah River basin, Georgia. Fish assemblages, sampled by electrofishing standardized stream reaches, were assessed using species richness, density, and species composition metrics. Habitat quality was scored using the Rapid Habitat Assessment Protocol (RHAP) of the U.S. Environmental Protection Agency. Urban land cover (including total, high-, and low-density urban) was estimated for the drainage basin above each reach. A previous study of these sites indicated that stream slope and basin area were strongly related to local variation in assemblage structure. We used multiple linear regression (MLR) analysis to account for this variation and isolate the urban effect on fishes. The MLR models indicated that urbanization lowered species richness and density and led to predictable changes in species composition. Darters and sculpin, cyprinids, and endemics declined along the urban gradient whereas centrarchids persisted and became the dominant group. The RHAP was not a suitable indicator of urban effects because RHAP-urban relationships were confounded by an overriding influence of stream slope on RHAP scores, and urban-related changes in fish assemblage structure preceded gross changes in stream habitat quality. Regression analysis indicated that urban effects on fishes accrue rapidly (<10 years) and are detectable at low levels (~5?10% urbanization). We predict that the decline of endemics and other species will continue and centrarchid-dominated streams will become more common as development proceeds within the Etowah basin.

Adulteration of Argentinean milk fats with animal fats: Detection by fatty acids analysis and multivariate regression techniques.

PubMed

Rebechi, S R; Vélez, M A; Vaira, S; Perotti, M C

2016-02-01

The aims of the present study were to test the accuracy of the fatty acid ratios established by the Argentinean Legislation to detect adulterations of milk fat with animal fats and to propose a regression model suitable to evaluate these adulterations. For this purpose, 70 milk fat, 10 tallow and 7 lard fat samples were collected and analyzed by gas chromatography. Data was utilized to simulate arithmetically adulterated milk fat samples at 0%, 2%, 5%, 10% and 15%, for both animal fats. The fatty acids ratios failed to distinguish adulterated milk fats containing less than 15% of tallow or lard. For each adulterant, Multiple Linear Regression (MLR) was applied, and a model was chosen and validated. For that, calibration and validation matrices were constructed employing genuine and adulterated milk fat samples. The models were able to detect adulterations of milk fat at levels greater than 10% for tallow and 5% for lard. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.

PubMed

Li, Guang; Wei, Jie; Huang, Hailiang; Gaebler, Carl Philipp; Yuan, Amy; Deasy, Joseph O

2015-12-01

To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients ( f v ) to account for most of the spectrum energy (Σ f v 2 ). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves ( R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors, providing an automatic robust tool to evaluate diaphragm motion.

76 FR 35445 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-17

... Information Collection: Medical Loss Ratio Quarterly Reporting; Use: Under Section 2718 of the Affordable Care... ratio, referred to as the medical loss ratio (MLR). An interim final rule (IFR) implementing the MLR was... information collections, please reference the document identifier or OMB control number. To be assured...

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

Trbović, D.; Petronijević, R.; Đorđević, V.

2017-09-01

Milk and milk-based products are among the leading food categories according to reported cases of food adulteration. Although many authentication problems exist in all areas of the food industry, adequate control methods are required to evaluate the authenticity of milk and milk products in the dairy industry. Moreover, gas chromatography (GC) analysis of triacylglycerols (TAGs) or fatty acid (FA) profiles of milk fat (MF) in combination with multivariate statistical data processing have been used to detect adulterations of milk and dairy products with foreign fats. The adulteration of milk and butter is a major issue for the dairy industry. The major adulterants of MF are vegetable oils (soybean, sunflower, groundnut, coconut, palm and peanut oil) and animal fat (cow tallow and pork lard). Multivariate analysis enables adulterated MF to be distinguished from authentic MF, while taking into account many analytical factors. Various multivariate analysis methods have been proposed to quantitatively detect levels of adulterant non-MFs, with multiple linear regression (MLR) seemingly the most suitable. There is a need for increased use of chemometric data analyses to detect adulterated MF in foods and for their expanded use in routine quality assurance testing.

Spatial variability in water-balance model performance in the conterminous United States

USGS Publications Warehouse

Hay, L.E.; McCabe, G.J.

2002-01-01

A monthly water-balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil-moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R-square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long-term mean and standard deviation of annual precipitation; temperature and runoff; and low-flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R-square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest. Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.

Nailfold Videocapillaroscopic Features and Other Clinical Risk Factors for Digital Ulcers in Systemic Sclerosis: A Multicenter, Prospective Cohort Study.

PubMed

Cutolo, Maurizio; Herrick, Ariane L; Distler, Oliver; Becker, Mike O; Beltran, Emma; Carpentier, Patrick; Ferri, Clodoveo; Inanç, Murat; Vlachoyiannopoulos, Panayiotis; Chadha-Boreham, Harbajan; Cottreel, Emmanuelle; Pfister, Thomas; Rosenberg, Daniel; Torres, Juan V; Smith, Vanessa

2016-10-01

To identify nailfold videocapillaroscopic features and other clinical risk factors for new digital ulcers (DUs) during a 6-month period in patients with systemic sclerosis (SSc). In this multicenter, prospective, observational cohort study, the videoCAPillaroscopy (CAP) study, we evaluated 623 patients with SSc from 59 centers (14 countries). Patients were stratified into 2 groups: a DU history group and a no DU history group. At enrollment, patients underwent detailed nailfold videocapillaroscopic evaluation and assessment of demographic characteristics, DU status, and clinical and SSc characteristics. Risk factors for developing new DUs were assessed using univariable and multivariable logistic regression (MLR) analyses. Of the 468 patients in the DU history group (mean ± SD age 54.0 ± 13.7 years), 79.5% were female, 59.8% had limited cutaneous SSc, and 22% developed a new DU during follow-up. The strongest risk factors for new DUs identified by MLR in the DU history group included the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs (categorized as 0, 1, 2, or ≥3), and the presence of critical digital ischemia. The receiver operating characteristic (ROC) of the area under the curve (AUC) of the final MLR model was 0.738 (95% confidence interval [95% CI] 0.681-0.795). Internal validation through bootstrap generated a ROC AUC of 0.633 (95% CI 0.510-0.756). This international prospective study, which included detailed nailfold videocapillaroscopic evaluation and extensive clinical characterization of patients with SSc, identified the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs at enrollment, and the presence of critical digital ischemia at enrollment as risk factors for the development of new DUs. © 2016, American College of Rheumatology.

Use of regression‐based models to map sensitivity of aquatic resources to atmospheric deposition in Yosemite National Park, USA

USGS Publications Warehouse

Clow, David W.; Nanus, Leora; Huggett, Brian

2010-01-01

An abundance of exposed bedrock, sparse soil and vegetation, and fast hydrologic flushing rates make aquatic ecosystems in Yosemite National Park susceptible to nutrient enrichment and episodic acidification due to atmospheric deposition of nitrogen (N) and sulfur (S). In this study, multiple linear regression (MLR) models were created to estimate fall‐season nitrate and acid neutralizing capacity (ANC) in surface water in Yosemite wilderness. Input data included estimated winter N deposition, fall‐season surface‐water chemistry measurements at 52 sites, and basin characteristics derived from geographic information system layers of topography, geology, and vegetation. The MLR models accounted for 84% and 70% of the variance in surface‐water nitrate and ANC, respectively. Explanatory variables (and the sign of their coefficients) for nitrate included elevation (positive) and the abundance of neoglacial and talus deposits (positive), unvegetated terrain (positive), alluvium (negative), and riparian (negative) areas in the basins. Explanatory variables for ANC included basin area (positive) and the abundance of metamorphic rocks (positive), unvegetated terrain (negative), water (negative), and winter N deposition (negative) in the basins. The MLR equations were applied to 1407 stream reaches delineated in the National Hydrography Data Set for Yosemite, and maps of predicted surface‐water nitrate and ANC concentrations were created. Predicted surface‐water nitrate concentrations were highest in small, high‐elevation cirques, and concentrations declined downstream. Predicted ANC concentrations showed the opposite pattern, except in high‐elevation areas underlain by metamorphic rocks along the Sierran Crest, which had relatively high predicted ANC (>200 μeq L−1). Maps were created to show where basin characteristics predispose aquatic resources to nutrient enrichment and acidification effects from N and S deposition. The maps can be used to help guide development of water‐quality programs designed to monitor and protect natural resources in national parks.

Monsoonal variations in atmospheric surfactants at different coastal areas of the Malaysian Peninsula.

PubMed

Jaafar, Shoffian Amin; Latif, Mohd Talib; Razak, Intan Suraya; Shaharudin, Muhammad Zulhilmi; Khan, Md Firoz; Wahid, Nurul Bahiyah Abd; Suratman, Suhaimi

2016-08-15

This study determined the effect of monsoonal changes on the composition of atmospheric surfactants in coastal areas. The composition of anions (SO4(2-), NO3(-), Cl(-), F(-)) and the major elements (Ca, K, Mg, Na) in aerosols were used to determine the possible sources of surfactants. Surfactant compositions were determined using a colorimetric method as methylene blue active substances (MBAS) and disulphine blue active substances (DBAS). The anion and major element compositions of the aerosol samples were determined by ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results indicated that the concentrations of surfactant in aerosols were dominated by MBAS (34-326pmolm(-3)). Monsoonal changes were found to significantly affect the concentration of surfactants. Using principal component analysis-multiple linear regressions (PCA-MLR), major possible sources for surfactants in the aerosols were motor vehicle emissions, secondary aerosol and the combustion of biomass along with marine aerosol. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

NASA Astrophysics Data System (ADS)

Ojha, Lokendra Kumar

2017-07-01

The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

A new spectrophotometric method for determination of EDTA in water using its complex with Mn(III).

PubMed

Andrade, Carlos Eduardo O; Oliveira, André F; Neves, Antônio A; Queiroz, Maria Eliana L R

2016-11-05

EDTA is an important ligand used in many industrial products as well as in agriculture, where it is employed to assist in phytoextraction procedures and the absorption of nutrients by plants. Due to its intensive use and recalcitrance, it is now considered an emerging pollutant in water, so there is great interest in techniques suitable for its monitoring. This work proposes a method based on formation of the Mn(III)-EDTA complex after oxidation of the Mn(II)-EDTA complex by PbO2 immobilized on cyanoacrylate spheres. A design of experiments (DOE) based on the Doehlert matrix was used to determine the optimum conditions of the method, and the influence of the variables was evaluated using a multiple linear regression (MLR) model. The optimized method presented a linear response in the range from 0.77 to 100.0μmolL(-1), with analytical sensitivity of 7.7×10(3)Lmol(-1), a coefficient of determination of 0.999, and a limit of detection of 0.23μmolL(-1). The method was applied using samples fortified at different concentration levels, and the recoveries achieved were between 97.0 and 104.9%. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Health insurance issuers implementing medical loss ratio (MLR) requirements under the Patient Protection and Affordable Care Act. Interim final rule with request for comments.

PubMed

2010-12-01

This document contains the interim final regulation implementing medical loss ratio (MLR) requirements for health insurance issuers under the Public Health Service Act, as added by the Patient Protection and Affordable Care Act (Affordable Care Act).

Abnormal reflex activation of hamstring muscles in dogs with cranial cruciate ligament rupture.

PubMed

Hayes, Graham M; Granger, Nicolas; Langley-Hobbs, Sorrel J; Jeffery, Nick D

2013-06-01

The mechanisms underlying cranial cruciate ligament rupture (CCLR) in dogs are poorly understood. In this study hamstring muscle reflexes in response to cranial tibial translation were analysed to determine whether these active stabilisers of the stifle joint are differently activated in dogs with CCLR compared to control dogs. In a prospective clinical study reflex muscle activity from the lateral and medial hamstring muscles (biceps femoris and semimembranosus) was recorded using surface electrodes in control dogs (n=21) and dogs with CCLR (n=22). These electromyographic recordings were analysed using an algorithm previously validated in humans. The hamstring reflex was reliably and reproducibly recorded in normal dogs. Both a short latency response (SLR, 17.6±2.1ms) and a medium latency response (MLR, 37.7±2.7ms) could be identified. In dogs with unilateral CCLR, the SLR and MLR were not significantly different between the affected and the unaffected limbs, but the MLR latency of both affected and unaffected limbs in CCLR dogs were significantly prolonged compared to controls. In conclusion, the hamstring reflex can be recorded in dogs and the MLR is prolonged in dogs with CCLR. Since both affected and unaffected limbs exhibit prolonged MLR, it is possible that abnormal hamstring reflex activation is a mechanism by which progressive CCL damage may occur. The methodology allows for further investigation of the relationship between neuromuscular imbalance and CCLR or limitations in functional recovery following surgical intervention. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cannabinoids Inhibit T-cells via Cannabinoid Receptor 2 in an in vitro Assay for Graft Rejection, the Mixed Lymphocyte Reaction

PubMed Central

Robinson, Rebecca Hartzell; Meissler, Joseph J.; Breslow-Deckman, Jessica M.; Gaughan, John; Adler, Martin W.; Eisenstein, Toby K.

2013-01-01

Cannabinoids are known to have anti-inflammatory and immunomodulatory properties. Cannabinoid receptor 2 (CB2) is expressed mainly on leukocytes and is the receptor implicated in mediating many of the effects of cannabinoids on immune processes. This study tested the capacity of Δ9-tetrahydrocannabinol (Δ9-THC) and of two CB2-selective agonists to inhibit the murine Mixed Lymphocyte Reaction (MLR), an in vitro correlate of graft rejection following skin and organ transplantation. Both CB2-selective agonists and Δ9-THC significantly suppressed the MLR in a dose dependent fashion. The inhibition was via CB2, as suppression could be blocked by pretreatment with a CB2-selective antagonist, but not by a CB1 antagonist, and none of the compounds suppressed the MLR when splenocytes from CB2 deficient mice were used. The CB2 agonists were shown to act directly on T-cells, as exposure of CD3+ cells to these compounds completely inhibited their action in a reconstituted MLR. Further, the CB2-selective agonists completely inhibited proliferation of purified T-cells activated by anti-CD3 and anti-CD28 antibodies. T-cell function was decreased by the CB2 agonists, as an ELISA of MLR culture supernatants revealed IL-2 release was significantly decreased in the cannabinoid treated cells. Together, these data support the potential of this class of compounds as useful therapies to prolong graft survival in transplant patients. PMID:23824763

Algorithm Development and Validation of CDOM Properties for Estuarine and Continental Shelf Waters Along the Northeastern U.S. Coast

NASA Technical Reports Server (NTRS)

Mannino, Antonio; Novak, Michael G.; Hooker, Stanford B.; Hyde, Kimberly; Aurin, Dick

2014-01-01

An extensive set of field measurements have been collected throughout the continental margin of the northeastern U.S. from 2004 to 2011 to develop and validate ocean color satellite algorithms for the retrieval of the absorption coefficient of chromophoric dissolved organic matter (aCDOM) and CDOM spectral slopes for the 275:295 nm and 300:600 nm spectral range (S275:295 and S300:600). Remote sensing reflectance (Rrs) measurements computed from in-water radiometry profiles along with aCDOM() data are applied to develop several types of algorithms for the SeaWiFS and MODIS-Aqua ocean color satellite sensors, which involve least squares linear regression of aCDOM() with (1) Rrs band ratios, (2) quasi-analytical algorithm-based (QAA based) products of total absorption coefficients, (3) multiple Rrs bands within a multiple linear regression (MLR) analysis, and (4) diffuse attenuation coefficient (Kd). The relative error (mean absolute percent difference; MAPD) for the MLR retrievals of aCDOM(275), aCDOM(355), aCDOM(380), aCDOM(412) and aCDOM(443) for our study region range from 20.4-23.9 for MODIS-Aqua and 27.3-30 for SeaWiFS. Because of the narrower range of CDOM spectral slope values, the MAPD for the MLR S275:295 and QAA-based S300:600 algorithms are much lower ranging from 9.9 and 8.3 for SeaWiFS, respectively, and 8.7 and 6.3 for MODIS, respectively. Seasonal and spatial MODIS-Aqua and SeaWiFS distributions of aCDOM, S275:295 and S300:600 processed with these algorithms are consistent with field measurements and the processes that impact CDOM levels along the continental shelf of the northeastern U.S. Several satellite data processing factors correlate with higher uncertainty in satellite retrievals of aCDOM, S275:295 and S300:600 within the coastal ocean, including solar zenith angle, sensor viewing angle, and atmospheric products applied for atmospheric corrections. Algorithms that include ultraviolet Rrs bands provide a better fit to field measurements than algorithms without the ultraviolet Rrs bands. This suggests that satellite sensors with ultraviolet capability could provide better retrievals of CDOM. Because of the strong correlations between CDOM parameters and DOM constituents in the coastal ocean, satellite observations of CDOM parameters can be applied to study the distributions, sources and sinks of DOM, which are relevant for understanding the carbon cycle, modeling the Earth system, and to discern how the Earth is changing.

76 FR 71569 - Agency Information Collection Activities: Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-18

... Loss Ratio (MLR) Quarterly Reporting for Mini-Med Plans and Expatriate Plans; Use: Under Section 2718... IFR is effective January 1, 2011. Issuers are required to submit annual MLR reporting data for each... conducts business. For policies that have a total annual limit of $250,000 or less (sometimes referred to...

77 FR 28788 - Health Insurance Issuers Implementing Medical Loss Ratio (MLR) Under the Patient Protection and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-16

... DEPARTMENT OF HEALTH AND HUMAN SERVICES 45 CFR Part 158 [CMS-9998-IFC3] Health Insurance Issuers..., entitled ``Health Insurance Issuers Implementing Medical Loss Ratio (MLR) Requirements Under the Patient...) requirements for health insurance issuers under section 2718 of the Public Health Service Act, as added by the...

Single particle maximum likelihood reconstruction from superresolution microscopy images

PubMed Central

Verdier, Timothée; Gunzenhauser, Julia; Manley, Suliana; Castelnovo, Martin

2017-01-01

Point localization superresolution microscopy enables fluorescently tagged molecules to be imaged beyond the optical diffraction limit, reaching single molecule localization precisions down to a few nanometers. For small objects whose sizes are few times this precision, localization uncertainty prevents the straightforward extraction of a structural model from the reconstructed images. We demonstrate in the present work that this limitation can be overcome at the single particle level, requiring no particle averaging, by using a maximum likelihood reconstruction (MLR) method perfectly suited to the stochastic nature of such superresolution imaging. We validate this method by extracting structural information from both simulated and experimental PALM data of immature virus-like particles of the Human Immunodeficiency Virus (HIV-1). MLR allows us to measure the radii of individual viruses with precision of a few nanometers and confirms the incomplete closure of the viral protein lattice. The quantitative results of our analysis are consistent with previous cryoelectron microscopy characterizations. Our study establishes the framework for a method that can be broadly applied to PALM data to determine the structural parameters for an existing structural model, and is particularly well suited to heterogeneous features due to its single particle implementation. PMID:28253349

Bicuculline and strychnine suppress the mesencephalic locomotor region-induced inhibition of group III muscle afferent input to the dorsal horn.

PubMed

Degtyarenko, A M; Kaufman, M P

2003-01-01

We examined the effect of iontophoretic application of bicuculline methiodide and strychnine hydrochloride on the mesencephalic locomotor region (MLR)-induced inhibition of dorsal horn cells in paralyzed cats. The activity of 60 dorsal horn cells was recorded extracellularly in laminae I, II, V-VII of spinal segments L7-S1. Each of the cells was shown to receive group III muscle afferent input as demonstrated by their responses to electrical stimulation of the tibial nerve (mean latency and threshold of activation: 20.1+/-6.4 ms and 15.2+/-1.4 times motor threshold, respectively). Electrical stimulation of the MLR suppressed transmission in group III muscle afferent pathways to dorsal horn cells. Specifically the average number of impulses generated by the dorsal horn neurons in response to a single pulse applied to the tibial nerve was decreased by 78+/-2.8% (n=60) during the MLR stimulation. Iontophoretic application (10-50 nA) of bicuculline and strychnine (5-10 mM) suppressed the MLR-induced inhibition of transmission of group III afferent input to laminae I and II cells by 69+/-5% (n=10) and 29+/-7% (n=7), respectively. Likewise, bicuculline and strychnine suppressed the MLR-induced inhibition of transmission of group III afferent input to lamina V cells by 59+/-13% (n=14) and 39+/-11% (n=10), respectively. Our findings raise the possibility that GABA and glycine release onto dorsal horn neurons in the spinal cord may play an important role in the suppression by central motor command of thin fiber muscle afferent-reflex pathways.

Severe asthma and adherence to peak flow monitoring: longitudinal assessment of psychological aspects.

PubMed

Halimi, Laurence; Pry, René; Pithon, Gérard; Godard, Philippe; Varrin, Muriel; Chanez, Pascal

2010-10-01

Adherence in severe asthma is a difficult health problem. Although psychosocial factors may be responsible for non-adherence, few longitudinal studies have investigated their link with adherence, with most studies having focused on pharmacology. Sixty patients with severe asthma were recruited. Adherence was electronically monitored using peak flow measurements at entry and after 1 year of follow-up. Eysenck's Personality Inventory, Rotter's Locus of Control (LOC), and health control beliefs were all studied. Multiple logistic regression (MLR) was used for risk calculations. Initially, subjects with poor adherence had an external LOC (P=.001) and a high extraversion score (P=.003) compared to those with good adherence. The lie score was high in all patients. Nocturnal awakenings were highly significantly correlated with poor adherence (P=.006). After 1 year, patient adherence, extraversion, and neuroticism remained unchanged. The LOC changed in subjects with poor adherence, showing a less "external" orientation (P=.007). The health parameters were better at the end of the study. By MLR analysis, externality, extraversion, and low social desirability were associated with poor adherence. Patients with poor adherence had a greater probability of nocturnal symptoms. No specific personality type was associated with lack of adherence in the present study, but a high extraversion score, a low social desirability score, and a high level of externality were all predictors of poor adherence. Copyright 2010 Elsevier Inc. All rights reserved.

Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis.

PubMed

Mercuri, A; Pagliari, M; Baxevanis, F; Fares, R; Fotaki, N

2017-02-25

In this study the selection of in vivo predictive in vitro dissolution experimental set-ups using a multivariate analysis approach, in line with the Quality by Design (QbD) principles, is explored. The dissolution variables selected using a design of experiments (DoE) were the dissolution apparatus [USP1 apparatus (basket) and USP2 apparatus (paddle)], the rotational speed of the basket/or paddle, the operator conditions (dissolution apparatus brand and operator), the volume, the pH, and the ethanol content of the dissolution medium. The dissolution profiles of two nifedipine capsules (poorly soluble compound), under conditions mimicking the intake of the capsules with i. water, ii. orange juice and iii. an alcoholic drink (orange juice and ethanol) were analysed using multiple linear regression (MLR). Optimised dissolution set-ups, generated based on the mathematical model obtained via MLR, were used to build predicted in vitro-in vivo correlations (IVIVC). IVIVC could be achieved using physiologically relevant in vitro conditions mimicking the intake of the capsules with an alcoholic drink (orange juice and ethanol). The multivariate analysis revealed that the concentration of ethanol used in the in vitro dissolution experiments (47% v/v) can be lowered to less than 20% v/v, reflecting recently found physiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Psychological symptoms among 2032 youth living with HIV: a multisite study.

PubMed

Brown, Larry K; Whiteley, Laura; Harper, Gary W; Nichols, Sharon; Nieves, Amethys

2015-04-01

This study determined the prevalence and patterns of psychological symptoms in adolescents and young adults living with HIV (YLWH) in medical care and relationships between psychological symptoms, route and duration of infection, and antiretroviral treatment (ART). A clinic-based sample of 2032 YLWH (mean age 20.3 years), recruited from 20 adolescent medicine HIV clinics, completed a cross-sectional survey of health behaviors and psychological symptoms using the Brief Symptom Inventory (BSI). Overall, 17.5% of youth reported psychological symptoms greater than the normative threshold on the Global Severity Index. A wide variety of symptoms were reported. The prevalence of clinical symptoms was significantly greater in youth with behaviorally acquired HIV compared to those with perinatally acquired infection (20.6% vs. 10.8%, OR=2.06 in Multiple Logistic Regression (MLR)), and in those not taking ART that had been prescribed (29. 2% vs. 18.8%, OR=1.68 in MLR). Knowing one's HIV status for more than one year and disclosure of HIV status were not associated with fewer symptoms. A large proportion of YLWH have psychological symptoms and the prevalence is greatest among those with behaviorally acquired infection. The high rate of psychological symptoms for youth not taking ART that is prescribed is a cause for concern. Symptoms do not appear to be a transient reaction to diagnosis of HIV.

Assessing soil heavy metal pollution in the water-level-fluctuation zone of the Three Gorges Reservoir, China.

PubMed

Ye, Chen; Li, Siyue; Zhang, Yulong; Zhang, Quanfa

2011-07-15

The water-level-fluctuation zone (WLFZ) between the elevations of 145-175 m in China's Three Gorges Reservoir has experienced a novel hydrological regime with half a year (May-September) exposed in summer and another half (October-April) submerged in winter. In September 2008 (before submergence) and June 2009 (after submergence), soil samples were collected in 12 sites in the WLFZ and heavy metals (Hg, As, Cr, Cd, Pb, Cu, Zn, Fe, and Mn) were determined. Enrichment factor (EF), factor analysis (FA), and factor analysis-multiple linear regression (FA-MLR) were employed for heavy metal pollution assessment, source identification, and source apportionment, respectively. Results demonstrate spatial variability in heavy metals before and after submergence and elements of As, Cd, Pb, Cu, and Zn are higher in the upper and low reaches. FA and FA-MLR reveal that As and Cd are the primary pollutants before submergence, and over 45% of As originates from domestic sewage and 59% of Cd from industrial wastes. After submergence, the major contaminants are Hg, Cd, and Pb, and traffic exhaust contributes approximately 81% to Hg and industrial effluent accounts about 36% and 73% for Cd and Pb, respectively. Our results suggest that increased shipping and industrial wastes have deposited large amounts of heavy metals which have been accumulated in the WLFZ during submergence period. Copyright © 2011 Elsevier B.V. All rights reserved.

Quantitative identification and source apportionment of anthropogenic heavy metals in marine sediment of Hong Kong

NASA Astrophysics Data System (ADS)

Zhou, Feng; Guo, Huaicheng; Liu, Lei

2007-10-01

Based on ten heavy metals collected twice annually at 59 sites from 1998 to 2004, enrichment factors (EFs), principal component analysis (PCA) and multivariate linear regression of absolute principal component scores (MLR-APCS) were used in identification and source apportionment of the anthropogenic heavy metals in marine sediment. EFs with Fe as a normalizer and local background as reference values was properly tested and suitable in Hong Kong, and Zn, Ni, Pb, Cu, Cd, Hg and Cr mainly originated from anthropogenic sources, while Al, Mn and Fe were derived from rocks weathering. Rotated PCA and GIS mapping further identified two types of anthropogenic sources and their impacted regions: (1) electronic industrial pollution, riparian runoff and vehicle exhaust impacted the entire Victoria Harbour, inner Tolo Harbour, Eastern Buffer, inner Deep Bay and Cheung Chau; and (2) discharges from textile factories and paint, influenced Tsuen Wan Bay and Kwun Tong typhoon shelter and Rambler Channel. In addition, MLR-APCS was successfully introduced to quantitatively determine the source contributions with uncertainties almost less than 8%: the first anthropogenic sources were responsible for 50.0, 45.1, 86.6, 78.9 and 87.5% of the Zn, Pb, Cu, Cd and Hg, respectively, whereas 49.9% of the Ni and 58.4% of the Cr came from the second anthropogenic sources.

Estimating solar radiation using NOAA/AVHRR and ground measurement data

NASA Astrophysics Data System (ADS)

Fallahi, Somayeh; Amanollahi, Jamil; Tzanis, Chris G.; Ramli, Mohammad Firuz

2018-01-01

Solar radiation (SR) data are commonly used in different areas of renewable energy research. Researchers are often compelled to predict SR at ground stations for areas with no proper equipment. The objective of this study was to test the accuracy of the artificial neural network (ANN) and multiple linear regression (MLR) models for estimating monthly average SR over Kurdistan Province, Iran. Input data of the models were two data series with similar longitude, latitude, altitude, and month (number of months) data, but there were differences between the monthly mean temperatures in the first data series obtained from AVHRR sensor of NOAA satellite (DS1) and in the second data series measured at ground stations (DS2). In order to retrieve land surface temperature (LST) from AVHRR sensor, emissivity of the area was considered and for that purpose normalized vegetation difference index (NDVI) calculated from channels 1 and 2 of AVHRR sensor was utilized. The acquired results showed that the ANN model with DS1 data input with R2 = 0.96, RMSE = 1.04, MAE = 1.1 in the training phase and R2 = 0.96, RMSE = 1.06, MAE = 1.15 in the testing phase achieved more satisfactory performance compared with MLR model. It can be concluded that ANN model with remote sensing data has the potential to predict SR in locations with no ground measurement stations.

Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2014-09-02

Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers. Copyright © 2014 Elsevier B.V. All rights reserved.

Molecular Insights into the pH-Dependent Adsorption and Removal of Ionizable Antibiotic Oxytetracycline by Adsorbent Cyclodextrin Polymers

PubMed Central

Zhang, Yu; Cai, Xiyun; Xiong, Weina; Jiang, Hao; Zhao, Haitong; Yang, Xianhai; Li, Chao; Fu, Zhiqiang; Chen, Jingwen

2014-01-01

Effects of pH on adsorption and removal efficiency of ionizable organic compounds (IOCs) by environmental adsorbents are an area of debate, because of its dual mediation towards adsorbents and adsorbate. Here, we probe the pH-dependent adsorption of ionizable antibiotic oxytetracycline (comprising OTCH2 +, OTCH±, OTC−, and OTC2−) onto cyclodextrin polymers (CDPs) with the nature of molecular recognition and pH inertness. OTCH± commonly has high adsorption affinity, OTC− exhibits moderate affinity, and the other two species have negligible affinity. These species are evidenced to selectively interact with structural units (e.g., CD cavity, pore channel, and network) of the polymers and thus immobilized onto the adsorbents to different extents. The differences in adsorption affinity and mechanisms of the species account for the pH-dependent adsorption of OTC. The mathematical equations are derived from the multiple linear regression (MLR) analysis of quantitatively relating adsorption affinity of OTC at varying pH to adsorbent properties. A combination of the MLR analysis for OTC and molecular recognition of adsorption of the species illustrates the nature of the pH-dependent adsorption of OTC. Based on this finding, γ-HP-CDP is chosen to adsorb and remove OTC at pH 5.0 and 7.0, showing high removal efficiency and strong resistance to the interference of coexisting components. PMID:24465975

Comparison of exercise capacity in COPD and other etiologies of chronic respiratory failure requiring non-invasive mechanical ventilation at home: retrospective analysis of 1-year follow-up.

PubMed

Salturk, Cuneyt; Karakurt, Zuhal; Takir, Huriye Berk; Balci, Merih; Kargin, Feyza; Mocin, Ozlem Yazıcıoglu; Gungor, Gokay; Ozmen, Ipek; Oztas, Selahattin; Yalcinsoy, Murat; Evin, Ruya; Ozturk, Murat; Adiguzel, Nalan

2015-01-01

The objective of this study was to compare the change in 6-minute walking distance (6MWD) in 1 year as an indicator of exercise capacity among patients undergoing home non-invasive mechanical ventilation (NIMV) due to chronic hypercapnic respiratory failure (CHRF) caused by different etiologies. This retrospective cohort study was conducted in a tertiary pulmonary disease hospital in patients who had completed 1-year follow-up under home NIMV because of CHRF with different etiologies (ie, chronic obstructive pulmonary disease [COPD], obesity hypoventilation syndrome [OHS], kyphoscoliosis [KS], and diffuse parenchymal lung disease [DPLD]), between January 2011 and January 2012. The results of arterial blood gas (ABG) analyses and spirometry, and 6MWD measurements with 12-month interval were recorded from the patient files, in addition to demographics, comorbidities, and body mass indices. The groups were compared in terms of 6MWD via analysis of variance (ANOVA) and multiple linear regression (MLR) analysis (independent variables: analysis age, sex, baseline 6MWD, baseline forced expiratory volume in 1 second, and baseline partial carbon dioxide pressure, in reference to COPD group). A total of 105 patients with a mean age (± standard deviation) of 61±12 years of whom 37 had COPD, 34 had OHS, 20 had KS, and 14 had DPLD were included in statistical analysis. There were no significant differences between groups in the baseline and delta values of ABG and spirometry findings. Both univariate ANOVA and MLR showed that the OHS group had the lowest baseline 6MWD and the highest decrease in 1 year (linear regression coefficient -24.48; 95% CI -48.74 to -0.21, P=0.048); while the KS group had the best baseline values and the biggest improvement under home NIMV (linear regression coefficient 26.94; 95% CI -3.79 to 57.66, P=0.085). The 6MWD measurements revealed improvement in exercise capacity test in CHRF patients receiving home NIMV treatment on long-term depends on etiological diagnoses.

GEMAS: prediction of solid-solution phase partitioning coefficients (Kd) for oxoanions and boric acid in soils using mid-infrared diffuse reflectance spectroscopy.

PubMed

Janik, Leslie J; Forrester, Sean T; Soriano-Disla, José M; Kirby, Jason K; McLaughlin, Michael J; Reimann, Clemens

2015-02-01

The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total or surface-bound metal/metalloid concentration of the solid phase to the total concentration in the solution phase. Values of Kd were measured for boric acid (B[OH]3(0)) and selected added soluble oxoanions: molybdate (MoO4(2-)), antimonate (Sb[OH](6-)), selenate (SeO4(2-)), tellurate (TeO4(2-)) and vanadate (VO4(3-)). Models were developed using approximately 500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) program. These calibration soils represented the major properties of the entire 4813 soils of the GEMAS project. Multiple linear regression (MLR) from soil properties, partial least-squares regression (PLSR) using mid-infrared diffuse reflectance Fourier-transformed (DRIFT) spectra, and models using DRIFT spectra plus analytical pH values (DRIFT + pH), were compared with predicted log K(d + 1) values. Apart from selenate (R(2)  = 0.43), the DRIFT + pH calibrations resulted in marginally better models to predict log K(d + 1) values (R(2)  = 0.62-0.79), compared with those from PSLR-DRIFT (R(2)  = 0.61-0.72) and MLR (R(2)  = 0.54-0.79). The DRIFT + pH calibrations were applied to the prediction of log K(d + 1) values in the remaining 4313 soils. An example map of predicted log K(d + 1) values for added soluble MoO4(2-) in soils across Europe is presented. The DRIFT + pH PLSR models provided a rapid and inexpensive tool to assess the risk of mobility and potential availability of boric acid and selected oxoanions in European soils. For these models to be used in the prediction of log K(d + 1) values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration. © 2014 SETAC.

Meteorological Contribution to Variability in Particulate Matter Concentrations

NASA Astrophysics Data System (ADS)

Woods, H. L.; Spak, S. N.; Holloway, T.

2006-12-01

Local concentrations of fine particulate matter (PM) are driven by a number of processes, including emissions of aerosols and gaseous precursors, atmospheric chemistry, and meteorology at local, regional, and global scales. We apply statistical downscaling methods, typically used for regional climate analysis, to estimate the contribution of regional scale meteorology to PM mass concentration variability at a range of sites in the Upper Midwestern U.S. Multiple years of daily PM10 and PM2.5 data, reported by the U.S. Environmental Protection Agency (EPA), are correlated with large-scale meteorology over the region from the National Centers for Environmental Prediction (NCEP) reanalysis data. We use two statistical downscaling methods (multiple linear regression, MLR, and analog) to identify which processes have the greatest impact on aerosol concentration variability. Empirical Orthogonal Functions of the NCEP meteorological data are correlated with PM timeseries at measurement sites. We examine which meteorological variables exert the greatest influence on PM variability, and which sites exhibit the greatest response to regional meteorology. To evaluate model performance, measurement data are withheld for limited periods, and compared with model results. Preliminary results suggest that regional meteorological processes account over 50% of aerosol concentration variability at study sites.

Predicting heavy metal concentrations in soils and plants using field spectrophotometry

NASA Astrophysics Data System (ADS)

Muradyan, V.; Tepanosyan, G.; Asmaryan, Sh.; Sahakyan, L.; Saghatelyan, A.; Warner, T. A.

2017-09-01

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R2 0.9, RPD 2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R2 0.7, RPD 1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R2, rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

Quantitative assessment of Naegleria fowleri and fecal indicator bacteria in brackish water of Lake Pontchartrain, Louisiana.

PubMed

Xue, Jia; Lamar, Frederica G; Zhang, Bowen; Lin, Siyu; Lamori, Jennifer G; Sherchan, Samendra P

2018-05-01

Brackish water samples from Lake Pontchartrain in Louisiana were assessed for the presence of pathogenic amoeba Naegleria fowleri, which causes primary amoebic meningoencephalitis (PAM). In our study, quantitative polymerase chain reaction (qPCR) methods were used to determine N. fowleri, E. coli, and enterococci in water collected from Lake Pontchartrain. N. fowleri target sequence was detected in 35.4% (56/158) of the water samples from ten sites around the lake. Statistically significant positive correlations between N. fowleri concentration and water temperature as well as E. coli (qPCR) were observed. Multiple linear regression (MLR) model shows seasonal factor (summer or winter) has significant effect on the concentration of N. fowleri, E. coli and enterococci (qPCR) concentration. Significant positive relationships between E. coli and enterococci was observed from both qPCR (r=0.25) and culture based method (r=0.54). Meanwhile, significant positive correlation between qPCR and culture based methods for enterococci concentration was observed (r=0.33). In our study, water temperature and E. coli concentration were indicative of N. fowleri concentrations in brackish water environment. Future research is needed to determine whether sediment is a source of N. fowleri found in the water column. Copyright © 2017 Elsevier B.V. All rights reserved.

Identification of IL-1β and LPS as optimal activators of monolayer and alginate-encapsulated mesenchymal stromal cell immunomodulation using design of experiments and statistical methods.

PubMed

Gray, Andrea; Maguire, Timothy; Schloss, Rene; Yarmush, Martin L

2015-01-01

Induction of therapeutic mesenchymal stromal cell (MSC) function is dependent upon activating factors present in diseased or injured tissue microenvironments. These functions include modulation of macrophage phenotype via secreted molecules including prostaglandin E2 (PGE2). Many approaches aim to optimize MSC-based therapies, including preconditioning using soluble factors and cell immobilization in biomaterials. However, optimization of MSC function is usually inefficient as only a few factors are manipulated in parallel. We utilized fractional factorial design of experiments to screen a panel of 6 molecules (lipopolysaccharide [LPS], polyinosinic-polycytidylic acid [poly(I:C)], interleukin [IL]-6, IL-1β, interferon [IFN]-β, and IFN-γ), individually and in combinations, for the upregulation of MSC PGE2 secretion and attenuation of macrophage secretion of tumor necrosis factor (TNF)-α, a pro-inflammatory molecule, by activated-MSC conditioned medium (CM). We used multivariable linear regression (MLR) and analysis of covariance to determine differences in functions of optimal factors on monolayer MSCs and alginate-encapsulated MSCs (eMSCs). The screen revealed that LPS and IL-1β potently activated monolayer MSCs to enhance PGE2 production and attenuate macrophage TNF-α. Activation by LPS and IL-1β together synergistically increased MSC PGE2, but did not synergistically reduce macrophage TNF-α. MLR and covariate analysis revealed that macrophage TNF-α was strongly dependent on the MSC activation factor, PGE2 level, and macrophage donor but not MSC culture format (monolayer versus encapsulated). The results demonstrate the feasibility and utility of using statistical approaches for higher throughput cell analysis. This approach can be extended to develop activation schemes to maximize MSC and MSC-biomaterial functions prior to transplantation to improve MSC therapies. © 2015 American Institute of Chemical Engineers.

Assessing the use of reflectance spectroscopy in determining CsCl stress in the model species Arabidopsis thaliana

DOE PAGES

Martinez, N. E.; Sharp, J. L.; Kuhne, W. W.; ...

2015-11-23

Here, reflectance spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress, and has potential applications for use in remediation. Changes in reflectance such as that due to metal stress may occur before damage is visible, and existing studies have shown that metal stress does cause changes in plant reflectance. To further investigate the potential use of reflectance spectroscopy as a method for assessing metal stress in plants, an exploratory study was conducted in which Arabidopsis thaliana plants were treated twice weekly in a laboratory setting with varying levels (0, 0.5, or 5 mMmore » (millimolar)) of caesium chloride (CsCl) solution, and reflectance spectra were collected every week for three weeks using an Analytical Spectral Devices FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view (FOV) probe at 36.8 and 66.7 cm, respectively, above the plant. Plants were harvested each week after spectra collection for determination of relative water content and chlorophyll content. A visual assessment of the plants was also conducted using point observations on a uniform grid of 81 points. A mixed-effects model analysis was conducted for each vegetation index (VI) considered to determine the effects of length of treatment, treatment level, view with which spectra were acquired, and the interactions of these terms. Two-way analyses of variance (ANOVAs) were performed on the aforementioned endpoints (e.g. chlorophyll content) to determine the significance of the effects of treatment level and length of treatment. Multiple linear regression (MLR) was used to develop a predictive model for each endpoint, considering VI acquired at each view (CP, high FOV, and low FOV). Of the 14 VI considered, 8 were included in the MLR models. Contact probe readings and FOV readings differed significantly, but FOV measurements were generally consistent at each height.« less

A CB2-Selective Cannabinoid Suppresses T-cell Activities and Increases Tregs and IL-10

PubMed Central

Robinson, Rebecca H.; Meissler, Joseph J.; Fan, Xiaoxuan; Yu, Daohai; Adler, Martin W.; Eisenstein, Toby K.

2015-01-01

We have previously shown that agonists selective for the cannabinoid receptor 2 (CB2), including O-1966, inhibit the Mixed Lymphocyte Reaction (MLR), an in vitro correlate of organ graft rejection, predominantly through effects on T-cells. Current studies explored the mechanism of this immunosuppression by O-1966 using mouse spleen cells. Treatment with O-1966 dose-relatedly decreased levels of the active nuclear forms of the transcription factors NF-κB and NFAT in wild-type T-cells, but not T-cells from CB2 knockout (CB2R k/o) mice. Additionally, a gene expression profile of purified T-cells from MLR cultures generated using a PCR T-cell activation array showed that O-1966 decreased mRNA expression of CD40 ligand and CyclinD3, and increased mRNA expression of Src-like-adaptor 2 (SLA2), Suppressor of Cytokine Signaling 5 (SOCS5), and IL-10. The increase in IL-10 was confirmed by measuring IL-10 protein levels in MLR culture supernatants. Further, an increase in the percentage of regulatory T-cells (Tregs) was observed in MLR cultures. Pretreatment with anti-IL-10 resulted in a partial reversal of the inhibition of proliferation and blocked the increase of Tregs. Additionally, O-1966 treatment caused a dose-related decrease in the expression of CD4 in MLR cultures from wild-type, but not CB2R k/o, mice. These data support the potential of CB2-selective agonists as useful therapeutic agents to prolong graft survival in transplant patients, and strengthens their potential as a new class of immunosuppressive agents with broader applicability. PMID:25980325

Transfer from blue light or green light to white light partially reverses changes in ocular refraction and anatomy of developing guinea pigs.

PubMed

Qian, Yi-Feng; Liu, Rui; Dai, Jin-Hui; Chen, Min-Jie; Zhou, Xing-Tao; Chu, Ren-Yuan

2013-09-26

Relative to the broadband white light (BL), postnatal guinea pigs develop myopia in a monochromic middle-wavelength light (ML, 530 nm) environment and develop hyperopia in a monochromic short-wavelength light (SL, 430 nm) environment. We investigated whether transfer from SL or ML to BL leads to recuperation of ocular refraction and anatomy of developing guinea pigs. Two-week-old guinea pigs were given (a) SL for 20 weeks, (b) SL recuperation (SLR, SL for 10 weeks then BL for 10 weeks), (c) ML for 20 weeks, (d) ML recuperation (MLR, ML for 10 weeks then BL for 10 weeks), or (e) BL for 20 weeks. Two weeks after transfer from ML to BL (MLR group), ocular refraction increased from 1.95 ± 0.35 D to 2.58 ± 0.24 D, and vitreous length decreased from 3.48 ± 0.06 mm to 3.41 ± 0.06 mm. Two weeks after transfer from SL to BL (SLR group), ocular refraction decreased from 5.65 ± 0.61 D to 4.33 ± 0.49 D, and vitreous length increased from 3.18 ± 0.07 mm to 3.26 ± 0.11 mm. The MLR and SLR groups had final ocular refractions that were significantly different from those of the ML and SL groups at 20 weeks (ML vs. MLR: p < 0.0001; SL vs. SLR: p < 0.0001) but were still significantly different from the BL group (BL vs. MLR: p = 0.0120; BL vs. SLR: p = 0.0010). These results suggest that recuperation was not complete after return to BL for 10 weeks.

Chemotherapy ± cetuximab modulates peripheral immune responses in metastatic colorectal cancer.

PubMed

Xynos, Ioannis D; Karadima, Maria L; Voutsas, Ioannis F; Amptoulach, Sousana; Skopelitis, Elias; Kosmas, Christos; Gritzapis, Angelos D; Tsavaris, Nikolas

2013-01-01

To identify changes in peripheral immune responses in patients with metastatic colorectal cancer (mCRC) treated with irinotecan/5-fluorouracil/leucovorin (IFL) alone or in combination with cetuximab (C-IFL). Peripheral blood mononuclear cells (PBMCs) collected from healthy donors (n = 20) and patients with mCRC receiving treatment with either IFL (n = 30) or C-IFL (n = 30) were tested for cytokine production upon polyclonal stimulation with anti-CD3 monoclonal antibody, T cell proliferation in the autologous mixed lymphocyte reaction (auto-MLR) and T regulatory cell (Treg) frequency. The respective results were evaluated over two treatment cycles and further assessed in relation to response to treatment. PBMCs prior to treatment exhibited significantly lower production of IL-2, IFN-γ, IL-12 and IL-18 cytokines and lower auto-MLR responses, whereas Treg frequency, IL-4, IL-10 cytokines were increased compared to healthy donors. During treatment, IL-2, IFN-γ, IL-12, IL-18 and auto-MLR responses increased, while Treg frequency and IL-10 secretion decreased significantly compared to the baseline. Responders to treatment exhibited a significantly higher increase in IL-2, IFN-γ, IL-12 and IL-18 production and auto-MLR responses, and higher decrease in IL-4, IL-10 secretion and Treg frequency. Among all patient subgroups analysed, responders to C-IFL demonstrated significantly higher increase in auto-MLR responses, IL-12 and IL-18 secretion and higher decrease in Treg frequency. The disturbed immune parameters observed in patients with mCRC at presentation can be significantly improved during treatment with IFL and this effect can be potentiated by the addition of cetuximab. Monitoring of the peripheral immune system function could be used as surrogate marker in predicting treatment-related outcome. Copyright © 2013 S. Karger AG, Basel.

Selecting the best tone-pip stimulus-envelope time for estimating an objective middle-latency response threshold for low- and middle-tone sensorineural hearing losses.

PubMed

Xu, Z M; De Vel, E; Vinck, B; Van Cauwenberge, P

1995-01-01

The effects of rise-fall and plateau times for the Pa component of the middle-latency response (MLR) were investigated in normally hearing subjects, and an objective MLR threshold was measured in patients with low- and middle-tone hearing losses, using a selected stimulus-envelope time. Our results showed that the stimulus-envelope time (the rise-fall time and plateau time groups) affected the Pa component of the MLR (quality was determined by the (chi 2-test and amplitude by the F-test). The 4-2-4 tone-pips produced good Pa quality by visual inspection. However, our data revealed no statistically significant Na-Pa amplitude differences between the two subgroups studied when comparing the 2- and 4-ms rise-fall times and the 0- and 2-ms plateau times. In contrast, Na-Pa became significantly smaller from the 4-ms to the 6-ms rise-fall time and from the 2-ms to the 4-ms plateau time (paired t-test). This result allowed us to select the 2- or 4-ms rise-fall time and the 0- or 2-ms plateau time without influencing amplitude. Analysis of the stimulus spectral characteristics demonstrated that a rise-fall time of at least 2ms could prevent spectral splatter and indicated that a stimulus with a 5-ms rise-fall time had a greater frequency-specificity than a stimulus of 2-ms rise-fall time. When considering the synchronous discharge and frequency-specificity of MLR, our findings show that a rise-fall time of four periods with a plateau of two periods is an acceptable compromise for estimating the objective MLR threshold.(ABSTRACT TRUNCATED AT 250 WORDS)

Bouncing on Mars and the Moon-the role of gravity on neuromuscular control: correlation of muscle activity and rate of force development.

PubMed

Ritzmann, Ramona; Freyler, Kathrin; Krause, Anne; Gollhofer, Albert

2016-11-01

On our astronomical neighbors Mars and the Moon, bouncing movements are the preferred locomotor techniques. During bouncing, the stretch-shortening cycle describes the muscular activation pattern. This study aimed to identify gravity-dependent changes in kinematic and neuromuscular characteristics in the stretch-shortening cycle. Hence, neuromuscular control of limb muscles as well as correlations between the muscles' pre-activation, reflex components, and force output were assessed in lunar, Martian, and Earth gravity. During parabolic flights, peak force (F max ), ground-contact-time, rate of force development (RFD), height, and impulse were measured. Electromyographic (EMG) activities in the m. soleus (SOL) and gastrocnemius medialis (GM) were assessed before (PRE) and during bounces for the reflex phases short-, medium-, and long-latency response (SLR, MLR, LLR). With gradually decreasing gravitation, F max , RFD, and impulse were reduced, whereas ground-contact time and height increased. Concomitantly, EMG_GM decreased for PRE, SLR, MLR, and LLR, and in EMG_SOL in SLR, MLR, and LLR. For SLR and MLR, F max and RFD were positively correlated to EMG_SOL. For PRE and LLR, RFD and F max were positively correlated to EMG_GM. Findings emphasize that biomechanically relevant kinematic adaptations in response to gravity variation were accompanied by muscle- and phase-specific modulations in neural control. Gravitational variation is anticipated and compensated for by gravity-adjusted muscle activities. Importantly, the pre-activation and reflex phases were differently affected: in SLR and MLR, SOL is assumed to contribute to the decline in force output with a decreasing load, and, complementary in PRE and LLR, GM seems to be of major importance for force generation. Copyright © 2016 the American Physiological Society.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models.

PubMed

Teng, Hongfen; Liang, Zongzheng; Chen, Songchao; Liu, Yong; Viscarra Rossel, Raphael A; Chappell, Adrian; Yu, Wu; Shi, Zhou

2018-04-18

Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76tha -1 y -1 , which is equivalent to an annual soil loss of 559.59×10 6 t. Our 2050 projections suggested that erosion on the TP will increase to 3.17tha -1 y -1 and 3.91tha -1 y -1 under conditions represented by RCP2.6 and RCP8.5, respectively. The current assessment and future prediction of soil erosion by water on the TP should be valuable for environment protection and soil conservation in this unique region and elsewhere. Copyright © 2018 Elsevier B.V. All rights reserved.

The contribution of atmospheric proxies to the vertical distribution of ozone over Summit Station, Greenland

NASA Astrophysics Data System (ADS)

Bahramvash Shams, S.; Walden, V. P.; Oltmans, S. J.; Petropavlovskikh, I. V.; Kivi, R.; Thölix, L.

2017-12-01

The current trend and future concentrations of atmospheric ozone are active areas of research as the effect of the Montreal Protocol is realized. The trend of ozone is due to various chemical and dynamical parameters that create, destroy, and transport atmospheric ozone. These important parameters can be represented by different proxies, but their effects on ozone concentration are not completely understood. Previous studies show that proxies related to ozone have different contributions depending on latitude and altitude. In this study, we use vertical profiles of ozone derived from ozonesondes launched by the NOAA Global Monitoring Division at Summit Station, Greenland from 2005 to 2016. The effects of different proxies on ozone are investigated. Summit Station is located at 3,200 meters above sea level on the Greenland Ice Sheet and is a unique place in the Arctic. We use a stepwise multiple regression (MLR) technique to remove the seasonal cycle of ozone and investigate how the different proxies [solar flux (SF), the Quasi-Biennial Oscillation (QBO), the El Nino-Southern Oscillation index (ENSO), the Arctic Oscillation (AO), eddy heat flux (EHF), the volume of polar stratospheric clouds (VPSC), equivalent latitude (EL), and the tropopause pressure (TP)] affect the vertical distribution of ozone over Summit. The MLR is applied separately to total column ozone (TCO) as well as partial ozone columns (PCO) in the troposphere and the lower, middle, and upper stratosphere. Our results show that dynamical processes are important contributors to ozone concentrations over Summit Station. Tropospheric pressure and the QBO are effective predictors of ozone in the troposphere, lower and middle stratosphere, and to the TCO. The VPSC is an important contributor to changes in ozone in the middle stratosphere. AO explains part of low/mid stratospheric and TCO ozone cycle. A simulation model of ozone over Summit built from the MLR results explains the seasonal cycle and the trends in TCO over Summit with a correlation coefficient (R2) of 82% for TCO. Simulations of PCO in the lower and middle stratosphere range from R2 = 62% to 85%.

Reinventing the Wheel: Impact of Prolonged Antibiotic Exposure on Multi-Drug Resistant Ventilator-Associated Pneumonia in Trauma Patients.

PubMed

Lewis, Richard H; Sharpe, John P; Swanson, Joseph M; Fabian, Timothy C; Croce, Martin A; Magnotti, Louis J

2018-04-16

Multi-drug resistant (MDR) strains of both Acinetobacter baumannii (AB) and Pseudomonas aeruginosa (PA) as causative VAP pathogens are becoming increasingly common. Still, the risk factors associated with this increased resistance have yet to be elucidated. The purpose of this study was to examine the changing sensitivity patterns of these pathogens over time and determine which risk factors predict MDR in trauma patients with VAP. Patients with either AB or PA VAP over 10 years were stratified by pathogen sensitivity (sensitive (SEN) and MDR), age, severity of shock and injury severity. Prophylactic and empiric antibiotic days, risk factors for severe VAP and mortality were compared. Multivariable logistic regression (MLR) was performed to determine which risk factors were independent predictors of MDR. 397 patients were identified with AB or PA VAP. There were 173 episodes of AB (91 SEN and 82 MDR) and 224 episodes of PA (170 SEN and 54 MDR). The incidence of MDR VAP did not change over the study (p=0.633). Groups were clinically similar with the exception of 24-hour transfusions (14 vs 19 units, p = 0.009) and extremity AIS (1 vs 3, p<0.001), both significantly increased in the MDR group. Antibiotic exposure as well as mIEAT (63% vs 81%, p<0.001) were significantly increased in the MDR group. MLR identified prophylactic antibiotic days (OR 23.1; 95%CI 16.7-28, p<0.001) and mIEAT (OR 18.1; 95%CI 12.2-26.1, p=0.001) as independent predictors of MDR after adjusting for severity of shock, injury severity, severity of VAP and antibiotic exposure. Prolonged exposure to unnecessary antibiotics remains one of the strongest predictors for the development of antibiotic resistance. MLR identified prophylactic antibiotic days and mIEAT an independent risk factors for MDR VAP. Thus, limiting prophylactic antibiotic days is the only potentially modifiable risk factor for the development of MDR VAP in trauma patients. Level III, Prognostic LEVEL OF EVIDENCE: Multi-drug resistant, antibiotic exposure.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

PubMed

Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

2017-04-01

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R training 2 and R test 2 , respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R training 2 and R test 2 , respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R training 2 and R test 2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615. Copyright © 2016 Elsevier Ltd. All rights reserved.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

Development of a Highly Automated and Multiplexed Targeted Proteome Pipeline and Assay for 112 Rat Brain Synaptic Proteins

PubMed Central

Colangelo, Christopher M.; Ivosev, Gordana; Chung, Lisa; Abbott, Thomas; Shifman, Mark; Sakaue, Fumika; Cox, David; Kitchen, Rob R.; Burton, Lyle; Tate, Stephen A; Gulcicek, Erol; Bonner, Ron; Rinehart, Jesse; Nairn, Angus C.; Williams, Kenneth R.

2015-01-01

We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling (xMRM) that improves Signal/Noise by >2-fold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, Normalized Group Area Ratio (NGAR), MLR normalization, weighted regression analysis, and data dissemination through the Yale Protein Expression Database. As a proof of principle we developed a robust 90 minute LC-MRM assay for Mouse/Rat Post-Synaptic Density (PSD) fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our first method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay. PMID:25476245

A Quantum Chemical and Statistical Study of Phenolic Schiff Bases with Antioxidant Activity against DPPH Free Radical

PubMed Central

Anouar, El Hassane

2014-01-01

Phenolic Schiff bases are known as powerful antioxidants. To select the electronic, 2D and 3D descriptors responsible for the free radical scavenging ability of a series of 30 phenolic Schiff bases, a set of molecular descriptors were calculated by using B3P86 (Becke’s three parameter hybrid functional with Perdew 86 correlation functional) combined with 6-31 + G(d,p) basis set (i.e., at the B3P86/6-31 + G(d,p) level of theory). The chemometric methods, simple and multiple linear regressions (SLR and MLR), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce the dimensionality and to investigate the relationship between the calculated descriptors and the antioxidant activity. The results showed that the antioxidant activity mainly depends on the first and second bond dissociation enthalpies of phenolic hydroxyl groups, the dipole moment and the hydrophobicity descriptors. The antioxidant activity is inversely proportional to the main descriptors. The selected descriptors discriminate the Schiff bases into active and inactive antioxidants. PMID:26784873

Estimating future burned areas under changing climate in the EU-Mediterranean countries.

PubMed

Amatulli, Giuseppe; Camia, Andrea; San-Miguel-Ayanz, Jesús

2013-04-15

The impacts of climate change on forest fires have received increased attention in recent years at both continental and local scales. It is widely recognized that weather plays a key role in extreme fire situations. It is therefore of great interest to analyze projected changes in fire danger under climate change scenarios and to assess the consequent impacts of forest fires. In this study we estimated burned areas in the European Mediterranean (EU-Med) countries under past and future climate conditions. Historical (1985-2004) monthly burned areas in EU-Med countries were modeled by using the Canadian Fire Weather Index (CFWI). Monthly averages of the CFWI sub-indices were used as explanatory variables to estimate the monthly burned areas in each of the five most affected countries in Europe using three different modeling approaches (Multiple Linear Regression - MLR, Random Forest - RF, Multivariate Adaptive Regression Splines - MARS). MARS outperformed the other methods. Regression equations and significant coefficients of determination were obtained, although there were noticeable differences from country to country. Climatic conditions at the end of the 21st Century were simulated using results from the runs of the regional climate model HIRHAM in the European project PRUDENCE, considering two IPCC SRES scenarios (A2-B2). The MARS models were applied to both scenarios resulting in projected burned areas in each country and in the EU-Med region. Results showed that significant increases, 66% and 140% of the total burned area, can be expected in the EU-Med region under the A2 and B2 scenarios, respectively. Copyright © 2013 Elsevier B.V. All rights reserved.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

PubMed

Heddam, Salim; Kisi, Ozgur

2017-07-01

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Estimating the impact of the medical loss ratio rule: a state-by-state analysis.

PubMed

Hall, Mark A; McCue, Michael J

2012-04-01

One of the most visible consumer protections in the Patient Protection and Affordable Care Act is the requirement that health insurers pay out at least 80 percent to 85 percent of premium dollars for medical care expenses. Insurers that pay out less than this minimum "medical loss ratio" (MLR) must rebate the difference to their policyholders, starting in 2011. Using insurers' MLR data from 2010, this issue brief estimates the rebates expected in each state if the new rules had been in effect a year earlier. Nationally, consumers would have received almost $2 billion of rebates if the new MLR rules had been in effect in 2010. Almost $1 billion would be in the individual market, where rebates would go to 5.3 million people nationally. Another $1 billion would go to policies covering about 10 million people in the small- and large-group markets.

Azathioprine suppresses the mixed lymphocyte reaction of patients with Lesch-Nyhan syndrome.

