Magic™ Anti-KDR monoclonal antibody, clone 90207 [PE]

The house fly, Musca domestica (Linnaeus) (Diptera: Muscidae), is a major pest for human and livestock health and is also resistant to different insecticides. Herein, six M. domestica populations were collected, five of them from industrial cattle farms and the Koohrang population from a remote area as a susceptible population. The resistance/susceptibility of populations to three pyrethroids was evaluated. High levels of permethrin resistance were observed in all field populations and the resistance ratios (RRs) were estimated to vary from 52- to 129-fold. Resistant populations also exhibited resistance to other pyrethroids (cypermethrin and deltamethrin), with RRs ranging between 45- and 180-fold. According to synergistic (piperonyl butoxide, diethyl maleate and triphenyl phosphate) and enzymatic assays, resistant populations exhibited multiple resistance phenotypes. Cytochrome P450 monooxygenases (P450s), glutathione S-transferases (GSTs), and carboxylesterases (CarEs) were found to be involved in pyrethroid resistance in Isfahan population, P450s and GSTs in Mobarake population and CarEs detoxified pyrethroids in Natanz and Alavijeh populations. As substitution of Leucine (CTT) with Phenylalanine (TTT) at position 1014 of the voltage sensitive sodium channel (VSSC) gene is the most common mutation conferring resistance to pyrethroids in M. domestica, we sequenced a partial fragment of IIS6 and L1014F mutation was detected in all resistant populations. The present study provides valuable information for early detection of pyrethroid resistance and developing resistance management strategies in the house fly populations.

The focus of the current work attempts to propose a purely data-based model for generating residuals for non-Gaussian process monitoring purposes, the idea of residual generation is borrowed from the field of model-based fault detection and applied in statistical monitoring, the generated residual instead of the measured variables is thus modeled and monitored. The proposed approach first employs the modified independent component analysis (MICA) algorithm to extract independent components (ICs) from a given dataset. Secondly, through assuming but only one variable is missing at one time, the known data regression (KDR) method dealing with missing data problem is then used for estimating the corresponding ICs. The inconsistency between the actual and estimated ICs is called residual and may present much lower level of non-Gaussianity, in contrast to the actual ICs. Thirdly, a principal component analysis based statistical monitoring model can be utilized for online fault detection based on the generated residual. Finally, the superiority and efficiency of the MICA-KDR approach over its counterparts are validated by implementing comparisons on two industrial processes, the proposed MICA-KDR method is demonstrated to be a comparative alternative in monitoring non-Gaussian processes (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.