ω-Azido fatty acids as probes to detect fatty acid biosynthesis, degradation, and modification
FAs play a central role in the metabolism of almost all known cellular life forms. Although GC-MS is regarded as a standard method for FA analysis, other methods, such as HPLC/MS, are nowadays widespread but are rarely applied to FA analysis. Here we present azido-FAs as probes that can be used to study FA biosynthesis (elongation, desaturation) or degradation (β-oxidation) upon their uptake, activation, and metabolic conversion. These azido-FAs are readily accessible by chemical synthesis and their metabolic products can be easily detected after click-chemistry based derivatization with high sensitivity by HPLC/MS, contributing a powerful tool to FA analysis, and hence, lipid analysis in general.
Fuzzy systems have been successfully used in the area of controllers for a long time. The Mamdani method is one of the most popular inference systems for practical applications. The main problem of Mamdani-type inference system and other fuzzy logic based controllers is how to gain the fuzzy rules the inference system based on. Several approaches have been proposed for automatic rule base identification. The bacterial type evolutionary algorithms have been successfully applied for solving this task. These algorithms are based on the Pseudo-Bacterial Genetic Algorithm and are supplied with operations and methods (e.g. the Levenberg-Marquardt method) to complete their task more efficiently. The goal is to create more accurate fuzzy rule bases from input-output data sets as quickly as possible. In this work, we summarize the bacterial type evolutionary algorithms used for fuzzy rule base identification.