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B Rabus

Publications and source records attributed to B Rabus.

2 recordsLinked to original sources

Simultaneous quantification of A-trichothecene mycotoxins in grains using liquid chromatography-atmospheric pressure chemical ionisation mass spectrometry.

An approach for simultaneous determination of the main type A-trichothecenes by liquid chromatography and atmospheric pressure chemical ionization mass spectrometry is described. Parameters for coupling of LC-MS such as cone voltage, nebulizing temperature and the LC flow-rate, were optimized to provide detection of mycotoxins with maximum sensitivity. Furthermore, the effects of cone voltage and temperature on the fragmentation pattern of the tested toxins were studied. Main type A-trichothecenes such as T-2 Toxin, HT-2 Toxin, acetyl T-2 Toxin, diacetoxyscirpenol, monoacetoxyscirpenol (15-acetoxyscirpenol) and neosolaniol were separated on a reversed-phase narrow bore C18 column, using a linear gradient and a flow-rate of 0.3 ml/min. Mass spectra were obtained in positive ion mode for confirmation and quantitation. The method involves extraction and purification of toxins by using multifunctional Mycosep columns. Deuterated T-2 Toxin was used as an internal standard. A linear working range between 80 and 500 microg/kg in matrix with an acceptable correlation coefficient was observed. The developed method was validated by using a blank oats sample. The detection limit in the matrix was found to be between 50 and 85 microg/kg in selected ion mode for all tested A-trichothecenes. Recovery data were found to be between 77 and 101%. Within run and day-to-day precision were determined as having comparable levels to those found using GC methods. Furthermore, the matrix effect was investigated by comparing the internal standard versus the external standard method in quantification studies. In addition, the developed method was applied for the analysis of naturally contaminated oats, maize, barley and wheat samples.

Atmospheric Pressure↗

An associative memory that can form hypotheses: a phase-coded neural network.

Nonlinear associative memories as realized, e.g., by Hopfield nets are characterized by attractor-type dynamics. When fed with a starting pattern, they converge to exactly one of the stored patterns which is supposed to be most similar. These systems cannot render hypotheses of classification, i.e., render several possible answers to a given classification problem. Inspired by von der Malsburg's correlation theory of brain function, we extend conventional neural network architectures by introducing additional dynamical variables. Assuming an oscillatory time structure of neural firing, i.e., the existence of neural clocks, we assign a so-called phase to each formal neuron. The phases explicitly describe detailed correlations of neural activities neglected in conventional neural network architectures. Implementing this extension into a simple self-organizing network based on a feature map, we present an associative memory that actually is capable of forming hypotheses of classification.

Animals↗