Multivariate Analysis of Sediment Toxicity in an Ocean Ecosystem: A Southern California Bight Case Study

Subject. This article explores the issues of business tourism clustering in Greater Moscow. Objectives. The article intends to justify the need to create a business tourism cluster in Greater Moscow to improve the investment climate in the region. Methods. For the study, I used a multivariate analysis, forecasting, and extrapolation. Results. The article shows a certain relationship between the efficient functioning of the business tourism cluster and the economy's development. Conclusions and Relevance. Certain types of tourist clusters can serve as platforms for attracting investors and implementing marketing plans. The business tourism cluster is a link between buyers and sellers in various industries. The results of the study can be used to improve the effectiveness of the cluster initiative in business tourism, as well as find ways of cooperation between the State and private investors when creating the business tourism cluster in Greater Moscow.

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Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-7-12-19 ◽

2020 ◽

Vol 86 (7) ◽

pp. 12-19

Author(s):

I. V. Plyushchenko ◽

D. G. Shakhmatov ◽

I. A. Rodin

Keyword(s):

Mass Spectrometry ◽

Multivariate Analysis ◽

Large Scale ◽

Complex Composition ◽

Unified Protocol ◽

Chromatography Mass Spectrometry ◽

Marker Substances ◽

Selection Testing ◽

Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

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