Effects of silymarin on biochemical and oxidative stress markers in end‐stage renal disease patients undergoing peritoneal dialysis

Introduction End‐stage renal disease (ESRD) patients especially those undergoing dialysis are vulnerable to several complications, in particular those related to oxidative stress. Silymarin is an herbal medicine commonly used as an antioxidant in different pathologies. Methods To evaluate the effect of silymarin on biochemical and oxidative stress markers, 50 ESRD patients undergoing peritoneal dialysis were randomly divided into two groups of silymarin (n = 28) and control (n = 22) and received silymarin (140 mg every 8 hours) or placebo for 2 months, respectively. Ferric reducing antioxidant power and total 8‐iso‐prostaglandin F_2α were measured in plasma, while catalase enzyme activity was measured in erythrocytes of both groups before and after treatment. Findings Ferric reducing antioxidant power values after treatment were significantly decreased in silymarin group compared to before treatment values (17.2 ± 2.9 and 15.9 ± 3.1 µM equivalent of quercetin/dL, respectively, P < 0.05). Conversely, catalase levels were increased 17.3% after silymarin consumption, while it was decreased 9.1% in control group. Further, hemoglobin (from 10.94 ± 2.17 to 11.54 ± 2.03 g/dL, P < 0.05) and albumin levels (from 3.48 ± 0.67 to 3.61 ± 0.53 g/dL, P < 0.05) were significantly increased after silymarin administration. Discussion It is concluded that silymarin could be regarded as a supplementary therapy for ESRD patients undergoing peritoneal dialysis in order to reduce complications.     

Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining

Feature selection is one of the most important techniques for data preprocessing in classification problems. In this paper, fuzzy grids–based association rules mining, as an effective data mining technique, is used for feature selection in misuse detection application in computer networks. The main idea of this algorithm is to find the relationships between items in large datasets so that it detects correlations between inputs of the system and then eliminates the redundant inputs. To classify the attacks, a fuzzy ARTMAP neural network is employed whose training parameters are optimized by gravitational search algorithm. The performance of the proposed system is compared with some other machine learning methods in the same application. Experimental results show that the proposed system, when choosing optimum “feature subset size-adjustment” parameter, performs better in terms of detection rate, false alarm rate, and cost per example in classification problems. In addition, employing the reduced-size feature set results in more than 8.4 percent reduction in computational complexity.     

Feature extraction using median–mean and feature line embedding

In this article, we propose a feature extraction method based on median–mean and feature line embedding (MMFLE) for the classification of hyperspectral images. In MMFLE, we maximize the class separability using discriminant analysis. Moreover, we remove the negative effect of outliers on the class mean using the median–mean line (MML) measurement and virtually enlarge the training set using the feature line (FL) distance metric. The experimental results on Indian Pines and University of Pavia data sets show the better performance of MMFLE compared to nearest feature line embedding (NFLE), median–mean line discriminant analysis (MMLDA), and some other feature extraction approaches in terms of classification accuracy using a small training set.     

Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

Nutrition education alone improves dietary practices but not hematologic indices of adolescent girls in Iran

BACKGROUND: Iron-deficiency anemia is the most prevalent nutritional deficiency worldwide. Iron-deficiency anemia has particular negative consequences on women in their childbearing years, and its prevention is a high priority in most health systems. OBJECTIVE: This interventional study assessed the effect of nutrition education on hematologic indices, iron status, nutritional knowledge, and nutritional practices of high-school girls in Iran. METHODS: Sixty healthy 16- to 18-year-old girls were randomly selected from two high schools in the city of Ahvaz and divided into two equally matched groups, one that received nutrition education, and one that did not. The education group received instruction in face-to-face sessions, group discussions, and pamphlets for 2 months. The control group did not receive any information during the study. Hematologic tests, corpuscular indices, and serum ferritin levels were measured at baseline and after 2 months. Food-frequency questionnaires were administered and histories taken, clinical signs of nutritional deficiencies observed, anthropometric measurements taken, nutritional knowledge tested, practices determined, and lifestyle questionnaires administered to all subjects. RESULTS: There were no statistically significant differences in any baseline characteristics between the two groups. Scores for nutritional knowledge and practices of the education group were significantly higher after two months compared with the baseline (31.4 +/- 6 vs. 24.3 +/- 5.9 points, p < .001, and 31.2 +/- 5 vs. 28.4 +/- 5.7 points, p < .05, respectively). The scores in the control group showed no significant changes from baseline to 2 months. Mean corpuscular volume values were elevated in the education group (p < .001) but not in the control group. However, in the control group, serum ferritin concentrations showed about a 17% drop at the end of the study (p < .004). There were no changes in other hematologic, lifestyle, clinical, or anthropometric data compared with baseline after completion of the study in both groups. CONCLUSION: These findings indicate that nutritional education can improve knowledge of healthy nutrition and lifestyle choices. Focused nutritional education using available resources and correcting current dietary habits in a vulnerable group of young women may result in dietary changes that can ultimately improve iron intake.     

Generalized Storage Equations for Flood Routing with Nonlinear Muskingum Models

Water Resources Management - The nonlinear Muskingum model is a leading method for hydrologic routing. The efficiency of the nonlinear Muskingum model for routing of hydrograph outflow has been...     

Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration

Estimation of evapotranspiration (ET) is necessary in water resources management, farm irrigation scheduling, and environmental assessment. Hence, in practical hydrology, it is often necessary to reliably and consistently estimate evapotranspiration. In this study, two artificial intelligence (AI) techniques, including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to compute garlic crop water requirements. Various architectures and input combinations of the models were compared for modeling garlic crop evapotranspiration. A case study in a semiarid region located in Hamedan Province in Iran was conducted with lysimeter measurements and weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation during 2008–2009. Both ANN and ANFIS models produced reasonable results. The ANN, with 6-6-1 architecture, presented a superior ability to estimate garlic crop evapotranspiration. The estimates of the ANN and ANFIS models were compared with the garlic crop evapotranspiration (ETc) values measured by lysimeter and those of the crop coefficient approach. Based on these comparisons, it can be concluded that the ANN and ANFIS techniques are suitable for simulation of ETc.     

Automatic computer-aided caries detection from dental x-ray images using intelligent level set

Multimedia Tools and Applications - Dental diseases have high risk of affection across the globe and mostly in adult population. The analysis of dental X-ray images has some difficulties in...     

Application of multidivisional bi-level programming to coordinate pricing and inventory decisions in a multiproduct competitive supply chain

This study concerns the coordination of pricing and inventory decisions in a multiproduct two-stage supply chain that consists of one manufacturer and multiple retailers within a competitive environment. The retailers order some substitutable products from a common manufacturer. It is assumed that channel members have different market power. The purpose of this paper is to coordinate pricing and inventory decisions such that utility of all involved levels (manufacturer and retailers) is met. Hence, a nonlinear multidivisional bi-level programming model is developed. This model considers both retailers and manufacturer when deciding about the pricing and production volume (for manufacturer) or amount of purchase (for retailers). A hybrid of genetic algorithm (GA) and local search method is proposed to solve the nonlinear bi-level model. This model is reduced to a nonlinear programming by replacing the Karush–Kuhn–Tucker (KKT) conditions of followers to the lower level of the model. Then, the obtained single-level model is relaxed to a linear model to achieve an upper bound (UB). Finally, a numerical example is presented to analyze which parameters have more effect on the price, lot size and, consequently, on the profit. Results show that increasing the market scale parameter of the manufacturer increases the profit of the manufacturer, but the market scale parameter of retailers has no effect on the manufacturer’s profit, although it increases the retailers’ profit.