Simultaneous determination of natural and synthetic steroid estrogens and their conjugates in aqueous matrices by liquid chromatography / mass spectrometry

An analytical method for the simultaneous determination of nine free and conjugated steroid estrogens was developed with application to environmental aqueous matrices. Solid phase extraction (SPE) was employed for isolation and concentration, with detection by liquid chromatography/mass spectrometry (LC/MS) using electrospray ionisation (ESI) in the negative mode. Method recoveries for various aqueous matrices (wastewater, lake and drinking water) were determined, recoveries proving to be sample dependent. When spiked at 50 ng/l concentrations in sewage influent, recoveries ranged from 62-89 % with relative standard deviations (RSD) < 8.1 %. In comparison, drinking water spiked at the same concentrations had recoveries between 82-100 % with an RSD < 5%. Ion suppression is a known phenomenon when using ESI; hence its impact on method recovery was elucidated for raw sewage. Both ion suppression from matrix interferences and the extraction procedure has bearing on the overall method recovery. Analysis of municipal raw sewage identified several of the analytes of interest at ng/l concentrations, estriol (E3) being the most abundant. Only one conjugate, estrone 3-sulphate (E1-3S) was observe

Qualitative aspects and validation of a screening method for pesticides in vegetables and fruits based on liquid chromatography coupled to full scan high resolution (Orbitrap) mass spectrometry

The analytical capabilities of liquid chromatography with single-stage high-resolution mass spectrometry have been investigated with emphasis on qualitative aspects related to selective detection during screening and to identification. The study involved 21 different vegetable and fruit commodities, a screening database of 556 pesticides for evaluation of false positives, and a test set of 130 pesticides spiked to the commodities at 0.01, 0.05, and 0.20 mg/kg for evaluation of false negatives. The final method involved a QuEChERS-based sample preparation (without dSPE clean up) and full scan acquisition using alternating scan events without/with fragmentation, at a resolving power of 50,000. Analyte detection was based on extraction of the exact mass (±5 ppm) of the major adduct ion at the database retention time ±30 s and the presence of a second diagnostic ion. Various options for the additional ion were investigated and compared (other adduct ions, M + 1 or M + 2 isotopes, fragments). The two-ion approach for selective detection of the pesticides in the full scan data was compared with two alternative approaches based on response thresholds. Using the two-ion approach, the number of false positives out of 11,676 pesticide/commodity combinations targeted was 36 (0.3 %). The percentage of false negatives, assessed for 2,730 pesticide/commodity combinations, was 13 %, 3 %, and 1 % at the 0.01-, 0.05-, and 0.20-mg/kg level, respectively (slightly higher with fully automated detection). Following the SANCO/12495/2011 protocol for validation of screening methods, the screening detection limit was determined for 130 pesticides and found to be 0.01, 0.05, and ≥0.20 mg/kg for 86, 30, and 14 pesticides, respectively. For the detected pesticides in the spiked samples, the ability for unambiguous identification according to EU criteria was evaluated. A proposal for adaption of the criteria was made

Trace analysis of environmental matrices by large-volume injection and liquid chromatography-mass spectrometry

The time-honored convention of concentrating aqueous samples by solid-phase extraction (SPE) is being challenged by the increasingly widespread use of large-volume injection (LVI) liquid chromatography–mass spectrometry (LC–MS) for the determination of traces of polar organic contaminants in environmental samples. Although different LVI approaches have been proposed over the last 40 years, the simplest and most popular way of performing LVI is known as single-column LVI (SC-LVI), in which a large-volume of an aqueous sample is directly injected into an analytical column. For the purposes of this critical review, LVI is defined as an injected sample volume that is ≥10% of the void volume of the analytical column. Compared with other techniques, SC-LVI is easier to set up, because it requires only small hardware modifications to existing autosamplers and, thus, it will be the main focus of this review. Although not new, SC-LVI is gaining acceptance and the approach is emerging as a technique that will render SPE nearly obsolete for many environmental applications.In this review, we discuss: the history and development of various forms of LVI; the critical factors that must be considered when creating and optimizing SC-LVI methods; and typical applications that demonstrate the range of environmental matrices to which LVI is applicable, for example drinking water, groundwater, and surface water including seawater and wastewater. Furthermore, we indicate direction and areas that must be addressed to fully delineate the limits of SC-LVI

