Production and use of Scenedesmus acuminatus biomass in synthetic municipal wastewater for integrated biorefineries.

Bioethanol production from algal biomass is a promising alternative for sustainable biofuel production. Algae possess a high photosynthetic capacity and an adaptive ability to thrive under harsh environmental conditions. The potential properties of Scenedesmus acuminatus CCALA 436 were assessed in this research for its bioethanol efficiency, and the effects of growing the algae in wastewater and at different concentrations of mepiquat chloride were studied. Also, pre-treatment efficiencies of different concentrations of calcium oxide were carried out on microalgae biomass. Superoxide dismutase, catalase activity, glutathione, and malondialdehyde contents of microalgae were examined, and the changes in chlorophyll, photoprotective carotenoid contents, and protein concentrations were determined. The results revealed that the maximum sugar and ethanol contents of Scenedesmus acuminatus CCALA 436 were 44.7 ± 1.5% and 20.32 g/L, respectively, for 50% wastewater and mepiquat chloride (2.5 mg/L) after pre-treatment with calcium oxide (0.08%). Additionally, the levels of oxidative enzymes varied depending on the wastewater concentrations. These findings indicate Scenedesmus acuminatus CCALA 436 grown in wastewater and mepiquat chloride can be used for the treatment of wastewater and the production of ethanol and high-value products such as carotenoid.

Sequential modelling for carbohydrate and bioethanol production from Chlorella saccharophila CCALA 258: a complementary experimental and theoretical approach for microalgal bioethanol production.

Bioethanol production from microalgal biomass is an attractive concept, and theoretical methods by which bioenergy can be produced indicate saving in both time and efficiency. The aim of the present study was to investigate the efficiencies of carbohydrate and bioethanol production by Chlorella saccharophila CCALA 258 using experimental, semiempirical, and theoretical methods, such as response surface methods (RSMs) and an artificial neural network (ANN) through sequential modeling. In addition, the interactive response surface modeling for determining the optimum conditions for the variables was assessed. The results indicated that the maximum bioethanol concentration was 11.20 g/L using the RSM model and 11.17 g/L using the ANN model under optimum conditions of 6% (v/v %) substrate and 4% (v/v %) inoculum at 96-h fermentation, pH 6, and 40 °C. In addition, the value of the experimental data for carbohydrate concentration was 0.2510 g/g biomass at ANN with the maximums of 50% (v/v) wastewater concentration, 4% (m/m) hydrogen peroxide concentration, and 6000 U/mL enzyme activity. Finally, although the RSM model was more effective than the ANN model for predicting bioethanol concentration, the ANN model yielded more precise values than the RSM model for carbohydrate concentration.

A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques.

BACKGROUND: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones. INTRODUCTION: Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group. METHODS: Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs. RESULTS: We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems. CONCLUSION: The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.

Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.

BACKGROUND AND OBJECTIVES: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. METHODS: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. RESULTS: According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. CONCLUSION: This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs.

Thermo-resistant green microalgae for effective biodiesel production: isolation and characterization of unialgal species from geothermal flora of Central Anatolia.

Oil content and composition, biomass productivity and adaptability to different growth conditions are important parameters in selecting a suitable microalgal strain for biodiesel production. Here, we describe isolation and characterization of three green microalgal species from geothermal flora of Central Anatolia. All three isolates, namely, Scenedesmus sp. METUNERGY1402 (Scenedesmus sp. ME02), Hindakia tetrachotoma METUNERGY1403 (H. tetrachotoma ME03) and Micractinium sp. METUNERGY1405 (Micractinium sp. ME05) are adaptable to growth at a wide temperature range (25-50 °C). Micractinium sp. ME05, particularly has superior properties for biodiesel production. Biomass productivity, lipid content and lipid productivity of this isolate are 0.17 g L(-1) d(-1), 22.7% and 0.04 g L(-1) d(-1), respectively. In addition, Micractinium sp. ME05 and Scenedesmus sp. ME03 mainly contain desirable fatty acid methyl esters (i.e. 16:0, 16:1, 18:0 and 18:1) for biodiesel production. All isolates can further be improved via genetic and metabolic engineering strategies.