A novel hybrid method of β-turn identification in protein using binary logistic regression and neural network

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins

Comparison between artificial neural network (ANN) and regression analysis in tree felling time estimation

Tree felling is a most important one among the tree harvesting components. Production estimation of forest equipments is an important part of cost management in forestry operational units which is associated with reduction of the operating expenses. In other words, the high cost of investment in forest utilization, is a good reason for forest engineering research and modeling time. Many techniques such as regression, fuzzy logic, neural networks, etc. are utilized to estimate trees felling time. They make a logical connection between the tree felling time and the independent variables and could be used to predict the tree felling time for the future operations. In this study the regression analysis, two neural network models, multi-layer perceptron (MLP) and radial basis function (RBF) were used to predict the trees felling time in the cutting operations of the Neka Choob Co. In order to collect the felling time data, the time continuous study method was applied. For this purpose, 84 trees were selected from the marked stands and the net felling time was estimated, using the Multi Layer Perceptron and Radial Basis Function and also by the common method of linear regression analysis. The results showed that the Radial Basis Function network provided more accurate results in estimating the net tree felling time than the MLP neural network. Comparing the evaluation criteria of ANN with the stepwise regression methods,  showed that MLP and RBF neural networks had RMSE value of 0.94 and 0.81, respectively whereas the RMSE value of the regression model was 1.15

Reduced Impact Logging and Its Effect on Forest Harvesting Operation

Forest harvesting is one of the most important objectives of forest management, in which it will cause damages to the residual stand, using any of the current methods, but the improved harvesting methods might reduce these effects. One of these methods is application of the directional felling. It was tried to investigate effect of directional felling on number of damaged trees at both felling and winching processes, as well as on felling and winching times. Observation and measuring was made in Neka-Choob Company’s forests. Overall, 84 trees were selected from the total marked trees, from which 42 trees were painted before the felling operation to specify the cutting and the felling direction on them by the project supervisor support. The time required for cutting and winching operations and number of the damaged trees at both operations were recorded. Results showed that the average net time required for the cutting operation at directed trees was 2.95 minutes per tree whereas it was 4.04 minute per tree for undirected trees. Furthermore, the number of the damaged trees with diameter greater than 10 cm at the undirected felling was more than the directed felling method (100 vs. 25 trees). In addition, winching time at undirected trees was more than two times in comparison to directed trees and more residual trees were damaged at undirected felling method at winching process (50 vs. 14 trees)

Resolving Spectra Overlapping Based on Net Analyte Signal for Simultaneous Spectrophotometric Determination of Fluoxetine and Sertraline

The net analyte signal standard addition method was used for simultaneous spectrophotometric determination of sertraline and fluoxetine in pharmaceutical preparations. The method combines the advantages of the standard addition method with the net analyte signal concept to enable the extraction of information about an analyte from the spectra of multi-component mixtures. This method uses full spectrum realization and does not require calibration and prediction steps. Determination requires only a few measurements. The limit of detection for fluoxetine was 0.31 µg ml-1 and for sertraline was 0.20 µg ml-1. The root mean square error for fluoxetine was 0.45 and for sertraline was 0.39

Adaptive Neuro-Fuzzy Inference System (ANFIS) Applied for Spectrophotometric Determination of Fluoxetine and Sertraline in Pharmaceutical Formulations and Biological Fluid: Determination of fluoxetine and sertraline in pharmaceutical formulations and biological fluid

The UV-spectrophotometric method of analysis was proposed for simultaneous determination of fluoxetine (FLX) and sertraline (SRT). Considering the strong spectral overlap between UV-Vis spectra of these compounds, a previous separation should be carried out in order to determine them by conventional spectrophotometric techniques. Here, full-spectrum multivariate calibrations adaptive neuro-fuzzy inference system (ANFIS) method is developed.Adaptive neuro-fuzzyinference system (ANFIS) is a neuro fuzzy technique where the fusion is made between the neural network and the fuzzy inference system that is a computational method. The experimental calibration matrix was constructed with 30 samples. The concentration ranges considered were 5-120μg.mL−1fluoxetine and 10-120μg.mL−1sertraline .Absorbance data of the calibration standards were taken between 200-300nm with UV-Vis spectrophotometer. The method was applied to accurately and simultaneously determine the content of pharmaceutical in several synthetic mixtures and real samples. Assaying various synthetic mixtures of the components validated the presented methods. Mean recovery values were found to be 101.26% and 100.24%, respectively for determination of FLX and SRT

Interaction of electromagnetic field (10 kHz) with ferritin nanoparticles and antioxidant system of wheat at reproductive phase

