Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

Recycling of tobacco wastes after tobacco products manufacturing

Utilizing tobacco wastes is one of the important problems for tobacco industry. They can be divided into easy recycled which can be returned into technological process without special treatment, and irretrievable which can be recycled only after special treatment. Easy recycled wastes consist of leaf parts and large tobacco scraps, which are cleaned from the dust and then returned into manufacturing process. Irretrievable wastes consist of small tobacco scraps which use for reconstituted tobacco production and midrib parts which used for expanded stem manufacturing and added into cigarette for nicotine decreasing. Little tobacco scraps is not used for recycling and thus utilized. In the laboratory of technologies for tobacco products manufacturing possibility for utilizing little tobacco scraps for manufacturing new tobacco products: hookah blends and non-smoking products has been studied. Fractional composition of little tobacco scraps from cigarette industry has been defined. Samples of hookah blends and non-smoking products have been manufactured. New tobacco products manufactured from burley leaves were used as comparison. Tasting of these products has been done, utilizing methods developed in the laboratory. As the result, it has been found that samples made of wastes have better tasting score because of rich taste and tobacco aroma. Utilizing wastes instead of expensive leaf tobacco greatly decreases final cost of the product. As the result possibility and expediency of utilizing cigarette’s manufacturing wastes for hookah blends and non-smoking products manufacturing has been proved

Towards the proteome of the marine bacterium Rhodopirellula baltica: mapping the soluble proteins

The marine bacterium Rhodopirellula baltica, a member of the phylum Planctomycetes, has distinct morphological properties and contributes to remineralization of biomass in the natural environment. On the basis of its recently determined complete genome we investigated its proteome by 2-DE and established a reference 2-DE gel for the soluble protein fraction. Approximately 1000 protein spots were excised from a colloidal Coomassie-stained gel (pH 4-7), analyzed by MALDI-MS and identified by PMF. The non-redundant data set contained 626 distinct protein spots, corresponding to 558 different genes. The identified proteins were classified into role categories according to their predicted functions. The experimentally determined and the theoretically predicted proteomes were compared. Proteins, which were most abundant in 2-DE gels and the coding genes of which were also predicted to be highly expressed, could be linked mainly to housekeeping functions in glycolysis, tricarboxic acid cycle, amino acid biosynthesis, protein quality control and translation. Absence of predictable signal peptides indicated a localization of these proteins in the intracellular compartment, the pirellulosome. Among the identified proteins, 146 contained a predicted signal peptide suggesting their translocation. Some proteins were detected in more than one spot on the gel, indicating post-translational modification. In addition to identifying proteins present in the published sequence database for R. baltica, an alternative approach was used, in which the mass spectrometric data was searched against a maximal ORF set, allowing the identification of four previously unpredicted ORFs. The 2-DE reference map presented here will serve as framework for further experiments to study differential gene expression of R. baltica in response to external stimuli or cellular development and compartmentalization

Liquid Chromatography Electron Capture Dissociation Tandem Mass Spectrometry (LC-ECD-MS/MS) versus Liquid Chromatography Collision-induced Dissociation Tandem Mass Spectrometry (LC-CID-MS/MS) for the Identification of Proteins

Electron capture dissociation (ECD) offers many advantages over the more traditional fragmentation techniques for the analysis of peptides and proteins, although the question remains: How suitable is ECD for incorporation within proteomic strategies for the identification of proteins? Here, we compare LC-ECD-MS/MS and LC-CID-MS/MS as techniques for the identification of proteins.Experiments were performed on a hybrid linear ion trap–Fourier transform ion cyclotron resonance mass spectrometer. Replicate analyses of a six-protein (bovine serum albumin, apo-transferrin,lysozyme, cytochrome c, alcohol dehydrogenase, and β-galactosidase) tryptic digest were performed and the results analyzed on the basis of overall protein sequence coverage and sequence tag lengths within individual peptides. The results show that although protein coverage was lower for LC-ECDMS/MS than for LC-CID-MS/MS, LC-ECD-MS/MS resulted in longer peptide sequence tags,providing greater confidence in protein assignment

Особенности клинического течения острых респираторных инфекций у дошкольников с избыточной массой тела

.Уважаемые коллеги! Представляем вашему вниманию первые результаты исследования наших коллег из Луганска, изучающих респираторную заболеваемость и особенности ее клинических проявлений у детей дошкольного возраста с избыточной массой тела

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics