Abstract: Grab samples were collected in the summer to characterize selected pharmaceuticals and personal care products (PPCPs) in the influent of two wastewater treatment plants (WWTPs) in Jordan. Liquid chromatography tandem mass spectrometry (LC–MS/MS) was utilized to determine the concentrations of 18 compounds of PPCPs. Among all of the PPCPs analyzed, eight compounds were detected in the influent samples (1,7-dimethylxanthine, acetaminophen, caffeine, carbamazepine, cotinine, morphine, sulfamethoxazole and trimethoprim). However, five compounds (amphetamine, cimetidine, diphenhydramine, methylenedioxyamphetamine (MDA) and sulfachloropyridazine) were not detected in collected samples (below the detection limits
Abstract: A sensitive and specific method for quantitative
determination of aflatoxins(B1, B2, G1,G2), deoxynivalenol,
fumonisin(B1,B2), ochratoxin A, zearalenone, T-2 and HT-2 in
roasted and ground grains using liquid chromatography combined
with tandem mass spectrometry. A double extraction using a
phosphate buffer solution followed by methanol was applied to
achieve effective co extraction of 11 mycotoxins. A multitoxin
immunoaffinity column for all these mycotoxins was used to clean up
the extract. The LODs of mycotoxins were 0.1~6.1 μg/kg, LOQs were
0.3~18.4 μg/kg. Forty seven samples collected from Seoul (Korea) for
mycotoxin contamination monitoring. The results showed that the
occurrence of zearalenone and deoxynivalenol were frequent.
Zearalenone was detected in all samples and deoxynivalenol was
detected in 80.9 % samples in the range 0.626 ~ 29.264 μg/kg and N.D
~ 48.332 μg/kg respectively. Fumonisins and ochratoxin A were
detected in 46.8% samples and 17 % samples respectively, aflatoxins
and T-2/HT-2 toxins were not detected all samples.
Abstract: Tandem mass spectrometry (MS/MS) is the engine
driving high-throughput protein identification. Protein mixtures possibly
representing thousands of proteins from multiple species are
treated with proteolytic enzymes, cutting the proteins into smaller
peptides that are then analyzed generating MS/MS spectra. The
task of determining the identity of the peptide from its spectrum
is currently the weak point in the process. Current approaches to de
novo sequencing are able to compute candidate peptides efficiently.
The problem lies in the limitations of current scoring functions. In this
paper we introduce the concept of proteome signature. By examining
proteins and compiling proteome signatures (amino acid usage) it is
possible to characterize likely combinations of amino acids and better
distinguish between candidate peptides. Our results strongly support
the hypothesis that a scoring function that considers amino acid usage
patterns is better able to distinguish between candidate peptides. This
in turn leads to higher accuracy in peptide prediction.