Effects of magnetic and electromagnetic fields on the living organisms have drawn attention in the recent years, but their exact mechanisms are still unclear. In the present study, the effects of 10 kHz electromagnetic magnetic field on some physiological parameters of wheat (Triticum aestivum L.) were evaluated. The plants in their reproductive phase were treated for 4 days, each 5 h. Then the iron content, ratio of Fe-bound proteins to total proteins, content, size and secondary structure of ferritin nanoparticles as well as activity of antioxidant system were evaluated. The results showed that in comparison with the control plants, treatment with electromagnetic field significantly increased the iron contents of all organs except seeds and ratio of Fe-bound proteins to total proteins of seeds. The content and secondary structure of ferritin nanoparticles in edible parts of magnetic field treated plants were decreased significantly, but the hydrodynamic diameter was increased significantly compared to the control plants. This treatment also caused the significant increase of catalase activity and remarkably decreased peroxidase activity of wheat seeds, which in turn led to maintenance of membrane lipid peroxidation. These results suggested that electromagnetic field with applied frequency affected wheat plants by changing the content of total iron, Fe-bound proteins special ferritin and its characteristics and activation of antioxidant system

<span style="font-size:11.0pt;mso-bidi-font-size: 10.0pt;font-family:"Times New Roman";mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language:AR-SA" lang="EN-GB">Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment</span>

202-210<span style="font-size:11.0pt;mso-bidi-font-size: 10.0pt;font-family:" times="" new="" roman";mso-fareast-font-family:"times="" roman";="" mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:ar-sa"="" lang="EN-GB">The antiviral drugs that inhibit human immunodeficiency virus (HIV) entry to the target cells are already in different phases of clinical trials. They prevent viral entry and have a highly specific mechanism of action with a low toxicity profile. Few QSAR studies have been performed on this group of inhibitors. This study was performed to develop a quantitative structure–activity relationship (QSAR) model of the biological activity of indole glyoxamide derivatives as inhibitors of the interaction between HIV glycoprotein gp120 and host cell CD4 receptors. Forty different indole glyoxamide derivatives were selected as a sample set and geometrically optimized using Gaussian 98W. Different combinations of multiple linear regression (MLR), genetic algorithms (GA) and artificial neural networks (ANN) were then utilized to construct the QSAR models. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear log (1/EC50) prediction. The results that were obtained using GA-ANN were compared with MLR-MLR and MLR-ANN models. A high predictive ability was observed for the MLR, MLR-ANN and GA-ANN models, with root mean sum square errors (RMSE) of 0.99, 0.91 and 0.67, respectively (N = 40). In summary, machine learning methods were highly effective in designing QSAR models when compared to statistical method.</span

A quantitative structure–activity relationship study on HIV-1 integrase inhibitors using genetic algorithm, artificial neural networks and different statistical methods

In this work, quantitative structure–activity relationship (QSAR) study has been done on tricyclic phthalimide analogues acting as HIV-1 integrase inhibitors. Forty compounds were used in this study. Genetic algorithm (GA), artificial neural network (ANN) and multiple linear regressions (MLR) were utilized to construct the non-linear and linear QSAR models. It revealed that the GA–ANN model was much better than other models. For this purpose, ab initio geometry optimization performed at B3LYP level with a known basis set 6–31G (d). Hyperchem, ChemOffice and Gaussian 98W softwares were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. To include some of the correlation energy, the calculation was done with the density functional theory (DFT) with the same basis set and Becke’s three parameter hybrid functional using the LYP correlation functional (B3LYP/6–31G (d)). For the calculations in solution phase, the polarized continuum model (PCM) was used and also included optimizations at gas-phase B3LYP/6–31G (d) level for comparison. In the aqueous phase, the root–mean–square errors of the training set and the test set for GA–ANN model using jack–knife method, were 0.1409, 0.1804, respectively. In the gas phase, the root–mean–square errors of the training set and the test set for GA–ANN model were 0.1408, 0.3103, respectively. Also, the R2 values in the aqueous and the gas phase were obtained as 0.91, 0.82, respectively

DataSheet_1_Polyethylenimine-based iron oxide nanoparticles enhance cisplatin toxicity in ovarian cancer cells in the presence of a static magnetic field.pdf

BackgroundDrug resistance in cancer cells is a major concern in chemotherapy. Cisplatin (CIS) is one of the most effective chemotherapeutics for ovarian cancer. Here, we investigated an experimental approach to increase CIS cytotoxicity and overcome cell resistance using nanoparticle-based combination treatments.MethodsPolyethylenimine (PEI)-based magnetic iron oxide nanocomplexes were used for drug delivery in genetically matched CIS-resistant (A2780/CP) and -sensitive (A2780) ovarian cancer cells in the presence of a 20 mT static magnetic field. Magnetic nanoparticles (MNPs) were synthesized and bonded to PEI cationic polymers to form binary complexes (PM). The binding of CIS to the PM binary complexes resulted in the formation of ternary complexes PM/C (PEI–MNP/CIS) and PMC (PEI–MNP–CIS).ResultsCIS cytotoxicity increased at different concentrations of CIS and PEI in all binary and ternary delivery systems over time. Additionally, CIS induced cell cycle arrest in the S and G2/M phases and reactive oxygen species production in both cell lines. Ternary complexes were more effective than binary complexes at promoting apoptosis in the treated cells.ConclusionPEI-based magnetic nanocomplexes can be considered novel carriers for increasing CIS cytotoxicity and likely overcoming drug resistance of ovarian cancer cells.</p