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The paper presents the ideas on the integration of English language academic writing into the training of Mater of Pharmacy students. The academic literature demonstrates a powerful didactic potential for the development of both language and professional competence of students majoring in pharmacy. This fact is evidenced by the empirical study that demonstrates the finding reflected in students' graduation academic project performance. As measurement tools, we employed assessment rubrics of the graduate project, content analysis and questionnaire on teachers' feedback. The participants of the study were students from the Pharmacy faculty and teachers of English for Academic Purposes in cooperation with teachers of pharmacy. Together they outlined the criteria for the texts selection. According to the results, students mastered not only academic writing skills but also the skills of information processing and evaluation, critical thinking, presentation of information and academic integrity. The paper also presents methodological recommendations on academic texts selection for pharmaceutical students and forms of teaching academic writing to students of nonlinguistic specialities. The results of the study allow drawing the conclusion academic writing course will enhance professional competence and reduce students' misinterpreting of academic language.

The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

Aminoacyl-tRNA-synthetases are crucial enzymes for initiation step of translation. Possessing editing activity, they protect living cells from misincorporation of non-cognate and non-proteinogenic amino acids into proteins. Tyrosyl-tRNA synthetase (TyrRS) does not have such editing properties, but it shares weak stereospecificity in recognition of d-/l-tyrosine (Tyr). Nevertheless, an additional enzyme, d-aminoacyl-tRNA-deacylase (DTD), exists to overcome these deficiencies. The precise catalytic role of hydroxyl groups of the tRNA Tyr A76 in the catalysis by TyrRS and DTD remained unknown. To address this issue, [ 32 P]-labeled tRNA Tyr substrates have been tested in aminoacylation and deacylation assays. TyrRS demonstrates similar activity in charging the 2' and 3'-OH groups of A76 with l-Tyr. This synthetase can effectively use both OH groups as primary sites for aminoacylation with l-Tyr, but demonstrates severe preference toward 2'-OH, in charging with d-Tyr. In both cases, the catalysis is not substrate-assisted: neither the 2'-OH nor the 3'-OH group assists catalysis. In contrast, DTD catalyzes deacylation of d-Tyr-tRNA Tyr specifically from the 3'-OH group, while the 2'-OH assists in this hydrolysis., (Copyright © 2018 Elsevier Ltd. All rights reserved.)