PubMed Central

Szawlowski, P W; Al-Safi, S A; Dooley, T; Maddocks, J L

1985-01-01

The mixed lymphocyte reaction of Lesch-Nyhan patients (HGPRT deficient) was used to study the immunosuppressive effects of azathioprine and 6-mercaptopurine (6-MP). Mitogen stimulated lymphocytes of these patients are highly resistant to azathioprine and 6-MP. When both stimulator and responder lymphocytes in the MLR were HGPRT deficient, azathioprine (36 microM) was much more inhibitory than 6-MP (100 microM). Azathioprine produced inhibition of 98.2% and 78.5% compared with the values of 63.9% and 30.6% for 6-MP. The difference in inhibitory activity between azathioprine and 6-MP was reduced when normal stimulator lymphocytes were cultured with HGPRT deficient responder lymphocytes in the MLR. These results provide very strong evidence that the nucleotide metabolites of azathioprine and 6-MP are unnecessary for immunosuppression. They also suggest that azathioprine and 6-MP interfere with antigenic triggering of the MLR. PMID:2934083

76 FR 36857 - Federal Employees Health Benefits Program: New Premium Rating Method for Most Community Rated Plans

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-23

..., beginning in calendar year 2011, meet an MLR of 85% for large groups, (i.e., non-claim costs may not exceed... groups subsidize the less healthy groups that use more health services. This subsidization is by design... shall be equivalent to the subscription rates given to the carrier's similarly sized subscriber groups...

Multivariate analysis of the heterogeneous geochemical processes controlling arsenic enrichment in a shallow groundwater system.

PubMed

Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin

2014-01-01

The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file.

Association of Toll-Like Receptor 4 Polymorphisms with Diabetic Foot Ulcers and Application of Artificial Neural Network in DFU Risk Assessment in Type 2 Diabetes Patients

PubMed Central

Singh, Kanhaiya; Agrawal, Neeraj K.; Gupta, Sanjeev K.

2013-01-01

The Toll-Like receptor 4 (TLR4) plays an important role in immunity, tissue repair, and regeneration. The objective of the present work was to evaluate the association of TLR4 single nucleotide polymorphisms (SNPs) rs4986790, rs4986791, rs11536858 (merged into rs10759931), rs1927911, and rs1927914 with increased diabetic foot ulcer (DFU) risk in patients with type 2 diabetes mellitus (T2DM). PCR-RFLP was used for genotyping TLR4 SNPs in 125 T2DM patients with DFU and 130 controls. The haplotypes and linkage disequilibrium between the SNPs were determined using Haploview software. Multivariate linear regression (MLR) and artificial neural network (ANN) modeling was done to observe their predictability for the risk of DFU in T2DM patients. Risk genotypes of all SNPs except rs1927914 were significantly associated with DFU. Haplotype ACATC (P value = 9.3E − 5) showed strong association with DFU risk. Two haplotypes ATATC (P value = 0.0119) and ATGTT (P value = 0.0087) were found to be protective against DFU. In conclusion TLR4 SNPs and their haplotypes may increase the risk of impairment of wound healing in T2DM patients. ANN model (83%) is found to be better than the MLR model (76%) and can be used as a tool for the DFU risk assessment in T2DM patients. PMID:23936790

An intercomparison of multidecadal observational and reanalysis data sets for global total ozone trends and variability analysis

NASA Astrophysics Data System (ADS)

Bai, Kaixu; Chang, Ni-Bin; Shi, Runhe; Yu, Huijia; Gao, Wei

2017-07-01

A four-step adaptive ozone trend estimation scheme is proposed by integrating multivariate linear regression (MLR) and ensemble empirical mode decomposition (EEMD) to analyze the long-term variability of total column ozone from a set of four observational and reanalysis total ozone data sets, including the rarely explored ERA-Interim total ozone reanalysis, from 1979 to 2009. Consistency among the four data sets was first assessed, indicating a mean relative difference of 1% and root-mean-square error around 2% on average, with respect to collocated ground-based total ozone observations. Nevertheless, large drifts with significant spatiotemporal inhomogeneity were diagnosed in ERA-Interim after 1995. To emphasize long-term trends, natural ozone variations associated with the solar cycle, quasi-biennial oscillation, volcanic aerosols, and El Niño-Southern Oscillation were modeled with MLR and then removed from each total ozone record, respectively, before performing EEMD analyses. The resulting rates of change estimated from the proposed scheme captured the long-term ozone variability well, with an inflection time of 2000 clearly detected. The positive rates of change after 2000 suggest that the ozone layer seems to be on a healing path, but the results are still inadequate to conclude an actual recovery of the ozone layer, and more observational evidence is needed. Further investigations suggest that biases embedded in total ozone records may significantly impact ozone trend estimations by resulting in large uncertainty or even negative rates of change after 2000.

Multibaseline gravitational wave radiometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Talukder, Dipongkar; Bose, Sukanta; Mitra, Sanjit

2011-03-15

We present a statistic for the detection of stochastic gravitational wave backgrounds (SGWBs) using radiometry with a network of multiple baselines. We also quantitatively compare the sensitivities of existing baselines and their network to SGWBs. We assess how the measurement accuracy of signal parameters, e.g., the sky position of a localized source, can improve when using a network of baselines, as compared to any of the single participating baselines. The search statistic itself is derived from the likelihood ratio of the cross correlation of the data across all possible baselines in a detector network and is optimal in Gaussian noise.more » Specifically, it is the likelihood ratio maximized over the strength of the SGWB and is called the maximized-likelihood ratio (MLR). One of the main advantages of using the MLR over past search strategies for inferring the presence or absence of a signal is that the former does not require the deconvolution of the cross correlation statistic. Therefore, it does not suffer from errors inherent to the deconvolution procedure and is especially useful for detecting weak sources. In the limit of a single baseline, it reduces to the detection statistic studied by Ballmer [Classical Quantum Gravity 23, S179 (2006).] and Mitra et al.[Phys. Rev. D 77, 042002 (2008).]. Unlike past studies, here the MLR statistic enables us to compare quantitatively the performances of a variety of baselines searching for a SGWB signal in (simulated) data. Although we use simulated noise and SGWB signals for making these comparisons, our method can be straightforwardly applied on real data.« less

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Is the medical loss ratio a good target measure for regulation in the individual market for health insurance?

PubMed

Karaca-Mandic, Pinar; Abraham, Jean M; Simon, Kosali

2015-01-01

Effective January 1, 2011, individual market health insurers must meet a minimum medical loss ratio (MLR) of 80%. This law aims to encourage 'productive' forms of competition by increasing the proportion of premium dollars spent on clinical benefits. To date, very little is known about the performance of firms in the individual health insurance market, including how MLRs are related to insurer and market characteristics. The MLR comprises one component of the price-cost margin, a traditional gauge of market power; the other component is percent of premiums spent on administrative expenses. We use data from the National Association of Insurance Commissioners (2001-2009) to evaluate whether the MLR is a good target measure for regulation by comparing the two components of the price-cost margin between markets that are more competitive versus those that are not, accounting for firm and market characteristics. We find that insurers with monopoly power have lower MLRs. Moreover, we find no evidence suggesting that insurers' administrative expenses are lower in more concentrated insurance markets. Thus, our results are largely consistent with the interpretation that the MLR could serve as a target measure of market power in regulating the individual market for health insurance but with notable limited ability to capture product and firm heterogeneity. Copyright © 2013 John Wiley & Sons, Ltd.

Pathophysiology Associated with Traumatic Brain Injury: Current Treatments and Potential Novel Therapeutics.

PubMed

Pearn, Matthew L; Niesman, Ingrid R; Egawa, Junji; Sawada, Atsushi; Almenar-Queralt, Angels; Shah, Sameer B; Duckworth, Josh L; Head, Brian P

2017-05-01

Traumatic brain injury (TBI) is one of the leading causes of death of young people in the developed world. In the United States alone, 1.7 million traumatic events occur annually accounting for 50,000 deaths. The etiology of TBI includes traffic accidents, falls, gunshot wounds, sports, and combat-related events. TBI severity ranges from mild to severe. TBI can induce subtle changes in molecular signaling, alterations in cellular structure and function, and/or primary tissue injury, such as contusion, hemorrhage, and diffuse axonal injury. TBI results in blood-brain barrier (BBB) damage and leakage, which allows for increased extravasation of immune cells (i.e., increased neuroinflammation). BBB dysfunction and impaired homeostasis contribute to secondary injury that occurs from hours to days to months after the initial trauma. This delayed nature of the secondary injury suggests a potential therapeutic window. The focus of this article is on the (1) pathophysiology of TBI and (2) potential therapies that include biologics (stem cells, gene therapy, peptides), pharmacological (anti-inflammatory, antiepileptic, progrowth), and noninvasive (exercise, transcranial magnetic stimulation). In final, the review briefly discusses membrane/lipid rafts (MLR) and the MLR-associated protein caveolin (Cav). Interventions that increase Cav-1, MLR formation, and MLR recruitment of growth-promoting signaling components may augment the efficacy of pharmacologic agents or already existing endogenous neurotransmitters and neurotrophins that converge upon progrowth signaling cascades resulting in improved neuronal function after injury.

Regulating the medical loss ratio: implications for the individual market.

PubMed

Abraham, Jean M; Karaca-Mandic, Pinar

2011-03-01

To provide state-level estimates of the size and structure of the US individual market for health insurance and to investigate the potential impact of new medical loss ratio (MLR) regulation in 2011, as indicated by the Patient Protection and Affordable Care Act (PPACA). Using data from the National Association of Insurance Commissioners, we provided state-level estimates of the size and structure of the US individual market from 2002 to 2009. We estimated the number of insurers expected to have MLRs below the legislated minimum and their corresponding enrollment. In the case of noncompliant insurers exiting the market, we estimated the number of enrollees that may be vulnerable to major coverage disruption given poor health status. In 2009, using a PPACA-adjusted MLR definition, we estimated that 29% of insurer-state observations in the individual market would have MLRs below the 80% minimum, corresponding to 32% of total enrollment. Nine states would have at least one-half of their health insurers below the threshold. If insurers below the MLR threshold exit the market, major coverage disruption could occur for those in poor health; we estimated the range to be between 104,624 and 158,736 member-years. The introduction of MLR regulation as part of the PPACA has the potential to significantly affect the functioning of the individual market for health insurance.

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.

PubMed

Heddam, Salim

2014-01-01

In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling.

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

NASA Astrophysics Data System (ADS)

Gramatica, Paola

This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

Six years' experience of tolerance induction in renal transplantation using stem cell therapy.

PubMed

Vanikar, Aruna V; Trivedi, Hargovind L; Thakkar, Umang G

2018-02-01

Tolerance induction (TI) has been attempted with chimerism/clonal deletion. We report results of TI protocol (TIP) using stem cell therapy (SCT) included adipose derived mesenchymal stem cells (AD-MSC) and hematopoietic stem cells (HSC) in 10 living-donor related renal transplantation (LDRT) patients under non-myeloablative conditioning with Bortezomib, Methylprednisone, rabbit-anti-thymoglobulin and Rituximab, without using conventional immunosuppression. Transplantation was performed following acceptable lymphocyte cross-match, flow cross-match, single antigen assay and negative mixed lymphocyte reaction (MLR). Monitoring included serum creatinine (SCr), donor specific antibodies (DSA) and MLR. Protocol biopsies were planned after 100days and yearly in willing patients. Rescue immunosuppression was planned for rejection/DSA/positive MLR. Over mean 6±0.37year follow-up patient survival was 80% and death-censored graft survival was 90%. Mean SCr was 1.44±0.41mg/dL. This is the first clinical report of sustained TI in LDRT for 6years using SCT. Copyright © 2017 Elsevier Inc. All rights reserved.

A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Igglessi-Markopoulou, Olga; Kollias, George

2010-05-01

A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R(LOO2)) = 0.67) and validation through an external test set (R(pred2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.

Effects of analog and digital filtering on auditory middle latency responses in adults and young children.

PubMed

Suzuki, T; Hirabayashi, M; Kobayashi, K

1984-01-01

Effects of analog high pass (HP) filtering were compared with those of zero phase-shift digital filtering on the auditory middle latency responses (MLR) from nine adults and 16 young children with normal hearing. Analog HP filtering exerted several prominent effects on the MLR waveforms in both adults and young children, such as suppression of Po (ABR), enhancement of Nb, enhancement or emergence of Pb, and latency decrements for Pa and the later components. Analog HP filtering at 20 Hz produced more pronounced waveform distortions in the responses from young children than from adults. Much greater latency decrements for Pa and Nb were observed for young children than for adults in the analog HP-filtered responses at 20 Hz. A large positive peak (Pb) emerged at about 65 ms after the stimulus onset. From these results, the use of digital HP filtering at 20 Hz is strongly recommended for obtaining unbiased and stable MLR in young children.

Immunosuppressive Effects of Bryoria sp. (Lichen-Forming Fungus) Extracts via Inhibition of CD8+ T-Cell Proliferation and IL-2 Production in CD4+ T Cells.

PubMed

Hwang, Yun-Ho; Lee, Sung-Ju; Kang, Kyung-Yun; Hur, Jae-Seoun; Yee, Sung-Tae

2017-06-28

Lichen-forming fungi are known to have various biological activities, such as antioxidant, antimicrobial, antitumor, antiviral, anti-inflammation, and anti proliferative effects. However, the immunosuppressive effects of Bryoria sp. extract (BSE) have not previously been investigated. In this study, the inhibitory activity of BSE on the proliferation of CD8 + T cells and the mixed lymphocytes reaction (MLR) was evaluated in vitro. BSE was non-toxic in spleen cells and suppressed the growth of splenocytes induced by anti-CD3. The suppressed cell population in spleen cells consisted of CD8 + T cells and their proliferation was inhibited by the treatment with BSE. This extract significantly suppressed the IL-2 associated with T cell growth and IFN-γ as the CD8 + T cell marker. Furthermore, BSE reduced the expression of the IL-2 receptor alpha chain (IL-2Rα) on CD8 + T cells and CD86 on dendritic cells by acting as antigen-presenting cells. Finally, the MLR produced by the co-culture of C57BL/6 and MMC-treated BALB/c was suppressed by BSE. IL-2, IFN-γ, and CD69 on CD8 + T cells in MLR condition were inhibited by BSE. These results indicate that BSE inhibits the MLR via the suppression of IL-2Rα expression in CD8 + T cells. BSE has the potential to be developed as an anti-immunosuppression agent for organ transplants.

Mapping SOC content and bulk density of a disturbed peatland relict with electromagnetic induction and DEM data

NASA Astrophysics Data System (ADS)

Altdorff, Daniel; Bechtold, Michel; van der Kruk, Jan; Tiemeyer, Bärbel; von Hebel, Christian; Huisman, Johan Alexander

2014-05-01

Peatlands represent a huge storage of soil organic carbon (SOC), and there is considerable interest to assess the total amount of carbon stored in these ecosystems. However, reliable field-scale information about peat properties, particularly SOC content and bulk density (BD) necessary to estimate C stocks, remains difficult to obtain. A potential way to acquire information on these properties and its spatial variation is the non-invasive mapping of easily recordable physical variables that correlate with peat properties, such as bulk electrical conductivity (ECa) measured with electromagnetic induction (EMI). However, ECa depends on a range of soil properties, including BD, soil and water chemistry, and water content, and thus results often show complex and site-specific relationships. Therefore, a reliable prediction of SOC and BD from ECa data is not necessarily given. In this study, we aim to explore the usefulness of Multiple Linear Regression (MLR) models to predict the peat soil properties SOC and BD from multi-offset EMI and high-resolution DEM data. The quality of the MLR models is assessed by cross-validation. We use data from a medium-scale disturbed peat relict (approximately 35ha) in Northern Germany. The potential explanatory variables considered in MLR were: EMI data of six different integral depths (approximately 0.25, 0.5, 0.6, 0.9, 1, and 1.80 m), their vertical heterogeneity, as well as several topographical variables extracted from the DEM. Ground truth information for SOC, BD content and peat layer thickness was obtained from 34 soil cores of 1 m depth. Each core was divided into several 5 to 20 cm thick layers so that integral information of the upper 0.25, 0.5, and 1 m as well as from the total peat layer was obtained. For cross-validation of results, we clustered the 34 soil cores into 4 classes using K-means clustering and selected 8 cores for validation from the clusters with a probability that depended on the size of the cluster. With the remaining 26 samples, we performed a stepwise MLR and generated separate models for each depth and soil property. Preliminary results indicate reliable model predictions for SOC and BD (R² = 0.83- 0.95). The RMSE values of the validation ranged between 3.5 and 7.2 vol. % for SOC and 0.13 and 0.37 g/cm³ for BD for the independent samples. This equates roughly the quality of SOC predictions obtained by field application of vis-NIR (visible-near infrared) presented in literature for a similar peatland setting. However, the EMI approach offers the potential to derive information from deeper depths and allows non-invasive mapping of BD variability, which is not possible with vis-NIR. Therefore, this new approach potentially provides a more useful tool for total carbon stock assessment in peatlands.

Hydrogel coated monoliths for enzymatic hydrolysis of penicillin G

PubMed Central

Smeltink, M. W.; Straathof, A. J. J.; Paasman, M. A.; van de Sandt, E. J. A. X.; Kapteijn, F.; Moulijn, J. A.

2008-01-01

The objective of this work was to develop a hydrogel-coated monolith for the entrapment of penicillin G acylase (E. coli, PGA). After screening of different hydrogels, chitosan was chosen as the carrier material for the preparation of monolithic biocatalysts. This protocol leads to active immobilized biocatalysts for the enzymatic hydrolysis of penicillin G (PenG). The monolithic biocatalyst was tested in a monolith loop reactor (MLR) and compared with conventional reactor systems using free PGA, and a commercially available immobilized PGA. The optimal immobilization protocol was found to be 5 g l−1 PGA, 1% chitosan, 1.1% glutaraldehyde and pH 7. Final PGA loading on glass plates was 29 mg ml−1 gel. For 400 cpsi monoliths, the final PGA loading on functionalized monoliths was 36 mg ml−1 gel. The observed volumetric reaction rate in the MLR was 0.79 mol s−1 m−3monolith. Apart from an initial drop in activity due to wash out of PGA at higher ionic strength, no decrease in activity was observed after five subsequent activity test runs. The storage stability of the biocatalysts is at least a month without loss of activity. Although the monolithic biocatalyst as used in the MLR is still outperformed by the current industrial catalyst (immobilized preparation of PGA, 4.5 mol s−1 m−3catalyst), the rate per gel volume is slightly higher for monolithic catalysts. Good activity and improved mechanical strength make the monolithic bioreactor an interesting alternative that deserves further investigation for this application. Although moderate internal diffusion limitations have been observed inside the gel beads and in the gel layer on the monolith channel, this is not the main reason for the large differences in reactor performance that were observed. The pH drop over the reactor as a result of the chosen method for pH control results in a decreased performance of both the MLR and the packed bed reactor compared to the batch system. A different reactor configuration including an optimal pH profile is required to increase the reactor performance. The monolithic stirrer reactor would be an interesting alternative to improve the performance of the monolith-PGA combination. PMID:18427849

Application of ant colony optimization in development of models for prediction of anti-HIV-1 activity of HEPT derivatives.

PubMed

Zare-Shahabadi, Vali; Abbasitabar, Fatemeh

2010-09-01

Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88). Copyright 2010 Wiley Periodicals, Inc.

Characterization of skin reactions and pain reported by patients receiving radiation therapy for cancer at different sites.

PubMed

Gewandter, Jennifer S; Walker, Joanna; Heckler, Charles E; Morrow, Gary R; Ryan, Julie L

2013-12-01

Skin reactions and pain are commonly reported side effects of radiation therapy (RT). To characterize RT-induced symptoms according to treatment site subgroups and identify skin symptoms that correlate with pain. A self-report survey-adapted from the MD Anderson Symptom Inventory and the McGill Pain Questionnaire--assessed RT-induced skin problems, pain, and specific skin symptoms. Wilcoxon Sign Ranked tests compared mean severity or pre- and post-RT pain and skin problems within each RT-site subgroup. Multiple linear regression (MLR) investigated associations between skin symptoms and pain. Survey respondents (N = 106) were 58% female and on average 64 years old. RT sites included lung, breast, lower abdomen, head/neck/brain, and upper abdomen. Only patients receiving breast RT reported significant increases in treatment site pain and skin problems (P < or = .007). Patients receiving head/neck/brain RT reported increased skin problems (P < .0009). MLR showed that post-RT skin tenderness and tightness were most strongly associated with post-RT pain (P = .066 and P = .122, respectively). Small sample size, exploratory analyses, and nonvalidated measure. Only patients receiving breast RT reported significant increases in pain and skin problems at the RT site while patients receiving head/neck/brain RT had increased skin problems but not pain. These findings suggest that the severity of skin problems is not the only factor that contributes to pain and that interventions should be tailored to specifically target pain at the RT site, possibly by targeting tenderness and tightness. These findings should be confirmed in a larger sampling of RT patients.

First measurements of ambient aerosol over an ecologically sensitive zone in Central India: Relationships between PM2.5 mass, its optical properties, and meteorology.

PubMed

Sunder Raman, Ramya; Kumar, Samresh

2016-04-15

PM2.5 mass and its optical properties were measured over an ecologically sensitive zone in Central India between January and December, 2012. Meteorological parameters including temperature, relative humidity, wind speed, wind direction, and barometric pressure were also monitored. During the study period, the PM2.5 (fine PM) concentration ranged between 3.2μgm(-3) and 193.9μgm(-3) with a median concentration of 31.4μgm(-3). The attenuation coefficients, βATN at 370nm, 550nm, and 880nm had median values of 104.5Mm(-1), 79.2Mm(-1), and 59.8Mm(-1), respectively. Further, the dry scattering coefficient, βSCAT at 550nm had a median value of 17.1Mm(-1) while the absorption coefficient βABS at 550nm had a median value of 61.2Mm(-1). The relationship between fine PM mass and attenuation coefficients showed pronounced seasonality. Scattering, absorption, and attenuation coefficient at different wavelengths were all well correlated with fine PM mass only during the post-monsoon season (October, November, and December). The highest correlation (r(2)=0.81) was between fine PM mass and βSCAT at 550nm during post-monsoon season. During this season, the mass scattering efficiency (σSCAT) was 1.44m(2)g(-1). Thus, monitoring optical properties all year round, as a surrogate for fine PM mass was found unsuitable for the study location. In order to assess the relationships between fine PM mass and its optical properties and meteorological parameters, multiple linear regression (MLR) models were fitted for each season, with fine PM mass as the dependent variable. Such a model fitted for the post-monsoon season explained over 88% of the variability in fine PM mass. However, the MLR models were able to explain only 31 and 32% of the variability in fine PM during pre-monsoon (March, April, and May) and monsoon (June, July, August, and September) seasons, respectively. During the winter (January and February) season, the MLR model explained 54% of the PM2.5 variability. Copyright © 2016 Elsevier B.V. All rights reserved.

Linear solvation energy relationship for the adsorption of synthetic organic compounds on single-walled carbon nanotubes in water.

PubMed

Ding, H; Chen, C; Zhang, X

2016-01-01

The linear solvation energy relationship (LSER) was applied to predict the adsorption coefficient (K) of synthetic organic compounds (SOCs) on single-walled carbon nanotubes (SWCNTs). A total of 40 log K values were used to develop and validate the LSER model. The adsorption data for 34 SOCs were collected from 13 published articles and the other six were obtained in our experiment. The optimal model composed of four descriptors was developed by a stepwise multiple linear regression (MLR) method. The adjusted r(2) (r(2)adj) and root mean square error (RMSE) were 0.84 and 0.49, respectively, indicating good fitness. The leave-one-out cross-validation Q(2) ([Formula: see text]) was 0.79, suggesting the robustness of the model was satisfactory. The external Q(2) ([Formula: see text]) and RMSE (RMSEext) were 0.72 and 0.50, respectively, showing the model's strong predictive ability. Hydrogen bond donating interaction (bB) and cavity formation and dispersion interactions (vV) stood out as the two most influential factors controlling the adsorption of SOCs onto SWCNTs. The equilibrium concentration would affect the fitness and predictive ability of the model, while the coefficients varied slightly.

QSAR analysis for nano-sized layered manganese-calcium oxide in water oxidation: An application of chemometric methods in artificial photosynthesis.

PubMed

Shahbazy, Mohammad; Kompany-Zareh, Mohsen; Najafpour, Mohammad Mahdi

2015-11-01

Water oxidation is among the most important reactions in artificial photosynthesis, and nano-sized layered manganese-calcium oxides are efficient catalysts toward this reaction. Herein, a quantitative structure-activity relationship (QSAR) model was constructed to predict the catalytic activities of twenty manganese-calcium oxides toward water oxidation using multiple linear regression (MLR) and genetic algorithm (GA) for multivariate calibration and feature selection, respectively. Although there are eight controlled parameters during synthesizing of the desired catalysts including ripening time, temperature, manganese content, calcium content, potassium content, the ratio of calcium:manganese, the average manganese oxidation state and the surface of catalyst, by using GA only three of them (potassium content, the ratio of calcium:manganese and the average manganese oxidation state) were selected as the most effective parameters on catalytic activities of these compounds. The model's accuracy criteria such as R(2)test and Q(2)test in order to predict catalytic rate for external test set experiments; were equal to 0.941 and 0.906, respectively. Therefore, model reveals acceptable capability to anticipate the catalytic activity. Copyright © 2015 Elsevier B.V. All rights reserved.

Risk factors of neonatal mortality and child mortality in Bangladesh

PubMed Central

Maniruzzaman, Md; Suri, Harman S; Kumar, Nishith; Abedin, Md Menhazul; Rahman, Md Jahanur; El-Baz, Ayman; Bhoot, Makrand; Teji, Jagjit S; Suri, Jasjit S

2018-01-01

Background Child and neonatal mortality is a serious problem in Bangladesh. The main objective of this study was to determine the most significant socio-economic factors (covariates) between the years 2011 and 2014 that influences on neonatal and child mortality and to further suggest the plausible policy proposals. Methods We modeled the neonatal and child mortality as categorical dependent variable (alive vs death of the child) while 16 covariates are used as independent variables using χ2 statistic and multiple logistic regression (MLR) based on maximum likelihood estimate. Findings Using the MLR, for neonatal mortality, diarrhea showed the highest positive coefficient (β = 1.130; P < 0.010) leading to most significant covariate for both 2011 and 2014. The corresponding odds ratios were: 0.323 for both the years. The second most significant covariate in 2011 was birth order between 2-6 years (β = 0.744; P < 0.001), while father’s education was negative correlation (β = -0.910; P < 0.050). In general, 10 covariates in 2011 and 5 covariates in 2014 were significant, so there was an improvement in socio-economic conditions for neonatal mortality. For child mortality, birth order between 2-6 years and 7 and above years showed the highest positive coefficients (β = 1.042; P < 0.010) and (β = 1.285; P < 0.050) for 2011. The corresponding odds ratios were: 2.835 and 3.614, respectively. Father's education showed the highest coefficient (β = 0.770; P < 0.050) indicating the significant covariate for 2014 and the corresponding odds ratio was 2.160. In general, 6 covariates in 2011 and 4 covariates in 2014 were also significant, so there was also an improvement in socio-economic conditions for child mortality. This study allows policy makers to make appropriate decisions to reduce neonatal and child mortality in Bangladesh. Conclusions In 2014, mother’s age and father’s education were also still significant covariates for child mortality. This study allows policy makers to make appropriate decisions to reduce neonatal and child mortality in Bangladesh. PMID:29740501

A comparison of numerical and machine-learning modeling of soil water content with limited input data

NASA Astrophysics Data System (ADS)

Karandish, Fatemeh; Šimůnek, Jiří

2016-12-01

Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a better choice for predicting SWCs with lower uncertainties when required data are available, and thus for designing water saving strategies for agriculture and for other environmental applications requiring estimates of SWCs.

Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling.

PubMed

Butail, Sachit; Salerno, Philip; Bollt, Erik M; Porfiri, Maurizio

2015-12-01

Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advancements in this area by affording the classification of these observables directly from images. While such methods have been successfully implemented in the classification of individual behavior, their potential in the study collective behavior is largely untested. In this paper, we compare three methods for the analysis of collective behavior: simple tracking (ST) without resolving occlusions, machine learning with real data (MLR), and machine learning with synthetic data (MLS). These methods are evaluated on videos recorded from an experiment studying the effect of ambient light on the shoaling tendency of Giant danios. In particular, we compute average nearest-neighbor distance (ANND) and polarization using the three methods and compare the values with manually-verified ground-truth data. To further assess possible dependence on sampling rate for computing ANND, the comparison is also performed at a low frame rate. Results show that while ST is the most accurate at higher frame rate for both ANND and polarization, at low frame rate for ANND there is no significant difference in accuracy between the three methods. In terms of computational speed, MLR and MLS take significantly less time to process an image, with MLS better addressing constraints related to generation of training data. Finally, all methods are able to successfully detect a significant difference in ANND as the ambient light intensity is varied irrespective of the direction of intensity change.

Identification of milk origin and process-induced changes in milk by stable isotope ratio mass spectrometry.

PubMed

Scampicchio, Matteo; Mimmo, Tanja; Capici, Calogero; Huck, Christian; Innocente, Nadia; Drusch, Stephan; Cesco, Stefano

2012-11-14

Stable isotope values were used to develop a new analytical approach enabling the simultaneous identification of milk samples either processed with different heating regimens or from different geographical origins. The samples consisted of raw, pasteurized (HTST), and ultrapasteurized (UHT) milk from different Italian origins. The approach consisted of the analysis of the isotope ratio of δ¹³C and δ¹⁵N for the milk samples and their fractions (fat, casein, and whey). The main finding of this work is that as the heat processing affects the composition of the milk fractions, changes in δ¹³C and δ¹⁵N were also observed. These changes were used as markers to develop pattern recognition maps based on principal component analysis and supervised classification models, such as linear discriminant analysis (LDA), multivariate regression (MLR), principal component regression (PCR), and partial least-squares (PLS). The results give proof of the concept that isotope ratio mass spectroscopy can discriminate simultaneously between milk samples according to their geographical origin and type of processing.

Linking In-Vehicle Ultrafine Particle Exposures to On-Road Concentrations

PubMed Central

Hudda, Neelakshi; Eckel, Sandrah P.; Knibbs, Luke D.; Sioutas, Constantinos; Delfino, Ralph J.; Fruin, Scott A.

2013-01-01

For traffic-related pollutants like ultrafine particles (UFP, Dp < 100 nm), a significant fraction of overall exposure occurs within or close to the transit microenvironment. Therefore, understanding exposure to these pollutants in such microenvironments is crucial to accurately assessing overall UFP exposure. The aim of this study was to develop models for predicting in-cabin UFP concentrations if roadway concentrations are known, taking into account vehicle characteristics, ventilation settings, driving conditions and air exchange rates (AER). Particle concentrations and AER were measured in 43 and 73 vehicles, respectively, under various ventilation settings and driving speeds. Multiple linear regression (MLR) and generalized estimating equation (GEE) regression models were used to identify and quantify the factors that determine inside-to-outside (I/O) UFP ratios and AERs across a full range of vehicle types and ages. AER was the most significant determinant of UFP I/O ratios, and was strongly influenced by ventilation setting (recirculation or outside air intake). Inclusion of ventilation fan speed, vehicle age or mileage, and driving speed explained greater than 79% of the variability in measured UFP I/O ratios. PMID:23888122

Optimization of immunosuppressive therapy based on a multiparametric mixed lymphocyte reaction assay reduces infectious complications and mortality in living donor liver transplant recipients.