Advances in mass spectrometry-based post-column bioaffinity profiling of mixtures

In the screening of complex mixtures, for example combinatorial libraries, natural extracts, and metabolic incubations, different approaches are used for integrated bioaffinity screening. Four major strategies can be used for screening of bioactive mixtures for protein targets—pre-column and post-column off-line, at-line, and on-line strategies. The focus of this review is on recent developments in post-column on-line screening, and the role of mass spectrometry (MS) in these systems. On-line screening systems integrate separation sciences, mass spectrometry, and biochemical methodology, enabling screening for active compounds in complex mixtures. There are three main variants of on-line MS based bioassays: the mass spectrometer is used for ligand identification only; the mass spectrometer is used for both ligand identification and bioassay readout; or MS detection is conducted in parallel with at-line microfractionation with off-line bioaffinity analysis. On the basis of the different fields of application of on-line screening, the principles are explained and their usefulness in the different fields of drug research is critically evaluated. Furthermore, off-line screening is discussed briefly with the on-line and at-line approaches

The peroxisome: still a mysterious organelle

More than half a century of research on peroxisomes has revealed unique features of this ubiquitous subcellular organelle, which have often been in disagreement with existing dogmas in cell biology. About 50 peroxisomal enzymes have so far been identified, which contribute to several crucial metabolic processes such as β-oxidation of fatty acids, biosynthesis of ether phospholipids and metabolism of reactive oxygen species, and render peroxisomes indispensable for human health and development. It became obvious that peroxisomes are highly dynamic organelles that rapidly assemble, multiply and degrade in response to metabolic needs. However, many aspects of peroxisome biology are still mysterious. This review addresses recent exciting discoveries on the biogenesis, formation and degradation of peroxisomes, on peroxisomal dynamics and division, as well as on the interaction and cross talk of peroxisomes with other subcellular compartments. Furthermore, recent advances on the role of peroxisomes in medicine and in the identification of novel peroxisomal proteins are discussed

Determining negation scope and strength in sentiment analysis

A key element for decision makers to track is their stakeholders' sentiment. Recent developments show a tendency of including various aspects other than word frequencies in automated sentiment analysis approaches. One of these aspects is negation, which can be accounted for in various ways. We compare several approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a significant increase in overall sentiment classification accuracy and macro-level F1 of 5.5% and 6.2%, respectively, compared to not accounting for negation. Additionally optimizing sentiment modification of negated words to a value of -1.27 rather than -1 yields a significant 7.1% increase in accuracy and a significant 8.0% increase in macro-level F1

Accounting for negation in sentiment analysis

more and more urgent as virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web. The role of negation in sentiment analysis has been explored only to a limited extent. In this paper, we investigate the impact of accounting for negation in sentiment analysis. To this end, we utilize a basic sentiment analysis framework – consisting of a wordbank creation part and a document scoring part – taking into account negation. Our experimental results show that by accounting for negation, precision relative to human ratings increases with 1.17%. On a subset of selected documents containing negated words, precision increases with 2.23%

Analyzing sentiment while accounting for negation scope and strength

Recent developments in automated sentiment analysis show a tendency of accounting for various aspects other than word frequencies. One of these aspects is negation. We compare several approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach turns out to be to consider the first two words, following a negation keyword, to be negated by that keyword. Additionally, we propose to optimize the sentiment modification in case of negation to a value of –1.27 rather than –1

Adaptive tactical pricing in multi-agent supply chain markets using economic regimes

In today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two-layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs