8 research outputs found
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, <sup>13</sup>C 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