PubMed

Tanaka, Y; Tashiro, H; Onoe, T; Ide, K; Ishiyama, K; Ohdan, H

2012-03-01

We investigated the clinical relevance of immune monitoring by a multiparametric mixed lymphocyte reaction (MLR) assay, wherein the number and phenotype of alloreactive precursors can be quantified by combining the results of carboxyfluorescein diacetate succinimidyl ester labeling and flow cytometry analysis. In 51 adult patients undergoing living donor liver transplantation (OLT), immunosuppressive drugs were dosed on the basis of immune monitoring by the MLR assay (optimized protocol: group O). In 64 other patients, the agents were prescribed according to empirical regimens (empirical protocol: group E). In group O, MLR assays were performed at 2- to 4-week intervals until 3 months after OLT and thereafter at 3- to 6-month intervals. Therapeutic adjustments for immunosuppressants were determined by tapering the doses in cases of anti-donor hyporesponsiveness for both CD4+ and CD8+ T-cell subsets. The 1-year patient and graft survivals in groups O versus E were 90.2% versus 76.6%, respectively. The incidence of acute rejection episodes (ARE) among group O (13.7%) were lower than in cohort E (28.1%). None of the patients in group O while four patients (3%) in group E already have shown chronic rejection to date. The incidences of bacteremia and fungal infections in group O (9.8% and 7.5%, respectively) were lower than in cohort E (18.8% and 12.6%, respectively). A multiparametric MLR assay may facilitate the development of adequate immunosuppressive regimens. Copyright © 2012 Elsevier Inc. All rights reserved.

Agricultural solid waste for sorption of metal ions, part II: competitive assessment in multielemental solution and lake water.

PubMed

Milani, Priscila Aparecida; Consonni, João Luiz; Labuto, Geórgia; Carrilho, Elma Neide Vasconcelos Martins

2018-03-20

Sugarcane bagasse and hydroponic lettuce roots were used as biosorbents for the removal of Cu(II), Fe(II), Mn(II), and Zn(II) from multielemental solutions and lake water, in batch processes. These biomasses were studied in natura (lettuce roots, NLR, and sugarcane bagasse, NSB) and chemically modified with HNO 3 (lettuce roots, MLR, and sugarcane bagasse, MSB). The results showed higher adsorption efficiency for MSB and either NLR or MLR. The maximum adsorption capacities (q max ) in multielemental solution for Cu(II), Fe(II), Mn(II), and Zn(II) were 35.86, 31.42, 3.33, and 24.07 mg/g for NLR; 25.36, 27.95, 14.06, and 6.43 mg/g for MLR; 0.92, 3.94, 0.03, and 0.18 mg/g for NSB; and 54.11, 6.52, 16.7, and 1.26 mg/g for MSB, respectively. The kinetic studies with chemically modified biomasses indicated that sorption was achieved in the first 5 min and reached equilibrium around 30 min. Sorption of Cu(II), Fe(II), Mn(II), and Zn(II) in lake water by chemically modified biomasses was 24.31, 14.50, 8.03, and 8.21 mg/g by MLR, and 13.15, 10.50, 6.10, and 5.14 mg/g by MSB, respectively. These biosorbents are promising and low costs agricultural residues, and as for lettuce roots, these showed great potential even with no chemical modification.

Impacts of 2000-2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality in China Based on Statistical Projections Using an Ensemble of Global Climate Models

NASA Astrophysics Data System (ADS)

Leung, D. M.; Tai, A. P. K.; Shen, L.; Moch, J. M.; van Donkelaar, A.; Mickley, L. J.

2017-12-01

Fine particulate matter (PM2.5) air quality is strongly dependent on not only on emissions but also meteorological conditions. Here we examine the dominant synoptic circulation patterns that control day-to-day PM2.5 variability over China. We perform principal component (PC) analysis on 1998-2016 NCEP/NCAR Reanalysis I daily meteorological fields to diagnose distinct synoptic meteorological modes, and perform PC regression on spatially interpolated 2014-2016 daily mean PM2.5 concentrations in China to identify modes dominantly explaining PM2.5 variability. We find that synoptic systems, e.g., cold-frontal passages, maritime inflow and frontal precipitation, can explain up to 40% of the day-to-day PM2.5 variability in major metropolitan regions in China. We further investigate how annually changing frequencies of synoptic systems, as well as changing local meteorology, drive interannual PM2.5 variability. We apply a spectral analysis on the PC time series to obtain the 1998-2016 annual median synoptic frequency, and use a forward-selection multiple linear regression (MLR) model of satellite-derived 1998-2015 annual mean PM2.5 concentrations on local meteorology and synoptic frequency, selecting predictors that explain the highest fraction of interannual PM2.5 variability while guarding against multicollinearity. To estimate the effect of climate change on future PM2.5 air quality, we project a multimodel ensemble of 15 CMIP5 models under the RCP8.5 scenario on the PM2.5-to-meteorology sensitivities derived for the present-day from the MLR model. Our results show that climate change could be responsible for increases in PM2.5 of more than 25 μg m-3 in northwestern China and 10 mg m-3 in northeastern China by the 2050s. Increases in synoptic frequency of cold-frontal passages cause only a modest 1 μg m-3 decrease in PM2.5 in North China Plain. Our analyses show that climate change imposes a significant penalty on air quality over China and poses serious threat on human health under the RCP8.5 future.

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.

PubMed

Bahadori, Behnoosh; Atabati, Morteza

2017-01-01

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1.

PubMed

Comelli, Nieves C; Duchowicz, Pablo R; Castro, Eduardo A

2014-10-01

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method. Copyright © 2014 Elsevier B.V. All rights reserved.

Forecasting generation of urban solid waste in developing countries--a case study in Mexico.

PubMed

Buenrostro, O; Bocco, G; Vence, J

2001-01-01

Based on a study of the composition of urban solid waste (USW) and of socioeconomic variables in Morelia, Mexico, generation rates were estimated. In addition, the generation of residential solid waste (RSW) and nonresidential solid waste (NRSW) was forecasted by means of a multiple linear regression (MLR) analysis. For residential sources, the independent variables analyzed were monthly wages, persons per dwelling, age, and educational level of the heads of the household. For nonresidential sources, variables analyzed were number of employees, area of facilities, number of working days, and working hours per day. The forecasted values for residential waste were similar to those observed. This approach may be applied to areas in which available data are scarce, and in which there is an urgent need for the planning of adequate management of USW.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Central command dysfunction in rats with heart failure is mediated by brain oxidative stress and normalized by exercise training.

PubMed

Koba, Satoshi; Hisatome, Ichiro; Watanabe, Tatsuo

2014-09-01

Sympathoexcitation elicited by central command, a parallel activation of the motor and autonomic neural circuits in the brain, has been shown to become exaggerated in chronic heart failure (CHF). The present study tested the hypotheses that oxidative stress in the medulla in CHF plays a role in exaggerating central command-elicited sympathoexcitation, and that exercise training in CHF suppresses central command-elicited sympathoexcitation through its antioxidant effects in the medulla. In decerebrate rats, central command was activated by electrically stimulating the mesencephalic locomotor region (MLR) after neuromuscular blockade. The MLR stimulation at a current intensity greater than locomotion threshold in rats with CHF after myocardial infarction (MI) evoked larger (P < 0.05) increases in renal sympathetic nerve activity and arterial pressure than in sham-operated healthy rats (Sham) and rats with CHF that had completed longterm (8–12 weeks) exercise training (MI + TR). In the Sham and MI + TR rats, bilateral microinjection of a superoxide dismutase (SOD) mimetic Tempol into the rostral ventrolateral medulla (RVLM) had no effects on MLR stimulation-elicited responses. By contrast, in MI rats, Tempol treatment significantly reduced MLR stimulation-elicited responses. In a subset of MI rats, treatment with Tiron, another SOD mimetic, within the RVLM also reduced responses. Superoxide generation in the RVLM, as evaluated by dihydroethidium staining, was enhanced in MI rats compared with that in Sham and MI + TR rats. Collectively, these results support the study hypotheses. We suggest that oxidative stress in the medulla in CHF mediates central command dysfunction, and that exercise training in CHF is capable of normalizing central command dysfunction through its antioxidant effects in the medulla.

Novel electrospun nanofibrous matrices prepared from poly(lactic acid)/poly(butylene adipate) blends for controlled release formulations of an anti-rheumatoid agent.

PubMed

Siafaka, Panoraia I; Barmbalexis, Panagiotis; Bikiaris, Dimitrios N

2016-06-10

In the present work, a series of novel formulations consisting of poly(lactic acid)/poly(butylene adipate) (PLA/PBAd) electrospun blends was examined as controlled release matrices for Leflunomide's active metabolite, Teriflunomide (TFL). The mixtures were prepared using different ratios of PLA and PBAd in order to produce nanofibrous matrices with different characteristics. Miscibility studies of the blended polymeric fibers were performed through differential scanning calorimetry (DSC) and X-ray diffractometry (XRD). Hydrolytic degradation in the prepared fibers was evaluated at 37°C using a phosphate buffered saline solution. Different concentrations of (TFL) (5, 10, 15wt.%) were incorporated into nanofibers for examining the drug release behavior in simulated body fluids (SBF), at 37°C. The drug-loaded nanofibrous formulations were further characterized by Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, DSC and XRD. Gel permeation chromatography (GPC) analysis was used to evaluate the mechanism of TFL release. Artificial neural networks (ANN) and multi-linear-regression (MLR) models were used to evaluate the effect of % content of PBAd (X1) and TFL (X2) on an initial burst effect and a dissolution behavior. It was found that PLA/PBAd nanofibers have different diameters depending on the ratio of used polyesters and added drug. TFL was incorporated in an amorphous form inside the polymeric nanofibers. In vitro release studies reveal that a drug release behavior is correlated with the size of the nanofibers, drug loading and matrix degradation after a specific time. ANN dissolution modeling showed increased correlation efficacy compared to MLR. Copyright © 2016 Elsevier B.V. All rights reserved.

Antiplatelet drug selection in PCI to vein grafts in patients with acute coronary syndrome and adverse clinical outcomes: Insights from the British Cardiovascular Intervention Society database.

PubMed

Sirker, Alex; Kwok, Chun Shing; Kontopantelis, Evangelos; Johnson, Tom; Freeman, Philip; de Belder, Mark A; Ludman, Peter; Zaman, Azfar; Mamas, Mamas A

2018-01-22

This study aims to evaluate outcomes associated with different P2Y12 agents in Saphenous Vein graft (SVG) percutaneous coronary intervention (PCI). SVG PCI is associated with greater risks of ischemic complications, compared with native coronary PCI. Outcomes associated with the use of potent P2Y12 blocking drugs, Prasugrel and Ticagrelor, in SVG PCI are unknown. Patients included in the study underwent SVG PCI in the United Kingdom between 2007 and 2014 for acute coronary syndrome and were grouped by P2Y12 antiplatelet use. In-hospital major adverse cardiac events, major bleeding and 30-day and 1-year mortality were examined. Multiple imputations with chained equations to impute missing data were used. Adjustment for baseline imbalances was performed using (1) multiple logistic regression (MLR) and (separately) (2) propensity score matching (PSM). Data weres analyzed from 8,119 patients and most cases were treated with Clopidogrel (n = 7,401), followed by Ticagrelor (n = 497) and Prasugrel (n = 221). In both MLR and PSM models, there was no significant evidence to suggest that either Prasugrel or Ticagrelor was associated with significantly lower 30-day mortality compared with Clopidogrel. The odds ratios reported from the multivariable analysis were 1.22 (95% CI: 0.60-2.51) for Prasugrel vs. Clopidogrel and 0.48 (95% CI: 0.20-1.16) for Ticagrelor vs. Clopidogrel. No significant differences were seen for in-hospital ischemic or bleeding events. Our real world national study provides no clear evidence to indicate that use of potent P2Y12 blockers in SVG PCI is associated with improved clinical outcomes. © 2018 Wiley Periodicals, Inc.

SAGE III solar ozone measurements: Initial results

NASA Technical Reports Server (NTRS)

Wang, Hsiang-Jui; Cunnold, Derek M.; Trepte, Chip; Thomason, Larry W.; Zawodny, Joseph M.

2006-01-01

Results from two retrieval algorithms, o3-aer and o3-mlr , used for SAGE III solar occultation ozone measurements in the stratosphere and upper troposphere are compared. The main differences between these two retrieved (version 3.0) ozone are found at altitudes above 40 km and below 15 km. Compared to correlative measurements, the SAGE II type ozone retrievals (o3-aer) provide better precisions above 40 km and do not induce artificial hemispheric differences in upper stratospheric ozone. The multiple linear regression technique (o3_mlr), however, can yield slightly more accurate ozone (by a few percent) in the lower stratosphere and upper troposphere. By using SAGE III (version 3.0) ozone from both algorithms and in their preferred regions, the agreement between SAGE III and correlative measurements is shown to be approx.5% down to 17 km. Below 17 km SAGE III ozone values are systematically higher, by 10% at 13 km, and a small hemispheric difference (a few percent) appears. Compared to SAGE III and HALOE, SAGE II ozone has the best accuracy in the lowest few kilometers of the stratosphere. Estimated precision in SAGE III ozone is about 5% or better between 20 and 40 km and approx.10% at 50 km. The precision below 20 km is difficult to evaluate because of limited coincidences between SAGE III and sondes. SAGE III ozone values are systematically slightly larger (2-3%) than those from SAGE II but the profile shapes are remarkably similar for altitudes above 15 km. There is no evidence of any relative drift or time dependent differences between these two instruments for altitudes above 15-20 km.

Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants.

PubMed

Funar-Timofei, Simona; Borota, Ana; Crisan, Luminita

2017-05-01

Cinnoline, pyridine, pyrimidine, and triazine herbicides were found be inhibitors of the D1 protein in photosystem II (D1 PSII) electron transport of plants. The photosystem II inhibitory activity of these herbicides, expressed by experimental [Formula: see text] values, was modeled by a docking and quantitative structure-activity relationships study. A conformer ensemble for each of the herbicide structure was generated using the MMFF94s force field. These conformers were further employed in a docking approach, which provided new information about the rational "active conformations" and various interaction patterns of the herbicide derivatives with D1 PSII. The most "active conformers" from the docking study were used to calculate structural descriptors, which were further related to the inhibitory experimental [Formula: see text] values by multiple linear regression (MLR). The dataset was divided into training and test sets according to the partition around medoids approach, taking 27% of the compounds from the entire series for the test set. Variable selection was performed using the genetic algorithm, and several criteria were checked for model performance. WHIM and GETAWAY geometrical descriptors (position of substituents and moieties in the molecular space) were found to contribute to the herbicidal activity. The derived MLR model is statistically significant, shows very good stability and was used to predict the herbicidal activity of new derivatives having cinnoline, indeno[1.2-c]cinnoline-ll-one, triazolo[1,5-a] pyridine, imidazo[1,2-a]pyridine, triazine and triazolo[1,5-a] pyrimidine scaffolds whose experimental inhibitory activity against D1 PSII had not been determined up to now.

A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon.

PubMed

Ghaedi, M; Ansari, A; Bahari, F; Ghaedi, A M; Vafaei, A

2015-02-25

In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R(2)) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively. Copyright © 2014 Elsevier B.V. All rights reserved.

A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon

NASA Astrophysics Data System (ADS)

Ghaedi, M.; Ansari, A.; Bahari, F.; Ghaedi, A. M.; Vafaei, A.

2015-02-01

In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R2) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively.

Who would students ask for help in academic cheating? Cross-sectional study of medical students in Croatia.

PubMed

Đogaš, Varja; Jerončić, Ana; Marušić, Matko; Marušić, Ana

2014-12-30

Academic cheating does not happen as an isolated action of an individual but is most often a collaborative practice. As there are few studies that looked at who are collaborators in cheating, we investigated medical students' readiness to engage others in academic dishonest behaviours. In a cross-sectional survey study in Zagreb, Croatia, 592 medical students from the first, 3rd and 6th (final) study year anonymously answered a survey of readiness to ask family, friends, colleagues or strangers for help in 4 different forms of academic cheating or for 2 personal material favours. Stepwise multiple linear regression models (MLR) were used to evaluate potential factors influencing propensity for engaging others in these two types of behaviour. Many students would ask another person for help in academic cheating, from 88.8% to 26.9% depending on a cheating behaviour. Students would most often ask a family member or friend for help in academic cheating. The same "helpers" were identified for non-academic related behaviour - asking for personal material favours. More respondents, however, would include three or four persons for asking help in academic cheating than for routine material favours. Score on material favours survey was the strongest positive predictor of readiness for asking help in academic cheating (stepwise MLR model; beta = 0.308, P < 0.0001) followed by extrinsic motivation (compensation) and male gender, whereas intrinsic motivation, year of study and grade point average were weak negative predictors. Our study indicates that medical students are willing to engage more than one person in either close or distant relationships in academic cheating. In order to develop effective preventive measures to deter cheating at medical academic institutions, factors surrounding students' preference towards academic cheating rather than routine favours should be further investigated.

Parental body mass index is associated with adolescent overweight and obesity in Mashhad, Iran.

PubMed

Shafaghi, Khosro; Shariff, Zalilah Mohd; Taib, Mohd Nasir Mohd; Rahman, Hejar Abdul; Mobarhan, Majid Ghayour; Jabbari, Hadi

2014-01-01

This cross-sectional study was carried out to determine the prevalence of overweight and obesity among secondary school children aged 12 to 14 years in the city of Mashhad, Iran and its association with parental body mass index. A total of 1189 secondary school children (579 males and 610 females) aged 12- 14 years old were selected through a stratified multistage random sampling. All adolescents were measured for weight and height. Household socio-demographic information and parental weight and height were self-reported by parents. Adolescents were classified as overweight or obese based on BMI-for age Z-score. Multivariable logistic Regression (MLR) determined the relationship between parental BMI and adolescent overweight and obesity. The overall prevalence of overweight and obesity among secondary school children in Mashhad was 17.2% and 11.9%, respectively. A higher proportion of male (30.7%) than female (27.4%) children were overweight or obese. BMI of the children was significantly related to parental BMI (p<0.001), gender (p= 0.02), birth order (p<0.01), parents' education level (p<0.001), father's employment status (p<0.001), and family income (p<0.001). MLR showed that the father's BMI was significantly associated with male BMI (OR: 2.02) and female BMI (OR: 1.59), whereas the mother's BMI was significantly associated with female BMI only (OR: 0.514). The high prevalence of overweight/obesity among the research population compared with previous studies in Iran could be related to the changing lifestyle of the population. The strong relationship with parental BMI was probably related to a combination of genetic and lifestyle factors. Strategies to address childhood obesity should consider the interaction of these factors.

Observed Seasonal to Decadal-Scale Responses in Mesospheric Water Vapor

NASA Technical Reports Server (NTRS)

Remsberg, Ellis

2010-01-01

The 14-yr (1991-2005) time series of mesospheric water vapor from the Halogen Occultation Experiment (HALOE) are analyzed using multiple linear regression (MLR) techniques for their6 seasonal and longer-period terms from 45S to 45N. The distribution of annual average water vapor shows a decrease from a maximum of 6.5 ppmv at 0.2 hPa to about 3.2 ppmv at 0.01 hPa, in accord with the effects of the photolysis of water vapor due to the Lyman-flux. The distribution of the semi-annual cycle amplitudes is nearly hemispherically symmetric at the low latitudes, while that of the annual cycles show larger amplitudes in the northern hemisphere. The diagnosed 11-yr, or solar cycle, max minus min, water vapor values are of the order of several percent at 0.2 hPa to about 23% at 0.01 hPa. The solar cycle terms have larger values in the northern than in the southern hemisphere, particularly in the middle mesosphere, and the associated linear trend terms are anomalously large in the same region. Those anomalies are due, at least in part, to the fact that the amplitudes of the seasonal cycles were varying at northern mid latitudes during 1991-2005, while the corresponding seasonal terms of the MLR model do not allow for that possibility. Although the 11-yr variation in water vapor is essentially hemispherically-symmetric and anti-phased with the solar cycle flux near 0.01 hPa, the concurrent temperature variations produce slightly colder conditions at the northern high latitudes at solar minimum. It is concluded that this temperature difference is most likely the reason for the greater occurrence of polar mesospheric clouds at the northern versus the southern high latitudes at solar minimum during the HALOE time period.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

NASA Astrophysics Data System (ADS)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

Variability of pCO2 in surface waters and development of prediction model.

PubMed

Chung, Sewoong; Park, Hyungseok; Yoo, Jisu

2018-05-01

Inland waters are substantial sources of atmospheric carbon, but relevant data are rare in Asian monsoon regions including Korea. Emissions of CO 2 to the atmosphere depend largely on the partial pressure of CO 2 (pCO 2 ) in water; however, measured pCO 2 data are scarce and calculated pCO 2 can show large uncertainty. This study had three objectives: 1) to examine the spatial variability of pCO 2 in diverse surface water systems in Korea; 2) to compare pCO 2 calculated using pH-total alkalinity (Alk) and pH-dissolved inorganic carbon (DIC) with pCO 2 measured by an in situ submersible nondispersive infrared detector; and 3) to characterize the major environmental variables determining the variation of pCO 2 based on physical, chemical, and biological data collected concomitantly. Of 30 samples, 80% were found supersaturated in CO 2 with respect to the overlying atmosphere. Calculated pCO 2 using pH-Alk and pH-DIC showed weak prediction capability and large variations with respect to measured pCO 2 . Error analysis indicated that calculated pCO 2 is highly sensitive to the accuracy of pH measurements, particularly at low pH. Stepwise multiple linear regression (MLR) and random forest (RF) techniques were implemented to develop the most parsimonious model based on 10 potential predictor variables (pH, Alk, DIC, Uw, Cond, Turb, COD, DOC, TOC, Chla) by optimizing model performance. The RF model showed better performance than the MLR model, and the most parsimonious RF model (pH, Turb, Uw, Chla) improved pCO 2 prediction capability considerably compared with the simple calculation approach, reducing the RMSE from 527-544 to 105μatm at the study sites. Copyright © 2017 Elsevier B.V. All rights reserved.

Turmeric improves post-prandial working memory in pre-diabetes independent of insulin.

PubMed

Lee, Meei-Shyuan; Wahlqvist, Mark L; Chou, Yu-Ching; Fang, Wen-Hui; Lee, Jiunn-Tay; Kuan, Jen-Chun; Liu, Hsiao-Yu; Lu, Ting-Mei; Xiu, Lili; Hsu, Chih-Cheng; Andrews, Zane B; Pan, Wen-Harn

2014-01-01

Cognitive impairment develops with pre-diabetes and dementia is a complication of diabetes. Natural products like turmeric and cinnamon may ameliorate the underlying pathogenesis. People ≥ 60 years (n=48) with newly-recognised untreated pre-diabetes were randomised to a double-blind metabolic study of placebo, turmeric (1 g), cinnamon (2 g) or both (1 g & 2 g respectively), ingested at a white bread (119 g) breakfast. Observations were made over 6 hours for pre- and post-working memory (WM), glycaemic and insulin responses and biomarkers of Alzheimer's disease (AD)(0, 2, 4 and 6 hours): amyloid precursor protein (APP), γ-secretase subunits presenilin-1 (PS1), presenilin-2 (PS2), and glycogen synthase kinase (GSK-3β). Differences between natural product users and non-users were determined by Students t and chi square tests; and between pre-test and post-test WM by Wilcoxon signed rank tests. Interaction between turmeric and cinnamon was tested by 2-way ANOVA. Multivariable linear regression (MLR) took account of BMI, glycaemia, insulin and AD biomarkers in the WM responses to turmeric and cinnamon. No interaction between turmeric and cinnamon was detected. WM increased from 2.6 to 2.9 out of 3.0 (p=0.05) with turmeric, but was unchanged with cinnamon. WM improvement was inversely associated with insulin resistance (r=-0.418, p<0.01), but not with AD biomarkers. With MLR, the WM responses to turmeric were best predicted with an R2 of 34.5%; and with significant turmeric, BMI and insulin/glucose AUC beta-coefficients. Co-ingestion of turmeric with white bread increases working memory independent of body fatness, glycaemia, insulin, or AD biomarkers.

Real-time process monitoring in a semi-continuous fluid-bed dryer - microwave resonance technology versus near-infrared spectroscopy.

PubMed

Peters, Johanna; Teske, Andreas; Taute, Wolfgang; Döscher, Claas; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg

2018-02-15

The trend towards continuous manufacturing in the pharmaceutical industry is associated with an increasing demand for advanced control strategies. It is a mandatory requirement to obtain reliable real-time information on critical quality attributes (CQA) during every process step as the decision on diversion of material needs to be performed fast and automatically. Where possible, production equipment should provide redundant systems for in-process control (IPC) measurements to ensure continuous process monitoring even if one of the systems is not available. In this paper, two methods for real-time monitoring of granule moisture in a semi-continuous fluid-bed drying unit are compared. While near-infrared (NIR) spectroscopy has already proven to be a suitable process analytical technology (PAT) tool for moisture measurements in fluid-bed applications, microwave resonance technology (MRT) showed difficulties to monitor moistures above 8% until recently. The results indicate, that the newly developed MRT sensor operating at four resonances is capable to compete with NIR spectroscopy. While NIR spectra were preprocessed by mean centering and first derivative before application of partial least squares (PLS) regression to build predictive models (RMSEP = 0.20%), microwave moisture values of two resonances sufficed to build a statistically close multiple linear regression (MLR) model (RMSEP = 0.07%) for moisture prediction. Thereby, it could be verified that moisture monitoring by MRT sensor systems could be a valuable alternative to NIR spectroscopy or could be used as a redundant system providing great ease of application. Copyright © 2017 Elsevier B.V. All rights reserved.

Production of Large-Particle-Size Monodisperse Latexes in Microgravity

NASA Technical Reports Server (NTRS)

Vanderhoff, J. W.; Micale, F. J.; El-Aasser, M. S.; Kornfeld, M.

1985-01-01

A latex is a suspension of very tiny (micrometer-size) plastic spheres in water, stabilized by emulsifiers. The growth of billions of these tiny plastic spheres to sizes larger than can be grown on Earth is attempted while keeping all of them exactly the same size and perfectly spherical. Thus far on several of the Monodisperse Latex Reactor (MLR) flights, the latex spheres have been returned to Earth with standard deviations of better than 1.4%. In microgravity the absence of buoyancy effects has allowed growth of the balls up to 30 micrometers in diameter thus far. The MLR has now flown 5 times on the Shuttle. The MLR has now produced the first commercial space product; that is the first commercial material ever manufactured in space and marketed on Earth. Once it is demonstrated that these large-size-monodisperse latexes can be routinely produced in quantity and quality, they can be marketed for many types of scientific applications. They can be used in biomedical research for such things as drug carriers and tracers in the body, human and animal blood flow studies, membrane and pore-sizing in the body, and medical diagnostic tests.

Phylogeny of lymphocyte heterogeneity: the cellular requirements for the mixed leucocyte reaction with channel catfish.

PubMed Central

Miller, N W; Deuter, A; Clem, L W

1986-01-01

Vigorous mixed leucocyte reactions (MLR) were obtained using channel catfish peripheral blood leucocytes (PBL) when equal numbers of responder and stimulator cells (5 X 10(5) cells each) were cocultured. The use of 2000 rads of X-irradiation was sufficient to block subsequent proliferative responses of the stimulator cells. The cellular requirements for channel catfish MLR responses were assessed by using three functionally distinct leucocyte subpopulations isolated from the PBL. B cells (sIg+ lymphocytes) and T cells (sIg- lymphocytes) were isolated by an indirect panning procedure employing a monoclonal antibody specific for channel catfish Ig. A third population, monocytes, was isolated or depleted by adherence to baby hamster kidney cell microexudate-coated surfaces or adherence to Sephadex G-10, respectively. The results indicated that only the T cells were able to respond in the fish MLR, with monocytes being required as accessory cells. In contrast, all three cell types could function as stimulator cells. In addition, it was observed that low in vitro culture temperatures inhibited the generation of channel catfish MLRs, thereby supporting the contention that low temperature immunosuppression in fish results from a preferential inhibition of the generation of primary T-cell responses. PMID:2944817

Adapting GNU random forest program for Unix and Windows

NASA Astrophysics Data System (ADS)

Jirina, Marcel; Krayem, M. Said; Jirina, Marcel, Jr.

2013-10-01

The Random Forest is a well-known method and also a program for data clustering and classification. Unfortunately, the original Random Forest program is rather difficult to use. Here we describe a new version of this program originally written in Fortran 77. The modified program in Fortran 95 needs to be compiled only once and information for different tasks is passed with help of arguments. The program was tested with 24 data sets from UCI MLR and results are available on the net.

Dynamics of air-sea CO2 fluxes based on FerryBox measurements and satellite-based prediction of pCO2 in the Western English Channel

NASA Astrophysics Data System (ADS)

Marrec, Pierre; Thierry, Cariou; Eric, Mace; Pascal, Morin; Marc, Vernet; Yann, Bozec

2014-05-01

Since April 2012, we installed an autonomous FerryBox system on a Voluntary Observing Ship (VOS), which crosses the Western English Channel (WEC) between Roscoff and Plymouth on a daily basis. High-frequency data of sea surface temperature (SST), salinity (SSS), fluorescence, dissolved oxygen (DO) and partial pressure of CO2 (pCO2) were recorded for two years across the all-year mixed southern WEC (sWEC) and the seasonally stratified northern WEC (nWEC). These contrasting hydrographical provinces strongly influenced the spatio-temporal distributions of pCO2 and air-sea CO2 fluxes. During the productive period (from May to September), the nWEC acted as a sink for atmospheric CO2 of -5.6 mmolC m-2 d-1 and -4.6 mmolC m-2 d-1, in 2012 and 2013, respectively. During the same period, the sWEC showed significant inter-annual variability degassing CO2 to the atmosphere in 2012 (1.4 mmolC m-2 d-1) and absorbing atmospheric CO2 in 2013 (-1.6 mmolC m-2 d-1). In 2012, high-frequency data revealed that an intense and short (less than 10 days) summer phytoplankton bloom in the nWEC contributed to 31% of the total CO2 drawdown during the productive period, highlighting the necessity of pCO2 high-frequency measurements in coastal ecosystems. Based on this multi-annual dataset, we developed pCO2 algorithms using multiple linear regression (MLR) based on SST, SSS, chlorophyll-a (Chl-a) concentration, time, latitude and mixed layer depth to predict pCO2 in the two hydrographical provinces of the WEC. MLR were performed based on more than 200,000 underway observations spanning the range from 150 to 480 µatm. The root mean square errors (RMSE) of the MLR fit to the data were 17.2 µatm and 21.5 µatm for the s WEC and the nWEC with correlation coefficient (r²) of 0.71 and 0.79, respectively. We applied these algorithms to satellite SST and Chl-a products and to modeled SSS estimates in the entire WEC. Based on these high-frequency and satellite approaches, we will discuss the main biogeochemical processes driving the air-sea CO2 fluxes in the WEC and adjacent coastal seas.

Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of Delhi, India

NASA Astrophysics Data System (ADS)

Mishra, Dhirendra; Goyal, P.; Upadhyay, Abhishek

2015-02-01

Delhi has been listed as the worst performer across the world with respect to the presence of alarmingly high level of haze episodes, exposing the residents here to a host of diseases including respiratory disease, chronic obstructive pulmonary disorder and lung cancer. This study aimed to analyze the haze episodes in a year and to develop the forecasting methodologies for it. The air pollutants, e.g., CO, O3, NO2, SO2, PM2.5 as well as meteorological parameters (pressure, temperature, wind speed, wind direction index, relative humidity, visibility, dew point temperature, etc.) have been used in the present study to analyze the haze episodes in Delhi urban area. The nature of these episodes, their possible causes, and their major features are discussed in terms of fine particulate matter (PM2.5) and relative humidity. The correlation matrix shows that temperature, pressure, wind speed, O3, and dew point temperature are the dominating variables for PM2.5 concentrations in Delhi. The hour-by-hour analysis of past data pattern at different monitoring stations suggest that the haze hours were occurred approximately 48% of the total observed hours in the year, 2012 over Delhi urban area. The haze hour forecasting models in terms of PM2.5 concentrations (more than 50 μg/m3) and relative humidity (less than 90%) have been developed through artificial intelligence based Neuro-Fuzzy (NF) techniques and compared with the other modeling techniques e.g., multiple linear regression (MLR), and artificial neural network (ANN). The haze hour's data for nine months, i.e. from January to September have been chosen for training and remaining three months, i.e., October to December in the year 2012 are chosen for validation of the developed models. The forecasted results are compared with the observed values with different statistical measures, e.g., correlation coefficients (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA). The performed analysis has indicated that R has values 0.25 for MLR, 0.53 for ANN, and NF: 0.72, between the observed and predicted PM2.5 concentrations during haze hours invalidation period. The results show that the artificial intelligence implementations have a more reasonable agreement with the observed values. Finally, it can be concluded that the most convincing advantage of artificial intelligence based NF model is capable for better forecasting of haze episodes in Delhi urban area than ANN and MLR models.

Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

PubMed

Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

2017-02-01

High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression

NASA Astrophysics Data System (ADS)

Beh, W. L.; Yong, M. K. Au

2017-04-01

This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

Groundwater quality assessment and pollution source apportionment in an intensely exploited region of northern China.

PubMed

Zhang, Qianqian; Wang, Huiwei; Wang, Yanchao; Yang, Mingnan; Zhu, Liang

2017-07-01

Deterioration in groundwater quality has attracted wide social interest in China. In this study, groundwater quality was monitored during December 2014 at 115 sites in the Hutuo River alluvial-pluvial fan region of northern China. Results showed that 21.7% of NO 3 - and 51.3% of total hardness samples exceeded grade III of the national quality standards for Chinese groundwater. In addition, results of gray relationship analysis (GRA) show that 64.3, 10.4, 21.7, and 3.6% of samples were within the I, II, IV, and V grades of groundwater in the Hutuo River region, respectively. The poor water quality in the study region is due to intense anthropogenic activities as well as aquifer vulnerability to contamination. Results of principal component analysis (PCA) revealed three major factors: (1) domestic wastewater and agricultural runoff pollution (anthropogenic activities), (2) water-rock interactions (natural processes), and (3) industrial wastewater pollution (anthropogenic activities). Using PCA and absolute principal component scores-multivariate linear regression (APCS-MLR), results show that domestic wastewater and agricultural runoff are the main sources of groundwater pollution in the Hutuo River alluvial-pluvial fan area. Thus, the most appropriate methods to prevent groundwater quality degradation are to improve capacities for wastewater treatment and to optimize fertilization strategies.

Quantitative structure-retention relationship studies for taxanes including epimers and isomeric metabolites in ultra fast liquid chromatography.

PubMed

Dong, Pei-Pei; Ge, Guang-Bo; Zhang, Yan-Yan; Ai, Chun-Zhi; Li, Guo-Hui; Zhu, Liang-Liang; Luan, Hong-Wei; Liu, Xing-Bao; Yang, Ling

2009-10-16

Seven pairs of epimers and one pair of isomeric metabolites of taxanes, each pair of which have similar structures but different retention behaviors, together with additional 13 taxanes with different substitutions were chosen to investigate the quantitative structure-retention relationship (QSRR) of taxanes in ultra fast liquid chromatography (UFLC). Monte Carlo variable selection (MCVS) method was adopted to choose descriptors. The selected four descriptors were used to build QSRR model with multi-linear regression (MLR) and artificial neural network (ANN) modeling techniques. Both linear and nonlinear models show good predictive ability, of which ANN model was better with the determination coefficient R(2) for training, validation and test set being 0.9892, 0.9747 and 0.9840, respectively. The results of 100 times' leave-12-out cross validation showed the robustness of this model. All the isomers can be correctly differentiated by this model. According to the selected descriptors, the three dimensional structural information was critical for recognition of epimers. Hydrophobic interaction was the uppermost factor for retention in UFLC. Molecules' polarizability and polarity properties were also closely correlated with retention behaviors. This QSRR model will be useful for separation and identification of taxanes including epimers and metabolites from botanical or biological samples.

What controls the stable isotope composition of precipitation in the Mekong Delta? A model-based statistical approach

NASA Astrophysics Data System (ADS)

Le Duy, Nguyen; Heidbüchel, Ingo; Meyer, Hanno; Merz, Bruno; Apel, Heiko

2018-02-01

This study analyzes the influence of local and regional climatic factors on the stable isotopic composition of rainfall in the Vietnamese Mekong Delta (VMD) as part of the Asian monsoon region. It is based on 1.5 years of weekly rainfall samples. In the first step, the isotopic composition of the samples is analyzed by local meteoric water lines (LMWLs) and single-factor linear correlations. Additionally, the contribution of several regional and local factors is quantified by multiple linear regression (MLR) of all possible factor combinations and by relative importance analysis. This approach is novel for the interpretation of isotopic records and enables an objective quantification of the explained variance in isotopic records for individual factors. In this study, the local factors are extracted from local climate records, while the regional factors are derived from atmospheric backward trajectories of water particles. The regional factors, i.e., precipitation, temperature, relative humidity and the length of backward trajectories, are combined with equivalent local climatic parameters to explain the response variables δ18O, δ2H, and d-excess of precipitation at the station of measurement. The results indicate that (i) MLR can better explain the isotopic variation in precipitation (R2 = 0.8) compared to single-factor linear regression (R2 = 0.3); (ii) the isotopic variation in precipitation is controlled dominantly by regional moisture regimes (˜ 70 %) compared to local climatic conditions (˜ 30 %); (iii) the most important climatic parameter during the rainy season is the precipitation amount along the trajectories of air mass movement; (iv) the influence of local precipitation amount and temperature is not significant during the rainy season, unlike the regional precipitation amount effect; (v) secondary fractionation processes (e.g., sub-cloud evaporation) can be identified through the d-excess and take place mainly in the dry season, either locally for δ18O and δ2H, or along the air mass trajectories for d-excess. The analysis shows that regional and local factors vary in importance over the seasons and that the source regions and transport pathways, and particularly the climatic conditions along the pathways, have a large influence on the isotopic composition of rainfall. Although the general results have been reported qualitatively in previous studies (proving the validity of the approach), the proposed method provides quantitative estimates of the controlling factors, both for the whole data set and for distinct seasons. Therefore, it is argued that the approach constitutes an advancement in the statistical analysis of isotopic records in rainfall that can supplement or precede more complex studies utilizing atmospheric models. Due to its relative simplicity, the method can be easily transferred to other regions, or extended with other factors. The results illustrate that the interpretation of the isotopic composition of precipitation as a recorder of local climatic conditions, as for example performed for paleorecords of water isotopes, may not be adequate in the southern part of the Indochinese Peninsula, and likely neither in other regions affected by monsoon processes. However, the presented approach could open a pathway towards better and seasonally differentiated reconstruction of paleoclimates based on isotopic records.

Association of membrane/lipid rafts with the platelet cytoskeleton and the caveolin PY14: participation in the adhesion process.

PubMed

Cerecedo, Doris; Martínez-Vieyra, Ivette; Maldonado-García, Deneb; Hernández-González, Enrique; Winder, Steve J

2015-11-01

Platelets are the most prominent elements of blood tissue involved in hemostasis at sites of blood vessel injury. Platelet cytoskeleton is responsible for their shape modifications observed during activation and adhesion to the substratum; therefore the interactions between cytoskeleton and plasma membrane are critical to modulate blood platelet functions. Several cytoskeletal components and binding partners, as well as enzymes that regulate the cytoskeleton, localize to membrane/lipid rafts (MLR) and regulate lateral diffusion of membrane proteins and lipids. Resting, thrombin-activated, and adherent human platelets were processed for biochemical studies including western-blot and immunprecipitation assays and confocal analysis were performed to characterize the interaction of MLR with the main cytoskeleton elements and β-dystroglycan as well as with the association of caveolin-1 PY14 with focal adhesion proteins. We transfected a megakaryoblast cell line (Meg-01) to deplete β-dystroglycan, subsequent to their differentiation to the platelet progenitors. Our data showed a direct interaction of the MLR with cytoskeleton to regulate platelet shape, while an association of caveolin-1 PY14 with vinculin is needed to establish focal adhesions, which are modulated for β-dystroglycan. In conclusion, caveolin-1 PY14 in association with platelet cytoskeleton participate in focal adhesions dynamics. © 2015 Wiley Periodicals, Inc.

Dynamics of the contralateral white noise-induced enhancement in the guinea pig's middle latency response.

PubMed

Goksoy, Cuneyt; Demirtas, Serdar; Ungan, Pekcan

2004-08-13

The peak-to-peak amplitude of temporal middle latency response (MLR) of the guinea pig, evoked by a click in the contralateral ear, according to the recording side, is increased with the presence of continuous white noise (CWN) in the ipsilateral ear and this specialty is defined as the white noise enhancement (WNE). This phenomenon is evaluated as an interesting electrophysiological finding from the viewpoint of binaural interaction and in this study, its dynamic specifications were investigated. After the beginning of ipsilateral CWN, significant WNE was observed at 275th ms and it reached to a maximum, with an increase more than 40%, at 350th ms. After a habituation occurred, WNE reached to 20% on the 4th second by gradually decreasing and came to a steady state. In the time window between 2 and 5 ms after CWN started, a surprising amplitude decrease is observed. Therefore, CWN causes an effect, like a click, in the short-term and this on-response type effect originates from low level binaural centers, which decreases the MLR amplitude. However, the same CWN increases the MLR amplitude (WNE) by the effects over the high level binaural centers in the succeeding period, by its continuous characteristic.

Lateralization and Binaural Interaction of Middle-Latency and Late-Brainstem Components of the Auditory Evoked Response.

PubMed

Dykstra, Andrew R; Burchard, Daniel; Starzynski, Christian; Riedel, Helmut; Rupp, Andre; Gutschalk, Alexander

2016-08-01

We used magnetoencephalography to examine lateralization and binaural interaction of the middle-latency and late-brainstem components of the auditory evoked response (the MLR and SN10, respectively). Click stimuli were presented either monaurally, or binaurally with left- or right-leading interaural time differences (ITDs). While early MLR components, including the N19 and P30, were larger for monaural stimuli presented contralaterally (by approximately 30 and 36 % in the left and right hemispheres, respectively), later components, including the N40 and P50, were larger ipsilaterally. In contrast, MLRs elicited by binaural clicks with left- or right-leading ITDs did not differ. Depending on filter settings, weak binaural interaction could be observed as early as the P13 but was clearly much larger for later components, beginning at the P30, indicating some degree of binaural linearity up to early stages of cortical processing. The SN10, an obscure late-brainstem component, was observed consistently in individuals and showed linear binaural additivity. The results indicate that while the MLR is lateralized in response to monaural stimuli-and not ITDs-this lateralization reverses from primarily contralateral to primarily ipsilateral as early as 40 ms post stimulus and is never as large as that seen with fMRI.

The EAL domain protein YciR acts as a trigger enzyme in a c-di-GMP signalling cascade in E. coli biofilm control

PubMed Central

Lindenberg, Sandra; Klauck, Gisela; Pesavento, Christina; Klauck, Eberhard; Hengge, Regine

2013-01-01

C-di-GMP—which is produced by diguanylate cyclases (DGC) and degraded by specific phosphodiesterases (PDEs)—is a ubiquitous second messenger in bacterial biofilm formation. In Escherichia coli, several DGCs (YegE, YdaM) and PDEs (YhjH, YciR) and the MerR-like transcription factor MlrA regulate the transcription of csgD, which encodes a biofilm regulator essential for producing amyloid curli fibres of the biofilm matrix. Here, we demonstrate that this system operates as a signalling cascade, in which c-di-GMP controlled by the DGC/PDE pair YegE/YhjH (module I) regulates the activity of the YdaM/YciR pair (module II). Via multiple direct interactions, the two module II proteins form a signalling complex with MlrA. YciR acts as a connector between modules I and II and functions as a trigger enzyme: its direct inhibition of the DGC YdaM is relieved when it binds and degrades c-di-GMP generated by module I. As a consequence, YdaM then generates c-di-GMP and—by direct and specific interaction—activates MlrA to stimulate csgD transcription. Trigger enzymes may represent a general principle in local c-di-GMP signalling. PMID:23708798

Design, development and method validation of a novel multi-resonance microwave sensor for moisture measurement.

PubMed

Peters, Johanna; Taute, Wolfgang; Bartscher, Kathrin; Döscher, Claas; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg

2017-04-08

Microwave sensor systems using resonance technology at a single resonance in the range of 2-3 GHz have been shown to be a rapid and reliable tool for moisture determination in solid materials including pharmaceutical granules. So far, their application is limited to lower moisture ranges or limitations above certain moisture contents had to be accepted. Aim of the present study was to develop a novel multi-resonance sensor system in order to expand the measurement range. Therefore, a novel sensor using additional resonances over a wide frequency band was designed and used to investigate inherent limitations of first generation sensor systems and material-related limits. Using granule samples with different moisture contents, an experimental protocol for calibration and validation of the method was established. Pursuant to this protocol, a multiple linear regression (MLR) prediction model built by correlating microwave moisture values to the moisture determined by Karl Fischer titration was chosen and rated using conventional criteria such as coefficient of determination (R 2 ) and root mean square error of calibration (RMSEC). Using different operators, different analysis dates and different ambient conditions the method was fully validated following the guidance of ICH Q2(R1). The study clearly showed explanations for measurement uncertainties of first generation sensor systems which confirmed the approach to overcome these by using additional resonances. The established prediction model could be validated in the range of 7.6-19.6%, demonstrating its fit for its future purpose, the moisture content determination during wet granulations. Copyright © 2017 Elsevier B.V. All rights reserved.

Reconciling differences in stratospheric ozone composites

NASA Astrophysics Data System (ADS)

Ball, William T.; Alsing, Justin; Mortlock, Daniel J.; Rozanov, Eugene V.; Tummon, Fiona; Haigh, Joanna D.

2017-10-01

Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (˜ 21-48 km) for 1985-2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems - we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

Decline in Immunological Responses Mediated by Dendritic Cells in Mice Treated with 18α-Glycyrrhetinic Acid.

PubMed

Ebrahimnezhad, Salimeh; Amirghofran, Zahra; Karimi, Mohammad Hossein

2016-01-01

18α-Glycyrrhetinic acid (18α-GA), a bioactive component of Glycyrrhiza glabra, has been shown in vitro immunomodulatory effects on dendritic cells (DCs). The aim of the present study is to evaluate the in vivo effect of 18α-GA on DCs and T cell responses. 18α-GA was intraperitoneally administered to mice and splenic DCs were evaluated for expression of co-stimulatory molecules using flow cytometry. Isolated DCs were added to mixed lymphocyte reaction (MLR) and the proliferation of T cells was measured using BrdU assay. The level of IFN-γ in the MLR supernatant was determined by enzyme-linked immunosorbent assay. The in vivo effect of isolated DCs on antigen-specific delayed type hypersensitivity (DTH) response, and the number of regulatory T (Treg) cells in mice spleen by flow cytometry, were investigated. DCs isolated from 18α-GA-treated mice expressed lower levels of CD40 (p < 0.05) and MHC II (p < 0.01) compared to those of control group. In MLR assay isolated DCs decreased T cell proliferation to 83.54% ± 4.3% of control (p < 0.05). The level of IFN-γ in the MLR supernatant was declined to 25.2% ± 6.8% of control. In DTH test, DCs isolated from 18α-GA-treated mice significantly suppressed antigen-specific cell mediated immune response (3.3 ± 1 mm in test group versus 6.5 ± 1.2 mm in control group, ρ < 0.01). The percentage of Treg cells in spleen of 18α-GA-treated mice (6.37% ± 2.3%) was lower than that of control group (13.85% ± 0.4%, ρ < 0.05). In vivo administration of 18α-GA resulted in inhibition of DCs maturation and T cell-mediated responses, the effects that may candidate this compound for its possible benefits in immune-mediated diseases.

Functional Reorganization of the Locomotor Network in Parkinson Patients with Freezing of Gait

PubMed Central

Fling, Brett W.; Cohen, Rajal G.; Mancini, Martina; Carpenter, Samuel D.; Fair, Damien A.; Nutt, John G.; Horak, Fay B.

2014-01-01

Freezing of gait (FoG) is a transient inability to initiate or maintain stepping that often accompanies advanced Parkinson’s disease (PD) and significantly impairs mobility. The current study uses a multimodal neuroimaging approach to assess differences in the functional and structural locomotor neural network in PD patients with and without FoG and relates these findings to measures of FoG severity. Twenty-six PD patients and fifteen age-matched controls underwent resting-state functional magnetic resonance imaging and diffusion tensor imaging along with self-reported and clinical assessments of FoG. After stringent movement correction, fifteen PD patients and fourteen control participants were available for analysis. We assessed functional connectivity strength between the supplementary motor area (SMA) and the following locomotor hubs: 1) subthalamic nucleus (STN), 2) mesencephalic and 3) cerebellar locomotor region (MLR and CLR, respectively) within each hemisphere. Additionally, we quantified structural connectivity strength between locomotor hubs and assessed relationships with metrics of FoG. FoG+ patients showed greater functional connectivity between the SMA and bilateral MLR and between the SMA and left CLR compared to both FoG− and controls. Importantly, greater functional connectivity between the SMA and MLR was positively correlated with i) clinical, ii) self-reported and iii) objective ratings of freezing severity in FoG+, potentially reflecting a maladaptive neural compensation. The current findings demonstrate a re-organization of functional communication within the locomotor network in FoG+ patients whereby the higher-order motor cortex (SMA) responsible for gait initiation communicates with the MLR and CLR to a greater extent than in FoG− patients and controls. The observed pattern of altered connectivity in FoG+ may indicate a failed attempt by the CNS to compensate for the loss of connectivity between the STN and SMA and may reflect a loss of lower-order, automatic control of gait by the basal ganglia. PMID:24937008

Auditory Brainstem and Middle Latency Responses Measured Pre- and Posttreatment for Hyperacusic Hearing-Impaired Persons Successfully Treated to Improve Sound Tolerance and to Expand the Dynamic Range for Loudness: Case Evidence.

PubMed

Formby, Craig; Korczak, Peggy; Sherlock, LaGuinn P; Hawley, Monica L; Gold, Susan

2017-02-01

In this report of three cases, we consider electrophysiologic measures from three hyperacusic hearing-impaired individuals who, prior to treatment to expand their dynamic ranges for loudness, were problematic hearing aid candidates because of their diminished sound tolerance and reduced dynamic ranges. Two of these individuals were treated with structured counseling combined with low-level broadband sound therapy from bilateral sound generators and the third case received structured counseling in combination with a short-acting placebo sound therapy. Each individual was highly responsive to his or her assigned treatment as revealed by expansion of the dynamic range by at least 20 dB at one or more frequencies posttreatment. Of specific interest in this report are their latency and amplitude measures taken from tone burst-evoked auditory brainstem response (ABR) and cortically derived middle latency response (MLR) recordings, measured as a function of increasing loudness at 500 and 2,000 Hz pre- and posttreatment. The resulting ABR and MLR latency and amplitude measures for each case are considered here in terms of pre- and posttreatment predictions. The respective pre- and posttreatment predictions anticipated larger pretreatment response amplitudes and shorter pretreatment response latencies relative to typical normal control values and smaller normative-like posttreatment response amplitudes and longer posttreatment response latencies relative to the corresponding pretreatment values for each individual. From these results and predictions, we conjecture about the neural origins of the hyperacusis conditions (i.e., brainstem versus cortical) and the neuronal sites responsive to treatment. The only consistent finding in support of the pre- and posttreatment predictions and, thus, the strongest index of hyperacusis and positive treatment-related effects was measured for MLR latency responses for wave Pa at 2,000 Hz. Other response indices, including ABR wave V latency and wave V-V' amplitude and MLR wave Na-Pa amplitude for 500 and 2,000 Hz, appear either ambiguous across and/or within these individuals. Notwithstanding significant challenges for interpreting these findings, including associated confounding effects of their sensorineural hearing losses and differences in the presentation levels of the toneburst stimuli used to collect these measures for each individual, our limited analyses of three cases suggest measures of MLR wave Pa latency at 2,000 Hz (reflecting cortical contributions) may be a promising objective indicator of hyperacusis and dynamic range expansion treatment effects.

A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

PubMed

Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

2014-10-15

Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems. Copyright © 2014 Elsevier B.V. All rights reserved.

Inland-coastal water interaction: Remote sensing application for shallow-water quality and algal blooms modeling

NASA Astrophysics Data System (ADS)

Melesse, Assefa; Hajigholizadeh, Mohammad; Blakey, Tara

2017-04-01

In this study, Landsat 8 and Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) sensors were used to model the spatiotemporal changes of four water quality parameters: Landsat 8 (turbidity, chlorophyll-a (chl-a), total phosphate, and total nitrogen) and Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) (algal blooms). The study was conducted in Florda bay, south Florida and model outputs were compared with in-situ observed data. The Landsat 8 based study found that, the predictive models to estimate chl-a and turbidity concentrations, developed through the use of stepwise multiple linear regression (MLR), gave high coefficients of determination in dry season (wet season) (R2 = 0.86(0.66) for chl-a and R2 = 0.84(0.63) for turbidity). Total phosphate and TN were estimated using best-fit multiple linear regression models as a function of Landsat TM and OLI,127 and ground data and showed a high coefficient of determination in dry season (wet season) (R2 = 0.74(0.69) for total phosphate and R2 = 0.82(0.82) for TN). Similarly, the ability of SeaWIFS for chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels' benthic class was evaluated. Benthic class was determined through satellite image-based classification methods. It was found that benthic class based chl-a modeling algorithm was better than the existing regionally-tuned approach. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Key words: Landsat, SeaWIFS, water quality, Florida bay, Chl-a, turbidity

A Novel Dynamic Physical Layer Impairment-Aware Routing and Wavelength Assignment (PLI-RWA) Algorithm for Mixed Line Rate (MLR) Wavelength Division Multiplexed (WDM) Optical Networks

NASA Astrophysics Data System (ADS)

Iyer, Sridhar

2016-12-01

The ever-increasing global Internet traffic will inevitably lead to a serious upgrade of the current optical networks' capacity. The legacy infrastructure can be enhanced not only by increasing the capacity but also by adopting advance modulation formats, having increased spectral efficiency at higher data rate. In a transparent mixed-line-rate (MLR) optical network, different line rates, on different wavelengths, can coexist on the same fiber. Migration to data rates higher than 10 Gbps requires the implementation of phase modulation schemes. However, the co-existing on-off keying (OOK) channels cause critical physical layer impairments (PLIs) to the phase modulated channels, mainly due to cross-phase modulation (XPM), which in turn limits the network's performance. In order to mitigate this effect, a more sophisticated PLI-Routing and Wavelength Assignment (PLI-RWA) scheme needs to be adopted. In this paper, we investigate the critical impairment for each data rate and the way it affects the quality of transmission (QoT). In view of the aforementioned, we present a novel dynamic PLI-RWA algorithm for MLR optical networks. The proposed algorithm is compared through simulations with the shortest path and minimum hop routing schemes. The simulation results show that performance of the proposed algorithm is better than the existing schemes.

The fundamental closed-form solution of control-related states of kth order S3PR system with left-side non-sharing resource places of Petri nets

NASA Astrophysics Data System (ADS)

Chao, Daniel Yuh; Yu, Tsung Hsien

2016-01-01

Due to the state explosion problem, it has been unimaginable to enumerate reachable states for Petri nets. Chao broke the barrier earlier by developing the very first closed-form solution of the number of reachable and other states for marked graphs and the kth order system. Instead of using first-met bad marking, we propose 'the moment to launch resource allocation' (MLR) as a partial deadlock avoidance policy for a large, real-time dynamic resource allocation system. Presently, we can use the future deadlock ratio of the current state as the indicator of MLR due to which the ratio can be obtained real-time by a closed-form formula. This paper progresses the application of an MLR concept one step further on Gen-Left kth order systems (one non-sharing resource place in any position of the left-side process), which is also the most fundamental asymmetric net structure, by the construction of the system's closed-form solution of the control-related states (reachable, forbidden, live and deadlock states) with a formula depending on the parameters of k and the location of the non-sharing resource. Here, we kick off a new era of real-time, dynamic resource allocation decisions by constructing a generalisation formula of kth order systems (Gen-Left) with r* on the left side but at arbitrary locations.

The effect of preterm birth on brainstem, middle latency and cortical auditory evoked responses (BMC AERs).

PubMed

Pasman, J W; Rotteveel, J J; de Graaf, R; Stegeman, D F; Visco, Y M

1992-12-01

Recent studies on the maturation of auditory brainstem evoked responses (ABRs) present conflicting results, whereas only sparse reports exist with respect to the maturation of middle latency auditory evoked responses (MLRs) and auditory cortical evoked responses (ACRs). The present study reports the effect of preterm birth on the maturation of auditory evoked responses in low risk preterm infants (27-34 weeks conceptional age). The ABRs indicate a consistent trend towards longer latencies for all individual ABR components and towards longer interpeak latencies in preterm infants. The MLR shows longer latencies for early component P0 in preterm infants. The ACRs show a remarkable difference between preterm and term infants. At 40 weeks CA the latencies of ACR components Na and P2 are significantly longer in term infants, whereas at 52 weeks CA the latencies of the same ACR components are shorter in term infants. The results support the hypothesis that retarded myelination of the central auditory pathway is partially responsible for differences found between preterm infants and term infants with respect to late ABR components and early MLR component P0. Furthermore, mild conductive hearing loss in preterm infants may also play its role. A more complex mechanism is implicated to account for the findings noted with respect to MLR component Na and ACR components Na and P2.

Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy

NASA Astrophysics Data System (ADS)

Qiu, Peng; D'Souza, Warren D.; McAvoy, Thomas J.; Liu, K. J. Ray

2007-09-01

Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the feedback control system. Our results show that, with the tumor motion unknown but inferred by skin-based markers through the PLS model, the predictive feedback control system was able to effectively compensate intra-fraction motion.

Estimating Catchment-Scale Snowpack Variability in Complex Forested Terrain, Valles Caldera National Preserve, NM

NASA Astrophysics Data System (ADS)

Harpold, A. A.; Brooks, P. D.; Biederman, J. A.; Swetnam, T.

2011-12-01

Difficulty estimating snowpack variability across complex forested terrain currently hinders the prediction of water resources in the semi-arid Southwestern U.S. Catchment-scale estimates of snowpack variability are necessary for addressing ecological, hydrological, and water resources issues, but are often interpolated from a small number of point-scale observations. In this study, we used LiDAR-derived distributed datasets to investigate how elevation, aspect, topography, and vegetation interact to control catchment-scale snowpack variability. The study area is the Redondo massif in the Valles Caldera National Preserve, NM, a resurgent dome that varies from 2500 to 3430 m and drains from all aspects. Mean LiDAR-derived snow depths from four catchments (2.2 to 3.4 km^2) draining different aspects of the Redondo massif varied by 30%, despite similar mean elevations and mixed conifer forest cover. To better quantify this variability in snow depths we performed a multiple linear regression (MLR) at a 7.3 by 7.3 km study area (5 x 106 snow depth measurements) comprising the four catchments. The MLR showed that elevation explained 45% of the variability in snow depths across the study area, aspect explained 18% (dominated by N-S aspect), and vegetation 2% (canopy density and height). This linear relationship was not transferable to the catchment-scale however, where additional MLR analyses showed the influence of aspect and elevation differed between the catchments. The strong influence of North-South aspect in most catchments indicated that the solar radiation is an important control on snow depth variability. To explore the role of solar radiation, a model was used to generate winter solar forcing index (SFI) values based on the local and remote topography. The SFI was able to explain a large amount of snow depth variability in areas with similar elevation and aspect. Finally, the SFI was modified to include the effects of shading from vegetation (in and out of canopy), which further explained snow depth variability. The importance of SFI for explaining catchment-scale snow depth variability demonstrates that aspect is not a sufficient metric for direct radiation in complex terrain where slope and remote topographic shading are significant. Surprisingly, the net effects of interception and shading by vegetation on snow depths were minimal compared to elevation and aspect in these catchments. These results suggest that snowpack losses from interception may be balanced by increased shading to reduce the overall impacts from vegetation compared to topographic factors in this high radiation environment. Our analysis indicated that elevation and solar radiation are likely to control snow variability in larger catchments, with interception and shading from vegetation becoming more important at smaller scales.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models.

