Exploring QSARs for Inhibitory Activity of Non-peptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVM

The support vector machine (SVM) and partial least square (PLS) methods were used to develop quantitative structure activity relationship (QSAR) models to predict the inhibitory activity of non-peptide HIV-1 protease inhibitors. Genetic algorithm (GA) was employed to select variables that lead to the best-fitted models. A comparison between the obtained results using SVM with those of PLS revealed that the SVM model is much better than that of PLS. The root mean square errors of the training set and the test set for SVM model were calculated to be 0.2027, 0.2751, and the coefficients of determination (R(2)) are 0.9800, 0.9355 respectively. Furthermore, the obtained statistical parameter of leave-one-out cross-validation test (Q(2)) on SVM model was 0.9672, which proves the reliability of this model. The results suggest that TE2, Ui, GATS5e, Mor13e, ATS7m, Ss, Mor27e, and RDF035e are the main independent factors contributing to the inhibitory activities of the studied compounds.The authors would like to acknowledge the computational chemistry laboratory at Al-Quds University for providing Matlab software and for the time dedicated for performing the calculations of the study

Bouveret syndrome : a rare case of gallstone ileus further complicated by stone migration

Bouveret syndrome is a rare cause of gastric outlet obstruction due to gallstone impaction in the pylorus or proximal duodenum. This paper reports a case of Bouveret syndrome in a 66-year-old male patient in whom pre-operative investigations revealed a gallstone within the distal stomach, but spontaneous migration of the stone resulted in intraoperative difficulty requiring further surgical exploration than originally anticipated.Publisher PDFPeer reviewe

Study of the effect of family patterns Style on opium abusers, cigarette abusers and normal group

AbstractThe Goal of present study was examining the effect of family Parents Style on opium abusers, cigarette abusers and normal group.The sample group included 120 (opium abusers, cigarette abusers, normal group). Samples selected from different area of Shiraz, Iran. Revised version Schaefen Family Patterns Style Questionnaire was used. By using one way ANOVA the effect of patterns Style on groups, were verifies.Cronbakh alpha coefficient was acceptable. There is a significant difference between groups

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers

Consideration the Effect of E-health system on Economic in Iran†

The purpose of this paper is to examine the relationships between Ehealth expenditure and economic growth in the case of Iran; with this regard we use annually data (1970-2011). E-health is one of the most important assets a human being has. It permits us to fully develop our capacities. If this asset erodes or it is not developed completely, it can cause physical and emotional weakening, causing obstacles in the lives of people. The results show that variables of the ratio of E-health expenditure to GDP, the ratio of investment to GDP and Growth rate of graduates have positive effect on growth rate. Keywords: E-health expenditure, Economic growth, Vector Autoregressive Model (VAR)

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds

Quantum Chemical QSAR Models to Distinguish Between Inhibitory Activities of Sulfonamides Against Human Carbonic Anhydrases I and II and Bovine IV Isozymes

Linear and nonlinear quantitative structure activity relationship models for predicting the inhibitory activities of sulfonamides toward different carbonic anhydrase isozymes were developed based on multilinear regression, principal component-artificial neural network and correlation ranking-principal component analysis, to identify a set of structurally based numerical descriptors. Multilinear regression was used to build linear quantitative structure activity relationship models using 53 compounds with their quantum chemical descriptors. For each type of isozyme, separate quantitative structure activity relationship models were obtained. It was found that the hydration energy plays a significant role in the binding of ligands to the CAI isozyme, whereas the presence of five-membered ring was detected as a major factor for the binding to the CAII isozyme. It was also found that the softness exhibited significant effect on the binding to CAIV isozyme. Principal component-artificial neural network and correlation ranking-principal component analysis analyses provide models with better prediction capability for the three types of the carbonic anhydrase isozyme inhibitory activity than those obtained by multilinear regression analysis. The best models, with improved prediction capability, were obtained for the hCAII isozyme activity. Models predictivity was evaluated by cross-validation, using an external test set and chance correlation test

Characterizations of continuous distributions through inequalities involving the expected values of selected functions

summary:Nanda (2010) and Bhattacharjee et al. (2013) characterized a few distributions with help of the failure rate, mean residual, log-odds rate and aging intensity functions. In this paper, we generalize their results and characterize some distributions through functions used by them and Glaser's function. Kundu and Ghosh (2016) obtained similar results using reversed hazard rate, expected inactivity time and reversed aging intensity functions. We also, via $w(\cdot )$-function defined by Cacoullos and Papathanasiou (1989), characterize exponential and logistic distributions, as well as Type 3 extreme value distribution and obtain bounds for the expected values of selected functions in reliability theory. Moreover, a bound for the varentropy of random variable $X$ is provided

The impact of climate change on water and energy security

Abstract The interdependent fundamental systems, water and energy, face abundant challenges, one of which is climate change, which is expected to aggravate water and energy securities. The hydropower industry's benefits have led to its development and growth around the world. Nonetheless, climate change is expected to disturb the future performance of hydropower plants. This study looks at the Seimareh Hydropower Plant to assess the potential vulnerability of hydropower plants to climate change. Results indicate that climate change will affect the area's hydrological variables and suggest an increase in temperatures and decrease in precipitation during a 30-year future period (2040–2069). It is predicted that Seimareh Dam's inflow will decrease by between 5.2% and 13.4% in the same period. These hydrological changes will affect the Seimareh plant's performance: current predictions are that the total energy produced will decrease by between 8.4% and 16.3%. This research indicates the necessity of considering climate change impacts in designing and maintaining hydraulic structures to reach their optimal performance

Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN