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High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.

Quantitative structure-activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors. Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors. In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine. The experimental results of classifying the neuraminidase inhibitors of influenza A (H1N1) viruses show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors.

Renal transplantation is the best management for patients suffering from end-stage renal disease (ESRD). It has many benefits which overcome the complications of dialysis, it has some acute and long term complications that could result in failure. Failure of transplant is the fourth cause of death among patients with end stage kidney disease. The causes of failure is still a debate but rejection, drug toxicity and fibrosis as well as chronic allograft nephropathy and patient’s immunity related factors. This review will provide important information regarding the causes associated with post-renal transplant failure.

Background: A substantial group of patients with gallstone disease experience negative outcome after surgical removal of the gallbladder (cholecystectomy). Early identification of these patients is important. Purpose: The purpose of the study was to recognize predictors (trait anxiety and clinical symptoms) of negative symptomatic outcomes at 5 weeks after cholecystectomy. Materials and Methods: Consecutive patients (n = 66), 18–60 years, with symptomatic gallstone disease, completed symptom checklists and the state-trait anxiety inventory preoperatively and at 6 weeks after cholecystectomy. Results: High trait anxiety was the only predictor of persistence of biliary symptoms at 6 weeks after cholecystectomy (OR = 6.79). Conclusion: In addition to clinical symptoms, high trait anxiety is a predictor of negative symptomatic outcome at 6 weeks after cholecystectomy. Trait anxiety should be evaluated to aim at a patient-tailored approach in gallstone disease.

Background: otitis media (OM) and its complications resulted in high prevalence of hearing loss and increasing the rates of health care visits with special regard to the developing countries. Aim of the work: this study aimed toevaluate the knowledge, attitude and practice (KAP) toward otitis media among adult teachers in Kingdom of Saudi Arabia (KSA). Patients and Methods: this isa cross sectional questionnaire based study and it was conducted among a random sample of teachers in KSA from March to June 2017. All the teachers were inter viewed in their schools during the break period and answered the questionnaire that was comprised of four objects including the socio-demographics of subjects, knowledge, attitude and practice pattern of participants toward otitis media. Results: the overall knowledge about the risk factors, disease prevention and serous type was improper. Also, a poor level of attitude and practice pattern was found among most of the subjects. The overall KAP of the teachers was poor among 58.8%, while 41.2% showed adequate KAP about Otitis media. The good KAP showed a significant association with higher economic level, while other variables showed no association with KAP including age, gender and cigarette smoking. Conclusion: the suboptimal knowledge about otitis media would result in poor attitude and practice pattern which increased the prevalence and complications of otitis media. The economic level was associated with OM, thus it was considered a vital factor in prevention of otitis media.

Descriptor selection is a procedure widely used in chemometrics. The aim is to select the best subset of descriptors relevant to the quantitative structure-activity relationship (QSAR) study being considered. In this paper, a new descriptor selection method for the QSAR classification model is proposed by adding a new weight inside L1-norm. The experimental results from classifying the neuraminidase inhibitors of influenza A viruses (H1N1) demonstrate that the proposed method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance and the number of selected descriptors.

High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity (property) relationship (QSAR/QSPR) classification methods in chemometrics. Applying variable selection is essential to improve the performance of the classification task. Variable selection is well-known as an NP-hard optimization problem. Various evolutionary algorithms are dedicated to solving this problem in the literature. Recently, a pigeon optimization algorithm was proposed, which has been successfully applied to solve various continuous optimization problems. In this paper, a new time-varying transfer function is proposed to improve the exploration and exploitation capability of the binary pigeon optimization algorithm in selecting the most relevant descriptors (variables) in QSAR/QSPR classification models with high classification accuracy and short computing time. Based on seven benchmark biopharmaceutical datasets, the experimental results reveal the capability of the proposed time-varying transfer function to achieve high classification accuracy with minimizing the number of selected descriptors and reducing the computational time.

The study of physicochemical properties of solid catalyst such as particle size, surface area and basicity are significant today since their reactivity depend on these properties. The number of defect sites in CaO, an alkaline metal oxide is important in heterogeneous catalysis. In this research work, nanostructured CaO was synthesized from commercial CaO via hydration-dehydration method. The surface modified CaO was calcined at various temperatures from 300°C to 800°C respectively under vacuum atmosphere of 10-3mbar. The prepared nanostructured CaO was then characterized using Thermogravimetric Analysis (TGA) and Fourier Transform Infrared (FTIR). The basicity then determined by back titration method. This research found that when activation temperature increased, the basicity also increases. For optimization, the prepared nanostructured CaO-600 was selected and applied for base transesterification reaction using algae species, Kappaphycusalvarezii to produce biodiesel. The latter reaction was then confirmed by Nuclear Magnetic Resonance (NMR) spectroscopy.