Kvalitet vode za piće grada Užica

In 2014, the Serbian public was informed on several occasions about water supply problems in the City of Užice. The reason behind the problem was the presence of potentially toxic species Planktothrix rubescens (Cyanobacteria) in the waters of the Vrutci Reservoir (the water supply intake for the City of Užice), the alternative water supply intake Sušičko Vrelo and in the city distribution network. Although the measured concentration of hepatotoxic microcystin-LR (< 0.01 μg/L) was considerably below World Health Organisation guidelines for drinking water (1 μg/ L), the water was declared unsafe. The study presents and comments on the results of physicochemical, microbiological and biological analyses of both raw water samples collected from the water supply intakes and treated chlorinated drinking water samples from the distribution network of the City of Užice water supply system.Tokom 2014. godine javnost Srbije je nekoliko puta obaveštavana o problemima vodosnabdevanja grada Užica. Razlog je bilo prisustvo potencijalno toksične vrste Planktothrix rubescens (Cyanobacteria) u vodi akumulacije Vrutci (vodozahvata grada Užica), alternativnog vodozahvata Sušičkog vrela i u gradskoj distributivnoj mreži. Iako je izmerena koncentracija hepatotokisčnog mikrocistina-LR (< 0,01 μg/L) bila znatno niža од dozvoljene vrednosti propisane od strane Svetska zdravstveno organizacije (1 μg/L), voda je proglašavana zdravstveno neispravnom. U radu su prikazani i komentarisani rezultati fizičko-hemijskih, mikrobioloških i bioloških analiza sirove vode vodozahvata kao i prečišćene i hlorisane vode za piće iz distributivne mreže gradskog vodovoda Užice

Empirical multi-dimensional space for scoring peptide spectrum matches in shotgun proteomics

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10 000 to 20 000 for a several-hour LC MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches go (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the "grey area" below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, C-13 isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches

Empirical Multidimensional Space for Scoring Peptide Spectrum Matches in Shotgun Proteomics

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC–MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10 000 to 20 000 for a several-hour LC–MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the “grey area” below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, ¹³C isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches

Abstracts of The Second Eurasian RISK-2020 Conference and Symposium

This abstract book contains abstracts of the various research ideas presented at The Second Eurasian RISK-2020 Conference and Symposium.The RISK-2020 Conference and Symposium served as a perfect venue for practitioners, engineers, researchers, scientists, managers and decision-makers from all over the world to exchange ideas and technology about the latest innovation developments dealing with risk minimization