PubMed

Rezende, L F C; Arenque-Musa, B C; Moura, M S B; Aidar, S T; Von Randow, C; Menezes, R S C; Ometto, J P B H

2016-06-01

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

PubMed

Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H

2016-10-01

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Continuum beliefs and stigmatising beliefs about mental illness: results from an Asian community survey

PubMed Central

Subramaniam, Mythily; Abdin, Edimansyah; Picco, Louisa; Shahwan, Shazana; Jeyagurunathan, Anitha; Vaingankar, Janhavi Ajit; Chong, Siow Ann

2017-01-01

Objectives To establish the prevalence and correlates of continuum beliefs for five mental illnesses in a multiethnic population and to explore its association with stigma. Design A community-based, cross-sectional study. Setting A national study in a multiethnic Asian country. Participants A comprehensive study of 3006 Singapore residents (Singapore citizens and permanent residents) aged 18–65 years who were living in Singapore at the time of the survey. Outcome measures Parameters assessed included belief in a continuum of symptom experience, stigma dimensions and causal beliefs in mental illness. Statistical analyses included descriptive statistics and multiple linear regression (MLR). Results About half of the population indicated agreement with a continuum of symptoms for depression (57.9%) and dementia (46.8%), whereas only about one in three respondents agreed with it for alcohol abuse (35.6%), schizophrenia (32.7%) and obsessive–compulsive disorder (OCD) (36.8%). MLR analyses revealed that students (β=0.28; 95% CI 0.05 to 0.50; p=0.018) and those who were unemployed (β=0.60; 95% CI 0.26 to 0.95; p=0.001) (vs employed) as well as those who had previous contact with people with mental illness (β = 0.31; 95% CI 0.18 to 0.45; p<0.001) and believed stress, family arguments, difficulties at work or financial difficulties to be a cause for mental illness (β=0.43; 95% CI 0.13 to 0.73; p=0.005) were associated with a higher belief in a continuum of symptom experience. Continuum beliefs were related to lower desire for social distance in alcohol abuse, OCD and schizophrenia; however, they were associated with higher scores on ‘weak-not-sick’ stigma dimension in dementia and schizophrenia. Conclusions Perceiving that a person with a mental illness is similar to themselves may reduce social distancing by the public. Thus, the approach may lend itself well to public education aimed at reducing stigma. PMID:28381420

Associations Between Attitudes Toward Physical Education and Aerobic Capacity in Hungarian High School Students.

PubMed

Kaj, Mónika; Saint-Maurice, Pedro F; Karsai, István; Vass, Zoltán; Csányi, Tamás; Boronyai, Zoltán; Révész, László

2015-06-26

The purpose of this study was to create a physical education (PE) attitude scale and examine how it is associated with aerobic capacity (AC). Participants (n = 961, aged 15-20 years) were randomly selected from 26 Hungarian high schools. AC was estimated from performance on the Progressive Aerobic Cardiovascular and Endurance Run test, and the attitude scale had 31 items measured on a Likert scale that ranged from 1 to 5. Principal component analysis was used to examine the structure of the questionnaire while several 2-way analyses of variance and multiple linear regression (MLR) were computed to examine trends in AC and test the association between component scores obtained from the attitude scale and AC, respectively. Five components were identified: health orientation in PE (C1), avoid failure in PE (C2), success orientation in PE (C3), attitude toward physical activity (C4), and cooperation and social experience in PE (C5). There was a statistically significant main effect for sex on C3 (p < .05), C4 (p < .001), and C5 (p < .05) indicating that boys' scores were higher than girls. The Sex × Age interaction was also statistically significant (p < .05) and follow-up comparisons revealed sex differences in C5 for 15-year-old participants. Girls showed statistically significant higher values than boys in C5 at the age of 16 years. MLR results revealed that component scores were significantly associated with AC (p < .05). Statistically significant predictors included C1, C2, C3, and C4 for boys and C2 and C4 for girls. The 5-component scale seems to be suitable for measuring students' attitudes toward PE. The design of the study does not allow for direct associations between attitudes and AC but suggests that these 2 might be related.

Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff.

PubMed

Paule-Mercado, M A; Ventura, J S; Memon, S A; Jahng, D; Kang, J-H; Lee, C-H

2016-04-15

While the urban runoff are increasingly being studied as a source of fecal indicator bacteria (FIB), less is known about the occurrence of FIB in watershed with mixed land use and ongoing land use and land cover (LULC) change. In this study, Escherichia coli (EC) and fecal streptococcus (FS) were monitored from 2012 to 2013 in agricultural, mixed and urban LULC and analyzed according to the most probable number (MPN). Pearson correlation was used to determine the relationship between FIB and environmental parameters (physicochemical and hydrometeorological). Multiple linear regressions (MLR) were used to identify the significant parameters that affect the FIB concentrations and to predict the response of FIB in LULC change. Overall, the FIB concentrations were higher in urban LULC (EC=3.33-7.39; FS=3.30-7.36log10MPN/100mL) possibly because of runoff from commercial market and 100% impervious cover (IC). Also, during early-summer season; this reflects a greater persistence and growth rate of FIB in a warmer environment. During intra-event, however, the FIB concentrations varied according to site condition. Anthropogenic activities and IC influenced the correlation between the FIB concentrations and environmental parameters. Stormwater temperature (TEMP), turbidity, and TSS positively correlated with the FIB concentrations (p>0.01), since IC increased, implying an accumulation of bacterial sources in urban activities. TEMP, BOD5, turbidity, TSS, and antecedent dry days (ADD) were the most significant explanatory variables for FIB as determined in MLR, possibly because they promoted the FIB growth and survival. The model confirmed the FIB concentrations: EC (R(2)=0.71-0.85; NSE=0.72-0.86) and FS (R(2)=0.65-0.83; NSE=0.66-0.84) are predicted to increase due to urbanization. Therefore, these findings will help in stormwater monitoring strategies, designing the best management practice for FIB removal and as input data for stormwater models. Copyright © 2016 Elsevier B.V. All rights reserved.

Glucose tolerance status of Asian Indian women with gestational diabetes at 6weeks to 1year postpartum (WINGS-7).

PubMed

Bhavadharini, Balaji; Anjana, Ranjit Mohan; Mahalakshmi, Manni Mohanraj; Maheswari, Kumar; Kayal, Arivudainambi; Unnikrishnan, Ranjit; Ranjani, Harish; Ninov, Lyudmil; Pastakia, Sonak D; Usha, Sriram; Malanda, Belma; Belton, Anne; Uma, Ram; Mohan, Viswanathan

2016-07-01

To determine postpartum glucose tolerance status among women with gestational diabetes mellitus (GDM) recruited under the Women In India with GDM Strategy (WINGS) Model of Care (MOC). Through the WINGS MOC programme, 212 women with GDM were followed till delivery between November 2013 and August 2015. All women were advised to return for a postpartum oral glucose tolerance test (OGTT) 6-12weeks after delivery. A multivariate logistic regression (MLR) model was developed to identify the risk factors for postpartum dysglycemia which was defined as presence of diabetes (DM) or prediabetes. 203/212(95.8%) women completed their postpartum OGTT. Of the 161 women (79.3%) who came back for the test between 6 and 12weeks, 2(1.2%) developed DM, 5(3.1%), isolated IFG, 13(8.1%), isolated IGT and 5(3.1%) combined IFG/IGT [dysglycemia 25(15.5%)]. 136 women (84.5%) reverted to normal glucose tolerance (NGT). Of the 42 women who came back between 12weeks and a year, 5(11.9%) developed DM, 10(23.8%), isolated IFG and 1(2.4%) combined IFG/IGT [dysglycemia 16(38.1%)]. 26/42 women (61.9%) reverted to NGT. Thus overall dysglycemia occurred in 41/203 women (20.2%). MLR showed that BMI ⩾25kg/m(2) was significantly associated with postpartum dysglycemia (odds ratio: 4.47; 95% confidence interval: 1.8-11.2, p=0.001). Among Asian Indian women with GDM, over 20% develop dysglycemia within one year postpartum, and BMI ⩾25kg/m(2) increased this risk four-fold. Early postpartum screening can identify high risk women and help plan strategies for prevention of type 2 diabetes in the future. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.

Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin.

PubMed

Kang, Joo-Hyon; Lee, Seung Won; Cho, Kyung Hwa; Ki, Seo Jin; Cha, Sung Min; Kim, Joon Ha

2010-07-01

This study reveals land-use factors that explain stream water quality during wet and dry weather conditions in a large river basin using two different linear models-multiple linear regression (MLR) models and constrained least squares (CLS) models. Six land-use types and three topographical parameters (size, slope, and permeability) of the watershed were incorporated into the models as explanatory variables. The suggested models were then demonstrated using a digitized elevation map in conjunction with the land-use and the measured concentration data for Escherichia coli (EC), Enterococci bacteria (ENT), and six heavy metal species collected monthly during 2007-2008 at 50 monitoring sites in the Yeongsan Watershed, Korea. The results showed that the MLR models can be a powerful tool for predicting the average concentrations of pollutants in stream water (the Nash-Sutcliffe (NS) model efficiency coefficients ranged from 0.67 to 0.95). On the other hand, the CLS models, with moderately good prediction performance (the NS coefficients ranged 0.28-0.85), were more suitable for quantifying contributions of respective land-uses to the stream water quality. The CLS models suggested that industrial and urban land-uses are major contributors to the stream concentrations of EC and ENT, whereas agricultural, industrial, and mining areas were significant sources of many heavy metal species. In addition, the slope, size, and permeability of the watershed were found to be important factors determining the extent of the contribution from each land-use type to the stream water quality. The models proposed in this paper can be considered useful tools for developing land cover guidelines and for prioritizing locations for implementing management practices to maintain stream water quality standard in a large river basin. Copyright 2010 Elsevier Ltd. All rights reserved.

Variables that influence BRAF mutation probability: A next-generation sequencing, non-interventional investigation of BRAFV600 mutation status in melanoma.

PubMed

Gaiser, Maria Rita; Skorokhod, Alexander; Gransheier, Diana; Weide, Benjamin; Koch, Winfried; Schif, Birgit; Enk, Alexander; Garbe, Claus; Bauer, Jürgen

2017-01-01

The incidence of melanoma, particularly in older patients, has steadily increased over the past few decades. Activating mutations of BRAF, the majority occurring in BRAFV600, are frequently detected in melanoma; however, the prognostic significance remains unclear. This study aimed to define the probability and distribution of BRAFV600 mutations, and the clinico-pathological factors that may affect BRAF mutation status, in patients with advanced melanoma using next-generation sequencing. This was a non-interventional, retrospective study of BRAF mutation testing at two German centers, in Heidelberg and Tübingen. Archival tumor samples from patients with histologically confirmed melanoma (stage IIIB, IIIC, IV) were analyzed using PCR amplification and deep sequencing. Clinical, histological, and mutation data were collected. The statistical influence of patient- and tumor-related characteristics on BRAFV600 mutation status was assessed using multiple logistic regression (MLR) and a prediction profiler. BRAFV600 mutation status was assessed in 453 samples. Mutations were detected in 57.6% of patients (n = 261), with 48.1% (n = 102) at the Heidelberg site and 66.0% (n = 159) at the Tübingen site. The decreasing influence of increasing age on mutation probability was quantified. A main effects MLR model identified age (p = 0.0001), center (p = 0.0004), and melanoma subtype (p = 0.014) as significantly influencing BRAFV600 mutation probability; ultraviolet (UV) exposure showed a statistical trend (p = 0.1419). An interaction model of age versus other variables showed that center (p<0.0001) and melanoma subtype (p = 0.0038) significantly influenced BRAF mutation probability; age had a statistically significant effect only as part of an interaction with both UV exposure (p = 0.0110) and melanoma subtype (p = 0.0134). This exploratory study highlights that testing center, melanoma subtype, and age in combination with UV exposure and melanoma subtype significantly influence BRAFV600 mutation probability in patients with melanoma. Further validation of this model, in terms of reproducibility and broader relevance, is required.

On the Response of Halogen Occultation Experiment (HALOE) Stratospheric Oxone and Temperature to the 11-yr Solar Cycle Forcing

NASA Technical Reports Server (NTRS)

Remsberg, E. E.

2008-01-01

Results are presented on responses in 14-yr time series of stratospheric ozone and temperature from the Halogen Occultation Experiment (HALOE) of the Upper Atmosphere Research Satellite (UARS) to a solar cycle (SC-like) variation. The ozone time series are for ten, 20-degree wide, latitude bins from 45S to 45N and for thirteen "half-Umkehr" layers of about 2.5 km thickness and extending from 63 hPa to 0.7 hPa. The temperature time series analyses were restricted to pressure levels in the range of 2 hPa to 0.7 hPa. Multiple linear regression (MLR) techniques were applied to each of the 130 time series of zonally-averaged, sunrise plus sunset ozone points over that latitude/pressure domain. A simple, 11-yr periodic term and a linear trend term were added to the final MLR models after their seasonal and interannual terms had been determined. Where the amplitudes of the 11-yr terms were significant, they were in-phase with those of the more standard proxies for the solar uv-flux. The max minus min response for ozone is of order 2 to 3% from about 2 to 5 hPa and for the latitudes of 45S to 45N. There is also a significant max minus min response of order 1 K for temperature between 15S and 15N and from 2 to 0.7 hPa. The associated linear trends for ozone are near zero in the upper stratosphere. Negative ozone trends of 4 to 6%/decade were found at 10 to 20 hPa across the low to middle latitudes of both hemispheres. It is concluded that the analyzed responses from the HALOE data are of good quality and can be used to evaluate the responses of climate/chemistry models to a solar cycle forcing.

T cell function in tuatara (Sphenodon punctatus).

PubMed

Burnham, D Kim; Keall, Susan N; Nelson, Nicola J; Daugherty, Charles H

2005-05-01

Tuatara are the sole survivors of an entire order of reptiles that thrived during the age of the dinosaurs. Therefore, knowledge of their physiology is critical to understanding the phylogeny of reptiles. Previous studies of the immune system of the tuatara did not assess T cell function. We analyzed T cell function among six captive tuatara by assessing concanavalin A (Con A), phytohemagglutinin (PHA) and mixed lymphocyte reaction (MLR) induced T cell proliferation. Peripheral blood mononuclear cells from six out of six and four out of four tuatara tested exhibited significant proliferative responses to Con A and PHA, respectively, as measured by an MTT reduction assay. A lower level of proliferation was detected in an MLR. However, Con A activated lymphocytes were not cytotoxic for a xenogeneic murine mastocytoma cell line (P815).

Controlled release formulations of risperidone antipsychotic drug in novel aliphatic polyester carriers: Data analysis and modelling.

PubMed

Siafaka, Panoraia I; Barmpalexis, Panagiotis; Lazaridou, Maria; Papageorgiou, George Z; Koutris, Efthimios; Karavas, Evangelos; Kostoglou, Margaritis; Bikiaris, Dimitrios N

2015-08-01

In the present study a series of biodegradable and biocompatible poly(ε-caprolactone)/poly(propylene glutarate) (PCL/PPGlu) polymer blends were investigated as controlled release carriers of Risperidone drug (RISP), appropriate for transdermal drug delivery. The PCL/PPGlu carriers were prepared in different weight ratios. Miscibility studies of blends were evaluated through differential scanning calorimetry (DSC) and X-ray diffractometry (XRD). Hydrolysis studies were performed at 37°C using a phosphate buffered saline solution. The prepared blends have been used for the preparation of RISP patches via solvent evaporation method, containing 5, 10 and 15wt% RISP. These formulations were characterized using FT-IR spectroscopy, DSC and WAXD in order to evaluate interactions taking place between polymer matrix and drug, as well as the dispersion and the physical state of the drug inside the polymer matrix. In vitro drug release studies were performed using as dissolution medium phosphate buffered saline simulating body fluids. It was found that in all cases controlled release formulations were obtained, while the RISP release varies due to the properties of the used polymer blend and the different levels of drug loading. Artificial Neural Networks (ANNs) were used for dissolution behaviour modelling showing increased correlation efficacy compared to Multi-Linear-Regression (MLR). Copyright © 2015 Elsevier B.V. All rights reserved.

A quantum chemical study of molecular properties and QSPR modeling of oximes, amidoximes and hydroxamic acids with nucleophilic activity against toxic organophosphorus agents

NASA Astrophysics Data System (ADS)

Alencar Filho, Edilson B.; Santos, Aline A.; Oliveira, Boaz G.

2017-04-01

The proposal of this work includes the use of quantum chemical methods and cheminformatics strategies in order to understand the structural profile and reactivity of α-nucleophiles compounds such as oximes, amidoximes and hydroxamic acids, related to hydrolysis rate of organophosphates. Theoretical conformational study of 41 compounds were carried out through the PM3 semiempirical Hamiltonian, followed by the geometry optimization at the B3LYP/6-31+G(d,p) level of theory, complemented by Polarized Continuum Model (PCM) to simulate the aqueous environment. In line with the experimental hypothesis about hydrolytic power, the strength of the Intramolecular Hydrogen Bonds (IHBs) at light of the Bader's Quantum Theory of Atoms in Molecules (QTAIM) is related to the preferential conformations of α-nucleophiles. A set of E-Dragon descriptors (1,666) were submitted to a variable selection through Ordered Predictor Selection (OPS) algorithm. Five descriptors, including atomic charges obtained from the Natural Bond Orbitals (NBO) protocol jointly with a fragment index associated to the presence/absence of IHBs, provided a Quantitative Structure-Property Relationship (QSPR) model via Multiple Linear Regression (MLR). This model showed good validation parameters (R2 = 0.80, Qloo2 = 0.67 and Qext2 = 0.81) and allowed the identification of significant physicochemical features on the molecular scaffold in order to design compounds potentially more active against organophosphorus poisoning.

Estimation of Theaflavins (TF) and Thearubigins (TR) Ratio in Black Tea Liquor Using Electronic Vision System

NASA Astrophysics Data System (ADS)

Akuli, Amitava; Pal, Abhra; Ghosh, Arunangshu; Bhattacharyya, Nabarun; Bandhopadhyya, Rajib; Tamuly, Pradip; Gogoi, Nagen

2011-09-01

Quality of black tea is generally assessed using organoleptic tests by professional tea tasters. They determine the quality of black tea based on its appearance (in dry condition and during liquor formation), aroma and taste. Variation in the above parameters is actually contributed by a number of chemical compounds like, Theaflavins (TF), Thearubigins (TR), Caffeine, Linalool, Geraniol etc. Among the above, TF and TR are the most important chemical compounds, which actually contribute to the formation of taste, colour and brightness in tea liquor. Estimation of TF and TR in black tea is generally done using a spectrophotometer instrument. But, the analysis technique undergoes a rigorous and time consuming effort for sample preparation; also the operation of costly spectrophotometer requires expert manpower. To overcome above problems an Electronic Vision System based on digital image processing technique has been developed. The system is faster, low cost, repeatable and can estimate the amount of TF and TR ratio for black tea liquor with accuracy. The data analysis is done using Principal Component Analysis (PCA), Multiple Linear Regression (MLR) and Multiple Discriminate Analysis (MDA). A correlation has been established between colour of tea liquor images and TF, TR ratio. This paper describes the newly developed E-Vision system, experimental methods, data analysis algorithms and finally, the performance of the E-Vision System as compared to the results of traditional spectrophotometer.

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

NASA Astrophysics Data System (ADS)

Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

2015-10-01

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

Assessment of polycyclic aromatic hydrocarbons (PAHs) contamination in surface soil of coal stockpile sites in South Kalimantan, Indonesia.

PubMed

Mizwar, Andy; Priatmadi, Bambang Joko; Abdi, Chairul; Trihadiningrum, Yulinah

2016-03-01

Concentrations, spatial distribution, and sources of 16 polycyclic aromatic hydrocarbons (PAHs), listed as priority pollutants by the United States Environmental Protection Agency (USEPA), were investigated in surface soils of three different coal stockpile, agricultural, and residential sites in South Kalimantan Province, Indonesia. Total PAHs concentration ranged from 4.69 to 22.67 mg kg(-1)-dw. PAHs concentrations in soil of coal stockpile sites were higher than those in agricultural and residential soil. A complex of petrogenic origin and pyrolytic sources was found within the study area, as suggested by the isomeric ratios of PAHs. The results of principal component analysis and multiple linear regressions (PCA/MLR) showed that three sources contributed to the PAHs in the study area, including biomass and coal combustion (48.46%), raw coal (35.49%), and vehicular emission (16.05%). The high value of total benzo[a]pyrene equivalent concentration (B[a]Peq) suggests that local residents are exposed to a high carcinogenic potential.

In Silico Investigation of Traditional Chinese Medicine Compounds to Inhibit Human Histone Deacetylase 2 for Patients with Alzheimer's Disease

PubMed Central

Hung, Tzu-Chieh; Lee, Wen-Yuan; Chen, Kuen-Bao; Chan, Yueh-Chiu; Lee, Cheng-Chun

2014-01-01

Human histone deacetylase 2 (HDAC2) has been identified as being associated with Alzheimer's disease (AD), a neuropathic degenerative disease. In this study, we screen the world's largest Traditional Chinese Medicine (TCM) database for natural compounds that may be useful as lead compounds in the search for inhibitors of HDAC2 function. The technique of molecular docking was employed to select the ten top TCM candidates. We used three prediction models, multiple linear regression (MLR), support vector machine (SVM), and the Bayes network toolbox (BNT), to predict the bioactivity of the TCM candidates. Molecular dynamics simulation provides the protein-ligand interactions of compounds. The bioactivity predictions of pIC50 values suggest that the TCM candidatesm, (−)-Bontl ferulate, monomethylcurcumin, and ningposides C, have a greater effect on HDAC2 inhibition. The structure variation caused by the hydrogen bonds and hydrophobic interactions between protein-ligand interactions indicates that these compounds have an inhibitory effect on the protein. PMID:25045700

Are chirps better than clicks and tonebursts for evoking middle latency responses?

PubMed

Atcherson, Samuel R; Moore, Page C

2014-06-01

The middle latency response (MLR) is considered a valid clinical tool for assessing the integrity of cortical and subcortical structures. Several investigators have demonstrated that a rising frequency chirp stimulus is capable of eliciting not only larger wave V amplitudes but larger MLR components as well. However, the chirp has never been specifically examined in a hemispheric electrode montage setup that is typical for neurodiagnostic application and site-of-lesion testing. The purpose of this study was to examine the effect of chirp, click, and toneburst stimuli on MLR waveform peak latency and peak-to-peak amplitude in a hemispheric electrode montage setup. This study used a repeated-measures design. A total of 10 young adult participants (3 males, 7 females) with normal hearing were recruited and had negative histories of audiologic, otologic, and neurologic involvement, and no reported language or learning difficulties. MLR latencies (Na, Pa, Nb, and Pb) and peak-to-peak amplitudes (Na-Pa, Pa-Nb, and Nb-Pb) were measured for all conditions and were statistically evaluated for left hemisphere-right ear (C3-A2) and right hemisphere-left ear (C4-A1) recordings. Statistical analyses revealed no significant difference between C3-A2 and C4-A1 peak-to-peak amplitudes; therefore, data were collapsed. Stimulus comparisons revealed that Na evoked by tonebursts were statistically prolonged compared with both chirp and click, and that both Na-Pa and Pa-Nb peak-to-peak amplitudes were statistically larger for chirps compared with both clicks and tonebursts, and for clicks compared with tonebursts. The results of this study support the hypothesis that a chirp would offer a clinical advantage to the click and toneburst in overall peak-to-peak amplitude. As expected, normal-hearing participants did not exhibit hemispheric differences when comparing C3-A2 and C4-A1 peak-to-peak amplitudes demonstrating symmetric auditory brain function. However, chirp-evoked MLRs will require further study to determine its usefulness in clinical practice. American Academy of Audiology.

Improving MAVEN-IUVS Lyman-Alpha Apoapsis Images

NASA Astrophysics Data System (ADS)

Chaffin, M.; AlMannaei, A. S.; Jain, S.; Chaufray, J. Y.; Deighan, J.; Schneider, N. M.; Thiemann, E.; Mayyasi, M.; Clarke, J. T.; Crismani, M. M. J.; Stiepen, A.; Montmessin, F.; Epavier, F.; McClintock, B.; Stewart, I. F.; Holsclaw, G.; Jakosky, B. M.

2017-12-01

In 2013, the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission was launched to study the Martian upper atmosphere and ionosphere. MAVEN orbits through a very thin cloud of hydrogen gas, known as the hydrogen corona, that has been used to explore the planet's geologic evolution by detecting the loss of hydrogen from the atmosphere. Here we present various methods of extracting properties of the hydrogen corona from observations using MAVEN's Imaging Ultraviolet Spectograph (IUVS) instrument. The analysis presented here uses the IUVS Far Ultraviolet mode apoapase data. From apoapse, IUVS is able to obtain images of the hydrogen corona by detecting the Lyman-alpha airglow using a combination of instrument scan mirror and spacecraft motion. To complete one apoapse observation, eight scan swaths are performed to collect the observations and construct a coronal image. However, these images require further processing to account for the atmospheric MUV background that hinders the quality of the data. Here, we present new techniques for correcting instrument data. For the background subtraction, a multi-linear regression (MLR) routine of the first order MUV radiance was used to improve the images. A flat field correction was also applied by fitting a polynomial to periapse radiance observations. The apoapse data was re-binned using this fit.The results are presented as images to demonstrate the improvements in the data reduction. Implementing these methods for more orbits will improve our understanding of seasonal variability and H loss. Asymmetries in the Martian hydrogen corona can also be assessed to improve current model estimates of coronal H in the Martian atmosphere.

Dendritic cells induce antigen-specific regulatory T cells that prevent graft versus host disease and persist in mice

PubMed Central

Olds, Peter; Park, Andrew; Schlesinger, Sarah J.

2011-01-01

Foxp3+ regulatory T cells (T reg cells) effectively suppress immunity, but it is not determined if antigen-induced T reg cells (iT reg cells) are able to persist under conditions of inflammation and to stably express the transcription factor Foxp3. We used spleen cells to stimulate the mixed leukocyte reaction (MLR) in the presence of transforming growth factor β (TGF-β) and retinoic acid. We found that the CD11chigh dendritic cell fraction was the most potent at inducing high numbers of alloreactive Foxp3+ cells. The induced CD4+CD25+Foxp3+ cells appeared after extensive proliferation. When purified from the MLR, iT reg cells suppressed both primary and secondary MLR in vitro in an antigen-specific manner. After transfer into allogeneic mice, iT reg cells persisted for 6 mo and prevented graft versus host disease (GVHD) caused by co-transferred CD45RBhi T cells. Similar findings were made when iT reg cells were transferred after onset of GVHD. The CNS2 intronic sequence of the Foxp3 gene in the persisting iT reg cells was as demethylated as the corresponding sequence of naturally occurring T reg cells. These results indicate that induced Foxp3+ T reg cells, after proliferating and differentiating into antigen-specific suppressive T cells, can persist for long periods while suppressing a powerful inflammatory disease. PMID:22084406

Immunomodulatory properties of titanium dioxide nanostructural materials.

PubMed

Latha, T Sree; Reddy, Madhava C; R Durbaka, Prasad V; Muthukonda, Shankar V; Lomada, Dakshayani

2017-01-01

Although titanium dioxide (TiO 2 ) nanostructural materials have been widely used in Biology and Medicine, very little is known about immunomodulation mechanism of these materials. Objectives of this study are to investigate in vitro immunomodulatory effects of TiO 2 . Immunosuppressant may lower immune responses and are helpful for the treatment of graft versus host diseases and autoimmune disorders. In this study, we used H 2 Ti 3 O 7 titanium dioxide nanotubes (TNT) nanotubes along with commercial TiO 2 nanoparticles (TNP) and TiO 2 fine particles (TFP). We investigated the in vitro immunomodulatory effects of TNP, TNT, and TFP using mixed lymphocyte reaction (MLR). Suppression was studied by 3-(4, 5-dimethylthiazol-2yl)-2, 5-diphenyl tetrazolium bromide (MTT) assay. Cytokine profile was measured by enzyme-linked immunosorbent assay (ELISA). The results from this study illustrated that the TiO 2 nanostructural materials strongly suppressed splenocytes proliferation in MLR. For TNP and TNT, at 50 μg/ml suppression of 20%-25% and 30%-35%, respectively, and for TFP at 100 μg/ml suppression was 25%-30% was observed. Suppression of splenocytes proliferation in the presence of TNP, TNT, and TFP demonstrated that these nanostructural materials probably block T-cell-mediated responses in vitro . Our ELISA results confirmed that significantly lower levels of Th1 type cytokines (interleukin-2, interferon-γ) in the 48 h MLR culture supernatants. Our data suggest that TiO 2 nanostructural materials suppress splenocytes proliferation by suppressing Th1 cytokines.

Applying the Expectancy-Value Model to understand health values.

PubMed

Zhang, Xu-Hao; Xie, Feng; Wee, Hwee-Lin; Thumboo, Julian; Li, Shu-Chuen

2008-03-01

Expectancy-Value Model (EVM) is the most structured model in psychology to predict attitudes by measuring attitudinal attributes (AAs) and relevant external variables. Because health value could be categorized as attitude, we aimed to apply EVM to explore its usefulness in explaining variances in health values and investigate underlying factors. Focus group discussion was carried out to identify the most common and significant AAs toward 5 different health states (coded as 11111, 11121, 21221, 32323, and 33333 in EuroQol Five-Dimension (EQ-5D) descriptive system). AAs were measured in a sum of multiplications of subjective probability (expectancy) and perceived value of attributes with 7-point Likert scales. Health values were measured using visual analog scales (VAS, range 0-1). External variables (age, sex, ethnicity, education, housing, marital status, and concurrent chronic diseases) were also incorporated into survey questionnaire distributed by convenience sampling among eligible respondents. Univariate analyses were used to identify external variables causing significant differences in VAS. Multiple linear regression model (MLR) and hierarchical regression model were used to investigate the explanatory power of AAs and possible significant external variable(s) separately or in combination, for each individual health state and a mixed scenario of five states, respectively. Four AAs were identified, namely, "worsening your quality of life in terms of health" (WQoL), "adding a burden to your family" (BTF), "making you less independent" (MLI) and "unable to work or study" (UWS). Data were analyzed based on 232 respondents (mean [SD] age: 27.7 [15.07] years, 49.1% female). Health values varied significantly across 5 health states, ranging from 0.12 (33333) to 0.97 (11111). With no significant external variables identified, EVM explained up to 62% of the variances in health values across 5 health states. The explanatory power of 4 AAs were found to be between 13% and 28% in separate MLR models (P < 0.05). When data were analyzed for each health state, variances in health values became small and explanatory power of EVM was reduced to a range between 8% and 23%. EVM was useful in explaining variances of health values and predicting important factors. Its power to explain small variances might be restricted due to limitations of 7-point Likert scale to measure AAs accurately. With further improvement and validation of a compatible continuous scale for more accurate measurement, EVM is expected to explain health values to a larger extent.

Smoking behaviour predicts tobacco control attitudes in a high smoking prevalence hospital: A cross-sectional study in a Portuguese teaching hospital prior to the national smoking ban

PubMed Central

2011-01-01

Background Several studies have investigated attitudes to and compliance with smoking bans, but few have been conducted in healthcare settings and none in such a setting in Portugal. Portugal is of particular interest because the current ban is not in line with World Health Organization recommendations for a "100% smoke-free" policy. In November 2007, a Portuguese teaching-hospital surveyed smoking behaviour and tobacco control (TC) attitudes before the national ban came into force in January 2008. Methods Questionnaire-based cross-sectional study, including all eligible staff. Sample: 52.9% of the 1, 112 staff; mean age 38.3 ± 9.9 years; 65.9% females. Smoking behaviour and TC attitudes and beliefs were the main outcomes. Bivariable analyses were conducted using chi-squared and MacNemar tests to compare categorical variables and Mann-Whitney tests to compare medians. Multilogistic regression (MLR) was performed to identify factors associated with smoking status and TC attitudes. Results Smoking prevalence was 40.5% (95% CI: 33.6-47.4) in males, 23.5% (95% CI: 19.2-27.8) in females (p < 0.001); 43.2% in auxiliaries, 26.1% in nurses, 18.9% among physicians, and 34.7% among other non-health professionals (p = 0.024). The findings showed a very high level of agreement with smoking bans, even among smokers, despite the fact that 70.3% of the smokers smoked on the premises and 76% of staff reported being frequently exposed to second-hand smoke (SHS). In addition 42.8% reported that SHS was unpleasant and 28.3% admitted complaining. MLR showed that smoking behaviour was the most important predictor of TC attitudes. Conclusions Smoking prevalence was high, especially among the lower socio-economic groups. The findings showed a very high level of support for smoking bans, despite the pro-smoking environment. Most staff reported passive behaviour, despite high SHS exposure. This and the high smoking prevalence may contribute to low compliance with the ban and low participation on smoking cessation activities. Smoking behaviour had greater influence in TC attitudes than health professionals' education. Our study is the first in Portugal to identify potential predictors of non-compliance with the partial smoking ban, further emphasising the need for a 100% smoke-free policy, effective enforcement and public health education to ensure compliance and promote social norm change. PMID:21943400

Toddler physical activity study: laboratory and community studies to evaluate accelerometer validity and correlates.

PubMed

Hager, Erin R; Gormley, Candice E; Latta, Laura W; Treuth, Margarita S; Caulfield, Laura E; Black, Maureen M

2016-09-06

Toddlerhood is an important age for physical activity (PA) promotion to prevent obesity and support a physically active lifestyle throughout childhood. Accurate assessment of PA is needed to determine trends/correlates of PA, time spent in sedentary, light, or moderate-vigorous PA (MVPA), and the effectiveness of PA promotion programs. Due to the limited availability of objective measures that have been validated and evaluated for feasibility in community studies, it is unclear which subgroups of toddlers are at the highest risk for inactivity. Using Actical ankle accelerometry, the objectives of this study are to develop valid thresholds, examine feasibility, and examine demographic/ anthropometric PA correlates of MVPA among toddlers from low-income families. Two studies were conducted with toddlers (12-36 months). Laboratory Study (n = 24)- Two Actical accelerometers were placed on the ankle. PA was observed using the Child Activity Rating Scale (CARS, prescribed activities). Analyses included device equivalence reliability (correlation: activity counts of two Acticals), criterion-related validity (correlation: activity counts and CARS ratings), and sensitivity/specificity for thresholds. Community Study (n = 277, low-income mother-toddler dyads recruited)- An Actical was worn on the ankle for > 7 days (goal >5, 24-h days). Height/weight was measured. Mothers reported demographics. Analyses included frequencies (feasibility) and stepwise multiple linear regression (sMLR). Laboratory Study- Acticals demonstrated reliability (r = 0.980) and validity (r = 0.75). Thresholds demonstrated sensitivity (86 %) and specificity (88 %). Community Study- 86 % wore accelerometer, 69 % had valid data (mean = 5.2 days). Primary reasons for missing/invalid data: refusal (14 %) and wear-time ≤2 days (11 %). The MVPA threshold (>2200 cpm) yielded 54 min/day. In sMLR, MVPA was associated with age (older > younger, β = 32.8, p < 0.001), gender (boys > girls, β = -11.21, p = 0.032), maternal MVPA (β = 0.44, p = 0.002) and recruitment location (suburban > urban, β = 19.6, p < 0.001), or race (non-Black > Black, β = 18.5, p = 0.001). No association with toddler weight status. Ankle accelerometry is a valid, reliable, and feasible method of assessing PA in community studies of toddlers from low-income families. Sub-populations of toddlers may be at increased risk for inactivity, including toddlers that are younger, female, Black, those with less active mothers, and those living in an urban location.

75 FR 74863 - Health Insurance Issuers Implementing Medical Loss Ratio (MLR) Requirements Under the Patient...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-01

... regarding that topic, rather than here. For example, ``aggregation'' is addressed in Sec. 158.120... would disaggregate products by type of coverage--for example, HMO, PPO, and high-deductible coverage...

Insurers' medical loss ratios and quality improvement spending in 2011.

PubMed

Hall, Mark A; McCue, Michael J

2013-03-01

The Affordable Care Act's medical loss ratio (MLR) regulation requires insurers to spend 80 percent or 85 percent of premiums on medical claims and quality improvements. In 2011, insurers falling below this minimum paid more than $1 billion in rebates. This brief examines how insurers spend their premium dollars--particularly their investment in quality improvement activities--focusing on differences among insurers based on corporate traits. In the aggregate, insurers paid less than 1 percent of premiums on either MLR rebates or quality improvement activities in 2011, with amounts varying by insurer type. Publicly traded insurers had significantly lower MLRs in each market segment (individual, small group, and large group), and were more likely to owe a rebate in most segments compared with non-publicly traded insurers. The median quality improvement expenditure per member among nonprofit and provider-sponsored insurers was more than the median among for-profit and non-provider-sponsored insurers.

Early indices of deviance detection in humans and animal models.

PubMed

Grimm, Sabine; Escera, Carles; Nelken, Israel

2016-04-01

Detecting unexpected stimuli in the environment is a critical function of the auditory system. Responses to unexpected "deviant" sounds are enhanced compared to responses to expected stimuli. At the human scalp, deviance detection is reflected in the mismatch negativity (MMN) and in an enhancement of the middle-latency response (MLR). Single neurons often respond more strongly to a stimulus when rare than when common, a phenomenon termed stimulus-specific adaptation (SSA). Here we compare stimulus-specific adaptation with scalp-recorded deviance-related responses. We conclude that early markers of deviance detection in the time range of the MLR could be a direct correlate of cortical SSA. Both occur at an early level of cortical activation, both are robust findings with low-probability stimuli, and both show properties of genuine deviance detection. Their causal relation with the later scalp-recorded MMN is a key question in this field. Copyright © 2015 Elsevier B.V. All rights reserved.

Mid-latency auditory evoked potential response revealed as an evidence of neural plasticity in blinnd individuals.

PubMed

Muthusamy, Anbarasi; Gajendran, Rajkumar; Rao B, Vishwanatha

2014-01-01

There is a general impression that visually blind individuals show an exceptionally better perception of other sensory modalities such as hearing, touch and smell sensations. In this study, we intended to compare the mid-latency auditory evoked potential response (MLAEP) or Middle latency Response (MLR) to get an idea of the activity pattern of auditory thalamus and cortex between 30 visually handicapped subjects and 30 normal sighted subjects. The results showed a decrease in many of the MLR wave latencies, but highly significant for the wave Pa (P value <0.002). This fact can be reflected as an evidence of existence of cross-modal neuroplasticity. We also inferred that there are significant gender differences with latencies shorter in males than females (P value <0.02) in the blind subjects group which could be attributed to their rehabilitation training.

Evaluation of the immunomodulatory effect of the 14 kDa protein isolated from aged garlic extract on dendritic cells.

PubMed

Ahmadabad, Hasan Namdar; Hassan, Zuhair Mohammad; Safari, Elahe; Bozorgmehr, Mahmood; Ghazanfari, Tooba; Moazzeni, Seyed Mohammad

2011-01-01

Garlic is used all over the world for treatment of different diseases. A wide range of biological activities of garlic has been verified in vitro and in vivo. One of major proteins of garlic which has been isolated and purified is the 14 kDa protein. This protein has been shown to have immunomodulatory effects. In this study, the effect of the 14 kDa protein isolated from aged garlic extract (AGE) was investigated on maturation and immunomodulatory activity of dendritic cells (DC). Proteins were purified from AGE by biochemical method; the semi-purified 14 kDa protein was run on gel filtration Sephadex G50 and its purity was checked by SDS-PAGE. DC were isolated from spleen of BALB/c mice by Nycodenz centrifugation and their adhesiveness to plastic dish. 14 kDa protein from AGE was added to overnight culture of DC medium and the expression percentage of CD40, CD86, and MHC-II was evaluated by flowcytometric analysis. Also, proliferation of T-cells was measured by allogenic mixed lymphocyte reaction (MLR) test. The purified 14 kDa protein isolated from AGE increased the expression of CD40 molecule on DC, but it did not influence CD86 and MHCII molecules. Furthermore, no significant differences were noticed in the pulsed-DC with 14 kDa protein and non-pulsed DC on the MLR. Copyright © 2011 Elsevier Inc. All rights reserved.

Developing Data-driven models for quantifying Cochlodinium polykrikoides in Coastal Waters

NASA Astrophysics Data System (ADS)

Kwon, Yongsung; Jang, Eunna; Im, Jungho; Baek, Seungho; Park, Yongeun; Cho, Kyunghwa

2017-04-01

Harmful algal blooms have been worldwide problems because it leads to serious dangers to human health and aquatic ecosystems. Especially, fish killing red tide blooms by one of dinoflagellate, Cochlodinium polykrikoides (C. polykrikoides), have caused critical damage to mariculture in the Korean coastal waters. In this work, multiple linear regression (MLR), regression tree (RT), and random forest (RF) models were constructed and applied to estimate C. polykrikoides blooms in coastal waters. Five different types of input dataset were carried out to test the performance of three models. To train and validate the three models, observed number of C. polykrikoides cells from National institute of fisheries science (NIFS) and remote sensing reflectance data from Geostationary Ocean Color Imager (GOCI) images for 3 years from 2013 to 2015 were used. The RT model showed the best prediction performance when using 4 bands and 3 band ratios data were used as input data simultaneously. Results obtained from iterative model development with randomly chosen input data indicated that the recognition of patterns in training data caused a variation in prediction performance. This work provided useful tools for reliably estimate the number of C. polykrikoides cells by using reasonable input reflectance dataset in coastal waters. It is expected that the RT model is easily accessed and manipulated by administrators and decision-makers working with coastal waters.

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4).

PubMed

Sun, Lili; Zhou, Liping; Yu, Yu; Lan, Yukun; Li, Zhiliang

2007-01-01

Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of 209 PCDEs and diphenyl ether. The correlation coefficients (R) and the leave-one-out cross-validation (LOO) correlation coefficients (R(CV)) of all the 6-descriptor models for logP(L), logK(OW) and logS(W,L) are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for logP(L), 4-descriptor model for logK(OW), and 6-descriptor model for logS(W,L), respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for logP(L) is the best (R=0.996, R(CV)=0.996), followed by logK(OW) (R=0.992, R(CV)=0.992) and logS(W,L) (R=0.983, R(CV)=0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using LIDAR and Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Zaki, N. A. M.; Latif, Z. A.; Suratman, M. N.; Zainal, M. Z.

2016-06-01

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.

Degradation of [Dha7]MC-LR by a Microcystin Degrading Bacterium Isolated from Lake Rotoiti, New Zealand

PubMed Central

Somdee, Theerasak; Ruck, John; Lys, Isabelle; Allison, Margaret; Page, Rachel

2013-01-01

For the first time a microcystin-degrading bacterium (NV-3 isolate) has been isolated and characterized from a NZ lake. Cyanobacterial blooms in New Zealand (NZ) waters contain microcystin (MC) hepatotoxins at concentrations which are a risk to animal and human health. Degradation of MCs by naturally occurring bacteria is an attractive bioremediation option for removing MCs from drinking and recreational water sources. The NV-3 isolate was identified by 16S rRNA sequence analysis and found to have 100% nucleotide sequence homology with the Sphingomonas MC-degrading bacterial strain MD-1 from Japan. The NV-3 isolate (concentration of 1.0 × 108 CFU/mL) at 30°C degraded a mixture of [Dha7]MC-LR and MC-LR (concentration 25 μg/mL) at a maximum rate of 8.33 μg/mL/day. The intermediate by-products of [Dha7]MC-LR degradation were detected and similar to MC-LR degradation by-products. The presence of three genes (mlrA, mlrB, and mlrC), that encode three enzymes involved in the degradation of MC-LR, were identified in the NV-3 isolate. This study confirmed that degradation of [Dha7]MC-LR by the Sphingomonas isolate NV-3 occurred by a similar mechanism previously described for MC-LR by Sphingomonas strain MJ-PV (ACM-3962). This has important implications for potential bioremediation of toxic blooms containing a variety of MCs in NZ waters. PMID:23936728

78 FR 12427 - Medicare Program; Medical Loss Ratio Requirements for the Medicare Advantage and the Medicare...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-22

...This proposed rule would implement medical loss ratio (MLR) requirements for the Medicare Advantage Program and the Medicare Prescription Drug Benefit Program under the Patient Protection and Affordable Care Act.

Sources and potential health risk of gas phase PAHs in Hexi Corridor, Northwest China.

PubMed

Mao, Xiaoxuan; Yu, Zhousuo; Ding, Zhongyuan; Huang, Tao; Ma, Jianmin; Zhang, Gan; Li, Jun; Gao, Hong

2016-02-01

Gas phase polycyclic aromatic hydrocarbons (PAHs) in Hexi Corridor, Northwest China were determined during heating and non-heating seasons, respectively, using passive air samplers. Polyurethane foam (PUF) disks were chosen as the sampling medium. Fifteen PAHs out of the 16 PAHs classified by the United States Environmental Protection Agency (U.S. EPA) were detected in this field sampling investigation. The atmospheric levels of sampled PAHs were higher at urban sites than that at rural sites among 14 sampling sites and increased during heating season. The highest concentration (11.34 ng m(-3)) was observed in Lanzhou during the heating season, the capital and largest industrial city of Gansu Province. PAH contamination in air was dominated by three aromatic ring congeners. Possible sources of PAHs were apportioned using PAH species ratios and the principle component analysis (PCA) combined with a multiple linear regression (MLR) method. Fossil fuel consumption was identified to be the predominant source of PAHs over Hexi Corridor, accounting for 43 % of the concentration of total (15) PAHs. Backward and forward trajectory and cluster analysis were also carried out to identify potential origins of PAHs monitored at several urban and rural sites. Lung cancer risk of local residents to gas phase PAHs via inhalation exposure throughout the province was found to be around a critical value of the lung cancer risk level at 10(-6) recommended by the U.S. EPA risk assessment guideline.

A prediction scheme of tropical cyclone frequency based on lasso and random forest

NASA Astrophysics Data System (ADS)

Tan, Jinkai; Liu, Hexiang; Li, Mengya; Wang, Jun

2017-07-01

This study aims to propose a novel prediction scheme of tropical cyclone frequency (TCF) over the Western North Pacific (WNP). We concerned the large-scale meteorological factors inclusive of the sea surface temperature, sea level pressure, the Niño-3.4 index, the wind shear, the vorticity, the subtropical high, and the sea ice cover, since the chronic change of these factors in the context of climate change would cause a gradual variation of the annual TCF. Specifically, we focus on the correlation between the year-to-year increment of these factors and TCF. The least absolute shrinkage and selection operator (Lasso) method was used for variable selection and dimension reduction from 11 initial predictors. Then, a prediction model based on random forest (RF) was established by using the training samples (1978-2011) for calibration and the testing samples (2012-2016) for validation. The RF model presents a major variation and trend of TCF in the period of calibration, and also fitted well with the observed TCF in the period of validation though there were some deviations. The leave-one-out cross validation of the model exhibited most of the predicted TCF are in consistence with the observed TCF with a high correlation coefficient. A comparison between results of the RF model and the multiple linear regression (MLR) model suggested the RF is more practical and capable of giving reliable results of TCF prediction over the WNP.

Water Quality Assessment of River Soan (Pakistan) and Source Apportionment of Pollution Sources Through Receptor Modeling.

PubMed

Nazeer, Summya; Ali, Zeshan; Malik, Riffat Naseem

2016-07-01

The present study was designed to determine the spatiotemporal patterns in water quality of River Soan using multivariate statistics. A total of 26 sites were surveyed along River Soan and its associated tributaries during pre- and post-monsoon seasons in 2008. Hierarchical agglomerative cluster analysis (HACA) classified sampling sites into three groups according to their degree of pollution, which ranged from least to high degradation of water quality. Discriminant function analysis (DFA) revealed that alkalinity, orthophosphates, nitrates, ammonia, salinity, and Cd were variables that significantly discriminate among three groups identified by HACA. Temporal trends as identified through DFA revealed that COD, DO, pH, Cu, Cd, and Cr could be attributed for major seasonal variations in water quality. PCA/FA identified six factors as potential sources of pollution of River Soan. Absolute principal component scores using multiple regression method (APCS-MLR) further explained the percent contribution from each source. Heavy metals were largely added through industrial activities (28 %) and sewage waste (28 %), nutrients through agriculture runoff (35 %) and sewage waste (28 %), organic pollution through sewage waste (27 %) and urban runoff (17 %) and macroelements through urban runoff (39 %), and mineralization and sewage waste (30 %). The present study showed that anthropogenic activities are the major source of variations in River Soan. In order to address the water quality issues, implementation of effective waste management measures are needed.

Sociodemographic patterns of household water-use costs in Puerto Rico.

PubMed

Yu, Xue; Ghasemizadeh, Reza; Padilla, Ingrid; Meeker, John D; Cordero, Jose F; Alshawabkeh, Akram

2015-08-15

Variability of household water-use costs across different sociodemographic groups in Puerto Rico is evaluated using census microdata from the Integrated Public Use Microdata Series (IPUMS). Multivariate analyses such as multiple linear regression (MLR) and factor analysis (FA) are used to classify, extract and interpret the household water-use costs. The FA results suggest two principal varifactors in explaining the variability of household water-use costs (64% in 2000 and 50% in 2010), which are grouped into a soft coefficient (social, economic and demographic characteristics of household residents, i.e., age, size, income, education) and a hard coefficient (dwelling conditions, i.e., number of rooms, units in the building, building age). The demographic profile of a high water-use household in Puerto Rico tends to be that of renters, people who live in larger or older buildings, people living in metro areas, or those with higher education level and higher income. The findings and discussions from this study will help decision makers to plan holistic and integrated water management to achieve water sustainability. Copyright © 2015 Elsevier B.V. All rights reserved.

Sociodemographic patterns of household water-use costs in Puerto Rico

PubMed Central

Yu, Xue; Ghasemizadeh, Reza; Padilla, Ingrid; Meeker, John D.; Cordero, Jose F.; Alshawabkeh, Akram

2015-01-01

Variability of household water-use costs across different sociodemographic groups in Puerto Rico is evaluated using census microdata from the Integrated Public Use Microdata Series (IPUMS). Multivariate analyses such as Multiple Linear Regression (MLR) and Factor Analysis (FA) are used to classify, extract and interpret the household water-use costs. The FA results suggest two principal varifactors in explaining the variability of household water-use costs (64% in 2000 and 50% in 2010), which are grouped into a soft coefficient (social, economic and demographic characteristics of household residents, i.e. age, size, income, education) and a hard coefficient (dwelling conditions, i.e. number of rooms, units in the building, building age). The demographic profile of a high water-use household in Puerto Rico tends to be that of renters, people who live in larger or older buildings, people living in metro areas, or those with higher education level and higher income. The findings and discussions from this study will help decision makers to plan holistic and integrated water management to achieve water sustainability. PMID:25897735

Temporal-spatial variation and source apportionment of soil heavy metals in the representative river-alluviation depositional system.

PubMed

Wang, Cheng; Yang, Zhongfang; Zhong, Cong; Ji, Junfeng

2016-09-01

The contributions of major driving forces on temporal changes of heavy metals in the soil in a representative river-alluviation area at the lower of Yangtze River were successfully quantified by combining geostatistics analysis with the modified principal component scores & multiple linear regressions approach (PCS-MLR). The results showed that the temporal (2003-2014) changes of Cu, Zn, Ni and Cr presented a similar spatial distribution pattern, whereas the Cd and Hg showed the distinctive patterns. The temporal changes of soil Cu, Zn, Ni and Cr may be predominated by the emission of the shipbuilding industry, whereas the significant changes of Cd and Hg were possibly predominated by the geochemical and geographical processes, such as the erosion of the Yangtze River water and leaching because of soil acidification. The emission of metal-bearing shipbuilding industry contributed an estimated 74%-83% of the changes in concentrations of Cu, Zn, Ni and Cr, whereas the geochemical and geographical processes may contribute 58% of change of Cd in the soil and 59% of decrease of Hg. Copyright © 2016 Elsevier Ltd. All rights reserved.

QSAR models for anti-malarial activity of 4-aminoquinolines.

PubMed

Masand, Vijay H; Toropov, Andrey A; Toropova, Alla P; Mahajan, Devidas T

2014-03-01

In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

Dynamic response of the scenic beauty value of different forests to various thinning intensities in central eastern China.

PubMed

Deng, Songqiu; Yin, Na; Guan, Qingwei; Katoh, Masato

2014-11-01

Forest management has a significant influence on the preferences of people for forest landscapes. This study sought to evaluate the dynamic effects of thinning intensities on the landscape value of forests over time. Five typical stands in Wuxiangsi National Forest Park in Nanjing, China, were subjected to a thinning experiment designed with four intensities: unthinned, light thinning, moderate thinning, and heavy thinning. People's preferences for landscape photographs taken in plots under various thinning intensities were assessed through scenic beauty estimation (SBE) at 2 and 5 years after thinning. The differences in scenic beauty value between different thinning intensities were then analyzed with a paired samples t test for the two periods. The results indicated that the landscape value of all of the thinned plots significantly exceeded that of the unthinned plots 2 years after thinning (p < 0.01) and that the heavily thinned plots were most appreciated, showing an average improvement of 9.71 % compared with the control plots. Additionally, the heavily thinned plots were judged to be more beautiful than the lightly thinned and moderately thinned plots, whereas there was no significant difference between moderate thinning and light thinning. At 5 years after thinning, however, the moderately thinned plots received the highest preference scores among the four intensities, displaying an average improvement of 11.32 % compared with the unthinned plots. A multiple linear regression (MLR) model indicated that landscape value improved with increases in the average diameter at breast height (DBH) and with the improvement of environmental cleanliness in the stand, whereas the value decreased with an increasing stem density, species diversity, litter coverage, and canopy density. In addition, we found that the performance of a neural network model based on a multilayer perception (MLP) algorithm for predicting scenic beauty was slightly better than that of the MLR model. The findings of our study suggest that moderate to heavy thinning should be recommended to manage forests for the improvement of forest landscape value.

Interannual, solar cycle, and trend terms in middle atmospheric temperature time series from HALOE

NASA Astrophysics Data System (ADS)

Remsberg, E. E.; Deaver, L. E.

2005-03-01

Temperature versus pressure or T(p) time series from the Halogen Occultation Experiment (HALOE) have been generated and analyzed for the period of 1991-2004 and for the mesosphere and upper stratosphere for latitude zones from 40N to 40S. Multiple linear regression (MLR) techniques were used for the analysis of the seasonal and the significant interannual and solar cycle (or decadal-scale) terms. An 11-yr solar cycle (SC) term of amplitude 0.5 to 1.7 K was found for the middle to upper mesosphere; its phase was determined by a Fourier fit to the de-seasonalized residual. This SC term is largest and has a lag of several years for northern hemisphere middle latitudes of the middle mesosphere, perhaps due to the interfering effects of wintertime wave dissipation. The SC response from the MLR models is weaker but essentially in-phase at low latitudes and in the southern hemisphere. An in-phase SC response term is also significant near the tropical stratopause with an amplitude of about 0.4 to 0.6 K, which is somewhat less than predicted from models. Both sub-biennial (688-dy) and QBO (800-dy) terms are resolved for the mid to upper stratosphere along with a decadal-scale term that is presumed to have a 13.5-yr period due to their predicted modulation. This decadal-scale term is out-of-phase with the SC during 1991-2004. However, the true nature and source of this term is still uncertain, especially at 5 hPa. Significant linear cooling trends ranging from -0.3 K to -1.1 K per decade were found in the tropical upper stratosphere and subtropical mesosphere. Trends have not emerged so far for the tropical mesosphere, so it is concluded that the cooling rates that have been resolved for the subtropics are likely upper limits. As HALOE-like measurements continue and their time series lengthen, it is anticipated that better accuracy can be achieved for these interannual, SC, and trend terms.

A happiness degree predictor using the conceptual data structure for deep learning architectures.

PubMed

Pérez-Benito, Francisco Javier; Villacampa-Fernández, Patricia; Conejero, J Alberto; García-Gómez, Juan M; Navarro-Pardo, Esperanza

2017-11-13

Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. Our D-SDNN approach provided a better outcome (MSE: 1.46·10 -2 ) than MLR (MSE: 2.30·10 -2 ), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degree through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship. Copyright © 2017. Published by Elsevier B.V.

Undetectable HCV-RNA at treatment-week 8 results in high-sustained virological response in HCV G1 treatment-experienced patients with advanced liver disease: the International Italian/Spanish Boceprevir/Peginterferon/Ribavirin Name Patients Program.

PubMed

Bruno, S; Bollani, S; Zignego, A L; Pascasio, J M; Magni, C; Ciancio, A; Caremani, M; Mangia, A; Marenco, S; Piovesan, S; Chemello, L; Babudieri, S; Moretti, A; Gea, F; Colletta, C; Perez-Alvarez, R; Forns, X; Larrubia, J R; Arenas, J; Crespo, J; Calvaruso, V; Ceccherini Silberstein, F; Maisonneuve, P; Craxì, A; Calleja, J L

2015-05-01

In many countries, first-generation protease inhibitors (PIs)/peginterferon/ribavirin (P/R) still represent the only treatment option for HCV-infected patients. Subjects with advanced disease and previous failure to P/R urgently need therapy, but they are under-represented in clinical trials. All treatment-experienced F3/4 Metavir patients who received boceprevir (BOC)+P/R in the Italian-Spanish Name Patient Program have been included in this study. Multivariate logistic regression analysis (MLR) was used to identify baseline and on-treatment predictors of SVR and adverse events (AEs). Four hundred and sixteen patients, mean age 57.7 (range 25-78 years), 70% males, 69.5% (289/416) F4, 14% (41/289) Child-Pugh class A6, 24% (70/289) with varices and 42% (173/416) prior null responders to P/R, were analysed. Overall, SVR rate (all 381 patients who received one dose of BOC) was 49%, (58% in F3, 45% in F4, 61% in relapsers, 51% in partial, 38% in null responders, and 72% in subjects with undetectable HCV-RNA at treatment-week (TW)8. Among patients with TW8 HCV-RNA ≥ 1000 IU/L, SVR was 8% (negative predictive value = 92%). Death occurred in 3 (0.8%) patients, while decompensation and infections were observed in 2.9% and 11%, respectively. At MLR, SVR predictors were TW4 HCV-RNA ≥ 1log10 -decline from baseline, undetectable TW8 HCV-RNA, prior relapse, albumin levels ≥3.5 g/dL and platelet counts ≥100 000/μL. Metavir F4, Child-Pugh A6, albumin, platelets, age and female gender were associated with serious and haematological AEs. Among treatment-experienced patients with advanced liver disease eligible for IFN-based therapy, TW8 HCV-RNA characterised the subset with either high or poor likelihood of achieving SVR. Using TW8 HCV-RNA as a futility rule, BOC/P/R appears to have a favourable benefit-risk profile. © 2014 John Wiley & Sons Ltd.

Female urinary incontinence at orgasm: a possible marker of a more severe form of detrusor overactivity. Can ultrasound measurement of bladder wall thickness explain it?

PubMed

Serati, Maurizio; Salvatore, Stefano; Cattoni, Elena; Siesto, Gabriele; Soligo, Marco; Braga, Andrea; Sorice, Paola; Cromi, Antonella; Ghezzi, Fabio; Cardozo, Linda; Bolis, Pierfrancesco

2011-06-01

Coital incontinence (CI) during orgasm is a form of urinary incontinence possibly because of detrusor overactivity (DO), as the underlying pathophysiological condition. Women with this symptom usually show a pharmacological lower cure rate than those with DO alone. The ultrasound measurement of the bladder wall thickness (BWT) allows an indirect evaluation of detrusor muscle thickness, giving a potential index of detrusor activity. We wanted to understand if CI at orgasm could be a marker of severity of DO by comparing BWT in women with both DO and CI at orgasm vs. women with DO alone. In addition we aimed to confirm if CI during orgasm is related to antimuscarinics treatment failure. This is a prospective cohort study performed in two tertiary urogynecological referral departments, recruiting consecutive patients seeking treatment for symptomatic DO. All patients were thoroughly assessed including physical examination, urodynamic evaluation, and BWT measurement according to the International Continence Society/International Urogynecological Association and ICI recommendations. Solifenacine 5 mg once daily was then prescribed and follow-up was scheduled to evaluate treatment. Multiple logistic regression (MLR) was performed to identify risk factors for treatment failure. Between September 2007 and March 2010, 31 (22.6%) and 106 (77.4%) women with DO with and without CI at orgasm were enrolled. Women complaining of CI at orgasm had significantly higher BWT than the control group (5.8 ± 0.6 mm vs. 5.2 ± 1.2 mm [P=0.007]). In patients with CI at orgasm, the nonresponder rate to antimuscarinics was significantly higher than controls (P=0.01). After MLR, CI at orgasm was the only independent predictor decreasing antimuscarinics efficacy (odds ratio [OR] 3.16 [95% CI 1.22-8.18], P=0.02). Women with DO and CI at orgasm showed a significantly higher BWT values and worse cure rates than women with DO alone. CI at orgasm could be a marker of a more severe form of DO. © 2011 International Society for Sexual Medicine.

Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk.

PubMed

Ghaedi, M; Ghaedi, A M; Ansari, A; Mohammadi, F; Vafaei, A

2014-11-11

The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g(-1) for Au-NP-AC and 3.84 mg g(-1) for Tamarisk-AC. Copyright © 2014 Elsevier B.V. All rights reserved.

Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk

NASA Astrophysics Data System (ADS)

Ghaedi, M.; Ghaedi, A. M.; Ansari, A.; Mohammadi, F.; Vafaei, A.

2014-11-01

The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g-1 for Au-NP-AC and 3.84 mg g-1 for Tamarisk-AC.

Oxidative potential of ambient PM2.5 in the coastal cities of the Bohai Sea, northern China: Seasonal variation and source apportionment.

PubMed

Liu, WeiJian; Xu, YunSong; Liu, WenXin; Liu, QingYang; Yu, ShuangYu; Liu, Yang; Wang, Xin; Tao, Shu

2018-05-01

Emissions of air pollutants from primary and secondary sources in China are considerably higher than those in developed countries, and exposure to air pollution is main risk of public health. Identifying specific particulate matter (PM) compositions and sources are essential for policy makers to propose effective control measures for pollutant emissions. Ambient PM 2.5 samples covered a whole year were collected from three coastal cities of the Bohai Sea. Oxidative potential (OP) was selected as the indicator to characterize associated PM compositions and sources most responsible for adverse impacts on human health. Positive matrix factorization (PMF) and multiple linear regression (MLR) were employed to estimate correlations of PM 2.5 sources with OP. The volume- and mass-based dithiothreitol (DTT v and DTT m ) activities of PM 2.5 were significantly higher in local winter or autumn (p < 0.01). Spatial and seasonal variations in DTT v and DTT m were much larger than mass concentrations of PM 2.5 , indicated specific chemical components are responsible for PM 2.5 derived OP. Strong correlations (r > 0.700, p < 0.01) were found between DTT activity and water-soluble organic carbon (WSOC) and some transition metals. Using PMF, source fractions of PM 2.5 were resolved as secondary source, traffic source, biomass burning, sea spray and urban dust, industry, coal combustion, and mineral dust. Further quantified by MLR, coal combustion, biomass burning, secondary sources, industry, and traffic source were dominant contributors to the water-soluble DTT v activity. Our results also suggested large differences in seasonal contributions of different sources to DTT v variability. A higher contribution of DTT v was derived from coal combustion during the local heating period. Secondary sources exhibited a greater fraction of DTT v in summer, when there was stronger solar radiation. Traffic sources exhibited a prevailing contribution in summer, and industry contributed larger proportions in spring and winter. Future abatement priority of air pollution should reduce the sources contributing to OP of PM 2.5 . Copyright © 2018 Elsevier Ltd. All rights reserved.

QSAR by LFER model of cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives using principal component factor analysis and genetic function approximation.

PubMed

Roy, Kunal; Leonard, J Thomas

2005-04-15

Cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives were subjected to quantitative structure-activity relationship (QSAR) study using linear free energy related (LFER) model of Hansch using electronic (Hammett sigma), hydrophobicity (pi) and steric (molar refractivity and STERIMOL L, B1, B2, B3 and B4) parameters of phenyl ring substituents of the compounds, along with appropriate indicator variables. Principal component factor analysis (FA) was used as the data-preprocessing step to identify the important predictor variables contributing to the response variable and to avoid collinearities among them. The generated multiple linear regression (MLR) equations were statistically validated using leave-one-out technique. Genetic function approximation (GFA) was also used on the same data set to develop QSAR equations, which produced the same best equation as obtained with FA-MLR. The final equation is of acceptable statistical quality (explained variance 80.2%) and predictive potential (leave-one-out predicted variance 74%). The analysis explores the structural and physicochemical contributions of the compounds for cytotoxicity. A thiol substituent at 2 position of the imidazole nucleus decreases cytotoxicity when compared to the corresponding unsubstituted congener. Presence of hydrogen bond donor group at meta position of the phenyl ring present at 5 position of the imidazole nucleus also reduces cytotoxicity. Additionally, absence of any substituent at 2 and 3 positions of the phenyl ring of 1-phenylamino fragment reduces the cytotoxicity. The negative coefficient of sigmap indicates that presence of electron-withdrawing substituents at the para position of the phenyl ring of the 1-phenylamino fragment is not favourable for the cytotoxicity. Again, lipophilicity of meta substituents of the 5-phenyl ring increases cytotoxicity. The coefficients of molar refractivity (MRm) and STERIMOL parameters for meta substituents (Lm, B1m and B4m) of the phenyl ring of 1-phenylamino fragment indicate that the length, width and overall size of meta substituents are conducive factors for the cytotoxicity.

Risk factors of neonatal mortality and child mortality in Bangladesh.

PubMed

Maniruzzaman, Md; Suri, Harman S; Kumar, Nishith; Abedin, Md Menhazul; Rahman, Md Jahanur; El-Baz, Ayman; Bhoot, Makrand; Teji, Jagjit S; Suri, Jasjit S

2018-06-01

Child and neonatal mortality is a serious problem in Bangladesh. The main objective of this study was to determine the most significant socio-economic factors (covariates) between the years 2011 and 2014 that influences on neonatal and child mortality and to further suggest the plausible policy proposals. We modeled the neonatal and child mortality as categorical dependent variable (alive vs death of the child) while 16 covariates are used as independent variables using χ 2 statistic and multiple logistic regression (MLR) based on maximum likelihood estimate. Using the MLR, for neonatal mortality, diarrhea showed the highest positive coefficient (β = 1.130; P  < 0.010) leading to most significant covariate for both 2011 and 2014. The corresponding odds ratios were: 0.323 for both the years. The second most significant covariate in 2011 was birth order between 2-6 years (β = 0.744; P  < 0.001), while father's education was negative correlation (β = -0.910; P  < 0.050). In general, 10 covariates in 2011 and 5 covariates in 2014 were significant, so there was an improvement in socio-economic conditions for neonatal mortality. For child mortality, birth order between 2-6 years and 7 and above years showed the highest positive coefficients (β = 1.042; P  < 0.010) and (β = 1.285; P  < 0.050) for 2011. The corresponding odds ratios were: 2.835 and 3.614, respectively. Father's education showed the highest coefficient (β = 0.770; P  < 0.050) indicating the significant covariate for 2014 and the corresponding odds ratio was 2.160. In general, 6 covariates in 2011 and 4 covariates in 2014 were also significant, so there was also an improvement in socio-economic conditions for child mortality. This study allows policy makers to make appropriate decisions to reduce neonatal and child mortality in Bangladesh. In 2014, mother's age and father's education were also still significant covariates for child mortality. This study allows policy makers to make appropriate decisions to reduce neonatal and child mortality in Bangladesh.

Food swamps and food deserts in Baltimore City, MD, USA: associations with dietary behaviours among urban adolescent girls.

PubMed

Hager, Erin R; Cockerham, Alexandra; O'Reilly, Nicole; Harrington, Donna; Harding, James; Hurley, Kristen M; Black, Maureen M

2017-10-01

To determine whether living in a food swamp (≥4 corner stores within 0·40 km (0·25 miles) of home) or a food desert (generally, no supermarket or access to healthy foods) is associated with consumption of snacks/desserts or fruits/vegetables, and if neighbourhood-level socio-economic status (SES) confounds relationships. Cross-sectional. Assessments included diet (Youth/Adolescent FFQ, skewed dietary variables normalized) and measured height/weight (BMI-for-age percentiles/Z-scores calculated). A geographic information system geocoded home addresses and mapped food deserts/food swamps. Associations examined using multiple linear regression (MLR) models adjusting for age and BMI-for-age Z-score. Baltimore City, MD, USA. Early adolescent girls (6th/7th grade, n 634; mean age 12·1 years; 90·7 % African American; 52·4 % overweight/obese), recruited from twenty-two urban, low-income schools. Girls' consumption of fruit, vegetables and snacks/desserts: 1·2, 1·7 and 3·4 servings/d, respectively. Girls' food environment: 10·4 % food desert only, 19·1 % food swamp only, 16·1 % both food desert/swamp and 54·4 % neither food desert/swamp. Average median neighbourhood-level household income: $US 35 298. In MLR models, girls living in both food deserts/swamps consumed additional servings of snacks/desserts v. girls living in neither (β=0·13, P=0·029; 3·8 v. 3·2 servings/d). Specifically, girls living in food swamps consumed more snacks/desserts than girls who did not (β=0·16, P=0·003; 3·7 v. 3·1 servings/d), with no confounding effect of neighbourhood-level SES. No associations were identified with food deserts or consumption of fruits/vegetables. Early adolescent girls living in food swamps consumed more snacks/desserts than girls not living in food swamps. Dietary interventions should consider the built environment/food access when addressing adolescent dietary behaviours.

Factors determining anti-poliovirus type 3 antibodies among orally immunised Indian infants.

PubMed

Kaliappan, Saravanakumar Puthupalayam; Venugopal, Srinivasan; Giri, Sidhartha; Praharaj, Ira; Karthikeyan, Arun S; Babji, Sudhir; John, Jacob; Muliyil, Jayaprakash; Grassly, Nicholas; Kang, Gagandeep

2016-09-22

Among the three poliovirus serotypes, the lowest responses after vaccination with trivalent oral polio vaccine (tOPV) are to serotype 3. Although improvements in routine immunisation and supplementary immunisation activities have greatly increased vaccine coverage, there are limited data on antibody prevalence in Indian infants. Children aged 5-11months with a history of not having received inactivated polio vaccine were screened for serum antibodies to poliovirus serotype 3 (PV3) by a micro-neutralisation assay according to a modified World Health Organization (WHO) protocol. Limited demographic information was collected to assess risk-factors for a lack of protective antibodies. Student's t-test, logistic regression and multilevel logistic regression (MLR) model were used to estimate model parameters. Of 8454 children screened at a mean age of 8.3 (standard deviation [SD]-1.8) months, 88.1% (95% confidence interval (CI): 87.4-88.8) had protective antibodies to PV3. The number of tOPV doses received was the main determinant of seroprevalence; the maximum likelihood estimate yields a 37.7% (95% CI: 36.2-38.3) increase in seroprevalence per dose of tOPV. In multivariable logistic regression analysis increasing age, male sex, and urban residence were also independently associated with seropositivity (Odds Ratios (OR): 1.17 (95% CI: 1.12-1.23) per month of age, 1.27 (1.11-1.46) and 1.24 (1.05-1.45) respectively). Seroprevalence of antibodies to PV3 is associated with age, gender and place of residence, in addition to the number of tOPV doses received. Ensuring high coverage and monitoring of response are essential as long as oral vaccines are used in polio eradication. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Susceptibility to aflatoxin contamination among maize landraces from Mexico.

PubMed

Ortega-Beltran, Alejandro; Guerrero-Herrera, Manuel D J; Ortega-Corona, Alejandro; Vidal-Martinez, Victor A; Cotty, Peter J

2014-09-01

Maize, the critical staple food for billions of people, was domesticated in Mexico about 9,000 YBP. Today, a great array of maize landraces (MLRs) across rural Mexico is harbored in a living library that has been passed among generations since before the establishment of the modern state. MLRs have been selected over hundreds of generations by ethnic groups for adaptation to diverse environmental settings. The genetic diversity of MLRs in Mexico is an outstanding resource for development of maize cultivars with beneficial traits. Maize is frequently contaminated with aflatoxins by Aspergillus flavus, and resistance to accumulation of these potent carcinogens has been sought for over three decades. However, MLRs from Mexico have not been evaluated as potential sources of resistance. Variation in susceptibility to both A. flavus reproduction and aflatoxin contamination was evaluated on viable maize kernels in laboratory experiments that included 74 MLR accessions collected from 2006 to 2008 in the central west and northwest regions of Mexico. Resistant and susceptible MLR accessions were detected in both regions. The most resistant accessions accumulated over 99 % less aflatoxin B1 than did the commercial hybrid control Pioneer P33B50. Accessions supporting lower aflatoxin accumulation also supported reduced A. flavus sporulation. Sporulation on the MLRs was positively correlated with aflatoxin accumulation (R = 0.5336, P < 0.0001), suggesting that resistance to fungal reproduction is associated with MLR aflatoxin resistance. Results of the current study indicate that MLRs from Mexico are potentially important sources of aflatoxin resistance that may contribute to the breeding of commercially acceptable and safe maize hybrids and/or open pollinated cultivars for human and animal consumption.

The effects of hyperthermia on the immunomodulatory properties of human umbilical cord vein mesenchymal stem cells (MSCs).

PubMed

Hesami, Shilan; Mohammadi, Mehdi; Rezaee, Mohamad Ali; Jalili, Ali; Rahmani, Mohammad Reza

2017-11-01

Hyperthermia can modulate inflammation and the immune response. Based on the recruitment of mesenchymal stem cells (MSCs) to inflamed tissues and the immunomodulatory properties of these cells, the aim of this study was to examine the effects of hyperthermia on the immunomodulatory properties of MSCs in a mixed lymphocyte reaction (MLR). Passages 4-6 of human umbilical cord vein mesenchymal stem cells were co-cultured in a two-way MLR. Cells in the hyperthermia groups were incubated at 41 °C for 45 min. A colorimetric assay was employed to examine the effects of MSCs on cell proliferation. The levels of IL-4 and TNF-α proteins in the cell culture supernatant were measured, and non-adherent cells were used for RNA extraction, which was then used for cDNA synthesis. RT-PCR was utilised to assess levels of IL-10, IL-17A, IL-4, TNF-α, TGF-β1, FOX P 3 , IFN-γ, CXCL12 and β-actin mRNA expression. UCV-MSCs co-cultured in an MLR reduced lymphocyte proliferation at 37 °C, whereas hyperthermia attenuated this effect. Hyperthermia increased expression of IL-10, TGF-β1 and FOXP3 mRNAs in co-culture; however, no effects on IL-17A and IFN-γ were observed, and it reduced CXCL12 expression. In co-culture, IL-4 mRNA and protein increased at 37 °C, an effect that was reduced by hyperthermia. No considerable change in TNF-α mRNA expression was found in hyperthermia-treated cells. Hyperthermia increases cell proliferation of the peripheral blood mononuclear cells and modifies the cytokine profile in the presence of UCV-MSCs.

Monodisperse Latex Reactor (MLR): A materials processing space shuttle mid-deck payload

NASA Technical Reports Server (NTRS)

Kornfeld, D. M.

1985-01-01

The monodisperse latex reactor experiment has flown five times on the space shuttle, with three more flights currently planned. The objectives of this project is to manufacture, in the microgravity environment of space, large particle-size monodisperse polystyrene latexes in particle sizes larger and more uniform than can be manufactured on Earth. Historically it has been extremely difficult, if not impossible to manufacture in quantity very high quality monodisperse latexes on Earth in particle sizes much above several micrometers in diameter due to buoyancy and sedimentation problems during the polymerization reaction. However the MLR project has succeeded in manufacturing in microgravity monodisperse latex particles as large as 30 micrometers in diameter with a standard deviation of 1.4 percent. It is expected that 100 micrometer particles will have been produced by the completion of the the three remaining flights. These tiny, highly uniform latex microspheres have become the first material to be commercially marketed that was manufactured in space.

Inter-comparison of time series models of lake levels predicted by several modeling strategies

NASA Astrophysics Data System (ADS)

Khatibi, R.; Ghorbani, M. A.; Naghipour, L.; Jothiprakash, V.; Fathima, T. A.; Fazelifard, M. H.

2014-04-01

Five modeling strategies are employed to analyze water level time series of six lakes with different physical characteristics such as shape, size, altitude and range of variations. The models comprise chaos theory, Auto-Regressive Integrated Moving Average (ARIMA) - treated for seasonality and hence SARIMA, Artificial Neural Networks (ANN), Gene Expression Programming (GEP) and Multiple Linear Regression (MLR). Each is formulated on a different premise with different underlying assumptions. Chaos theory is elaborated in a greater detail as it is customary to identify the existence of chaotic signals by a number of techniques (e.g. average mutual information and false nearest neighbors) and future values are predicted using the Nonlinear Local Prediction (NLP) technique. This paper takes a critical view of past inter-comparison studies seeking a superior performance, against which it is reported that (i) the performances of all five modeling strategies vary from good to poor, hampering the recommendation of a clear-cut predictive model; (ii) the performances of the datasets of two cases are consistently better with all five modeling strategies; (iii) in other cases, their performances are poor but the results can still be fit-for-purpose; (iv) the simultaneous good performances of NLP and SARIMA pull their underlying assumptions to different ends, which cannot be reconciled. A number of arguments are presented including the culture of pluralism, according to which the various modeling strategies facilitate an insight into the data from different vantages.

48 CFR 1602.170-14 - FEHB-specific medical loss ratio threshold calculation.

Code of Federal Regulations, 2011 CFR

2011-10-01

... issuer's expenditures for activities that improve health care quality, to total premium revenue determined by OPM, as defined by the Department of Health and Human Services. (b) The FEHB-specific MLR will... OFFICE OF PERSONNEL MANAGEMENT FEDERAL EMPLOYEES HEALTH BENEFITS ACQUISITION REGULATION GENERAL...

77 FR 71801 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-04

... issuers' reporting obligations (disbursement of rebates by non-federal governmental plans) was also... conducts business. Each issuer is also required to provide a rebate notice to each policyholder that is owed a rebate and each subscriber of policyholders that are owed a rebate for any given MLR reporting...

45 CFR 800.203 - Medical loss ratio.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 45 Public Welfare 3 2013-10-01 2013-10-01 false Medical loss ratio. 800.203 Section 800.203 Public... PROGRAM Premiums, Rating Factors, Medical Loss Ratios, and Risk Adjustment § 800.203 Medical loss ratio. (a) Required medical loss ratio. An MSPP issuer must attain: (1) The medical loss ratio (MLR...

45 CFR 800.203 - Medical loss ratio.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 45 Public Welfare 3 2014-10-01 2014-10-01 false Medical loss ratio. 800.203 Section 800.203 Public... PROGRAM Premiums, Rating Factors, Medical Loss Ratios, and Risk Adjustment § 800.203 Medical loss ratio. (a) Required medical loss ratio. An MSPP issuer must attain: (1) The medical loss ratio (MLR...

Phenotypic and genotypic characterization of biofilm forming capability in non-O157 Shiga toxin-producing Escherichia coli strains

USDA-ARS?s Scientific Manuscript database

The biofilm life style helps bacteria resist oxidative stress, desiccation, antibiotic treatment, and starvation. Biofilm formation involves a complex regulatory gene network controlled by various environmental signals. It was previously shown that prophage insertions in mlrA and heterogeneous mutat...

75 FR 2929 - Agency Information Collection Activities: Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-19

... soliciting comments concerning an information collection titled ``Bank Secrecy Act/Money Laundering Risk Assessment'' (a.k.a. Money Laundering Risk (MLR) System). The OCC also gives notice that it has sent the... approval for the following information collection: Title: Bank Secrecy Act/Anti-Money Laundering Risk...

The Middle Latency Response (MLR) and Steady State Evoked Potential (SSEP) in Neonates.

DTIC Science & Technology

1985-05-01

diagnostic audiologic information will enhance habilitation efforts in prescribing hearing aids and designing appropriate language intervention strategies...auditory evoked brain stem response. A study of patients with sensory hearing loss. SCANDINAVIAN AUDIOLOGY 8: 67-70, 1979. Page 165 "- FILMED 10-85 DTIC * 4 N . . -. N

45 CFR 158.232 - Calculating the credibility adjustment.

Code of Federal Regulations, 2011 CFR

2011-10-01

... experience is zero. (2) The base credibility factor for partially credible experience is determined based on... adjustment for an MLR based on partially credible experience is zero if both of the following conditions are... deductible factor is based on the average per person deductible of policies whose experience is included in...

Estimation of the fraction of absorbed photosynthetically active radiation (fPAR) in maize canopies using LiDAR data and hyperspectral imagery.

PubMed

Qin, Haiming; Wang, Cheng; Zhao, Kaiguang; Xi, Xiaohuan

2018-01-01

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) for maize canopies are important for maize growth monitoring and yield estimation. The goal of this study is to explore the potential of using airborne LiDAR and hyperspectral data to better estimate maize fPAR. This study focuses on estimating maize fPAR from (1) height and coverage metrics derived from airborne LiDAR point cloud data; (2) vegetation indices derived from hyperspectral imagery; and (3) a combination of these metrics. Pearson correlation analyses were conducted to evaluate the relationships among LiDAR metrics, hyperspectral metrics, and field-measured fPAR values. Then, multiple linear regression (MLR) models were developed using these metrics. Results showed that (1) LiDAR height and coverage metrics provided good explanatory power (i.e., R2 = 0.81); (2) hyperspectral vegetation indices provided moderate interpretability (i.e., R2 = 0.50); and (3) the combination of LiDAR metrics and hyperspectral metrics improved the LiDAR model (i.e., R2 = 0.88). These results indicate that LiDAR model seems to offer a reliable method for estimating maize fPAR at a high spatial resolution and it can be used for farmland management. Combining LiDAR and hyperspectral metrics led to better performance of maize fPAR estimation than LiDAR or hyperspectral metrics alone, which means that maize fPAR retrieval can benefit from the complementary nature of LiDAR-detected canopy structure characteristics and hyperspectral-captured vegetation spectral information.

Ecological geochemical assessment and source identification of trace elements in atmospheric deposition of an emerging industrial area: Beibu Gulf economic zone.

PubMed

Zhong, Cong; Yang, Zhongfang; Jiang, Wei; Hu, Baoqing; Hou, Qingye; Yu, Tao; Li, Jie

2016-12-15

Industrialization and urbanization have led to a deterioration in air quality and provoked some serious environmental concerns. Fifty-four samples of atmospheric deposition were collected from an emerging industrial area and analyzed to determine the concentrations of 11 trace elements (As, Cd, Cu, Fe, Hg, Mn, Mo, Pb, Se, S and Zn). Multivariate geostatistical analyses were conducted to determine the spatial distribution, possible sources and enrichment degrees of trace elements in atmospheric deposition. Results indicate that As, Fe and Mo mainly originated from soil, their natural parent materials, while the remaining trace elements were strongly influenced by anthropogenic or natural activities, such as coal combustion in coal-fired power plants (Pb, Se and S), manganese ore (Mn, Cd and Hg) and metal smelting (Cu and Zn). The results of ecological geochemical assessment indicate that Cd, Pb and Zn are the elements of priority concern, followed by Mn and Cu, and other heavy metals, which represent little threat to local environment. It was determine that the resuspension of soil particles impacted the behavior of heavy metals by 55.3%; the impact of the coal-fired power plants was 18.9%; and the contribution of the local manganese industry was 9.6%. The comparison of consequences from various statistical methods (principal component analysis (PCA), cluster analysis (CA), enrichment factor (EF) and absolute principle component score (APCS)-multiple linear regression (MLR)) confirmed the credibility of this research. Copyright © 2016 Elsevier B.V. All rights reserved.

Surface Enhanced Raman Spectroscopy (SERS) for the Multiplex Detection of Braf, Kras, and Pik3ca Mutations in Plasma of Colorectal Cancer Patients

PubMed Central

Li, Xiaozhou; Yang, Tianyue; Li, Caesar Siqi; Song, Youtao; Lou, Hong; Guan, Dagang; Jin, Lili

2018-01-01

In this paper, we discuss the use of a procedure based on polymerase chain reaction (PCR) and surface enhanced Raman spectroscopy (SERS) (PCR-SERS) to detect DNA mutations. Methods: This method was implemented by first amplifying DNA-containing target mutations, then by annealing probes, and finally by applying SERS detection. The obtained SERS spectra were from a mixture of fluorescence tags labeled to complementary sequences on the mutant DNA. Then, the SERS spectra of multiple tags were decomposed to component tag spectra by multiple linear regression (MLR). Results: The detection limit was 10-11 M with a coefficient of determination (R2) of 0.88. To demonstrate the applicability of this process on real samples, the PCR-SERS method was applied on blood plasma taken from 49 colorectal cancer patients to detect six mutations located at the BRAF, KRAS, and PIK3CA genes. The mutation rates obtained by the PCR-SERS method were in concordance with previous research. Fisher's exact test showed that only two detected mutations at BRAF (V600E) and PIK3CA (E542K) were significantly positively correlated with right-sided colon cancer. No other clinical feature such as gender, age, cancer stage, or differentiation was correlated with mutation (V600E at BRAF, G12C, G12D, G12V, G13D at KRAS, and E542K at PIK3CA). Visually, a dendrogram drawn through hierarchical clustering analysis (HCA) supported the results of Fisher's exact test. The clusters drawn by all six mutations did not conform to the distributions of cancer stages, differentiation or cancer positions. However, the cluster drawn by the two mutations of V600E and E542K showed that all samples with those mutations belonged to the right-sided colon cancer group. Conclusion: The suggested PCR-SERS method is multiplexed, flexible in probe design, easy to incorporate into existing PCR conditions, and was sensitive enough to detect mutations in blood plasma. PMID:29556349

45 CFR 158.344 - Secretary's discretion to hold a hearing.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 1 2011-10-01 2011-10-01 false Secretary's discretion to hold a hearing. 158.344 Section 158.344 Public Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES REQUIREMENTS RELATING TO HEALTH... adjustment to the MLR standard. All testimony and materials received in connection with any public hearing...

Medical loss ratio rebate requirements for non-federal governmental plans. Interim final rule with request for comments.

PubMed

2011-12-07

This interim final rule with comment period revises the regulations implementing medical loss ratio (MLR) requirements for health insurance issuers under the Public Health Service Act in order to establish rules governing the distribution of rebates by issuers in group markets for non-Federal governmental plans.

77 FR 20023 - Agency Information Collection Activities: Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-03

... issue not included in issuers' reporting obligations (disbursement of rebates by non-federal... which the issuer conducts business. Each issuer is also required to provide a rebate notice to each enrollee that is due a rebate payment for any given MLR reporting year. Additionally, each issuer is...

76 FR 16789 - Emergency Clearance: Public Information Collection Requirements Submitted to the Office of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-25

... collection; Title of Information Collection: Medical Loss Ratio Quarterly Reporting; Use: Under Section 2718... required to submit annual MLR reporting data for each large group market, small group market, and individual market within each State in which the issuer conducts business. For policies that have a total...

76 FR 18221 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-01

... Ratio Standard for a State's Individual Market; Use: Under section 2718 of the Public Health Service Act... data allows for the calculation of an issuer's medical loss ratio (MLR) by market (individual, small... whether market destabilization has a high likelihood of occurring. Form Number: CMS-10361 (OMB Control No...

76 FR 31337 - Agency Information Collection Activities: Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-31

... Collection: Request for Adjustment to the Medical Loss Ratio Standard for a State's Individual Market; Use... medical loss ratio (MLR) by market (individual, small group, and large group) within each State in which... Number: CMS-10361 (OMB Control No. 0938-1114); Frequency: Once; Affected Public: State, local or tribal...

45 CFR 158.231 - Life-years used to determine credible experience.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 45 Public Welfare 1 2013-10-01 2013-10-01 false Life-years used to determine credible experience... and Providing the Rebate § 158.231 Life-years used to determine credible experience. (a) The life-years used to determine the credibility of an issuer's experience are the life-years for the MLR...

45 CFR 158.231 - Life-years used to determine credible experience.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 45 Public Welfare 1 2014-10-01 2014-10-01 false Life-years used to determine credible experience... and Providing the Rebate § 158.231 Life-years used to determine credible experience. (a) The life-years used to determine the credibility of an issuer's experience are the life-years for the MLR...

Combined treatment of toxic cyanobacteria Microcystis aeruginosa with hydrogen peroxide and microcystin biodegradation agents results in quick toxin elimination.

PubMed

Dziga, Dariusz; Maksylewicz, Anna; Maroszek, Magdalena; Marek, Sylwia

2018-01-01

Under some conditions the growth of toxic cyanobacteria must be controlled by treatment with algicidal compounds. Hydrogen peroxide has been proposed as an efficient and relatively safe chemical which can remove cyanobacteria from the environment selectively, without affecting other microorganisms. However, the uncontrolled release of secondary metabolites, including toxins may occur after such a treatment. Our proposal presented in this paper concerns fast biodegradation of microcystin released after cell lysis induced by hydrogen peroxide. The effectiveness of both, Sphingomonas sp. and heterologously expressed MlrA enzyme, in the removal of the toxin from Microcystis aeruginosa culture was investigated. The results indicate that neither Sphingomonas cells nor MlrA are affected by hydrogen peroxide at the concentrations which stop the growth of cyanobacteria. A several-fold reduction in microcystin levels was documented in the presence of these agents with biodegradation ability. Our results provide evidence that such a combined treatment of water reservoirs dominated by microcystin-producing cyanobacteria may be a promising alternative which allows fast elimination of both, the bloom forming species and toxins, from the environment.

Potentiation of lymphocyte proliferative responses by nickel sulfide

NASA Technical Reports Server (NTRS)

Jaramillo, A.; Sonnenfeld, G.

1992-01-01

Crystalline nickel sulfide (NiS) induced a spleen cell proliferation that resembles a mixed lymphocyte reaction (MLR). It depended on cell-cell interaction, induced high levels of interleukin-1 (IL-1) and interleukin-2 (IL-2) and the responding cell subpopulation was composed of CD4+ T lymphocytes. Furthermore, the proliferation was inhibited in a dose-dependent manner by magnesium. Crystalline NiS also increased significantly the spleen cell proliferative response to concanavalin A (Con A) and lipopolysaccharide (LPS) with magnesium potentiating the combined effects of crystalline NiS and mitogens. Interestingly, crystalline NiS did not show any effect on the induction of IL-2 by Con A. The results described herein suggest that crystalline NiS can potentiate both antigenic (MLR) and mitogenic (Con A and LPS) proliferative responses in vitro. Crystalline NiS appears to potentiate these responses by acting in the form of ionic nickel on several intracellular targets for which magnesium ions have different noncompetitive interactions. The effects of magnesium on the potentiating action of crystalline NiS are different depending upon the type of primary stimulatory signal for proliferation (mitogenic or antigenic).