Mouse Anti Xenopus nucleolin (95 kDa and 90 kDa isoforms) Hybridoma [q8-2B5]

Vibration signals are widely used as an effective way to fulfill gearbox fault diagnosis. However, it is quite challenging to extract effective fault features from noisy vibration signals and then to construct a reliable fault diagnosis model. This paper proposes a selective stacked denoising autoencoders (SDAE) with negative correlation learning (NCL) (SSDAE-NCL) for gearbox fault diagnosis. The component SDAEs are firstly constructed to extract effective fault features from vibration signals in the unsupervised-learning phase of SSDAE-NCL. Based on the extracted features, NCL is used to fine-tune the SDAE components to construct component classifiers in the supervised-learning phase of SSDAE-NCL. Finally, a selective ensemble is finished based on these divers and accurate component SDAEs for gearbox fault diagnosis. The motivation for developing ensemble of deep neural networks (DNNs) is that they can achieve higher accuracy and applicability than single component in machinery fault diagnosis. Furthermore, it can make an overall ensemble model easy to use in real cases for users, because it does not need too much prior knowledge about setup of a DNN model. The effectiveness of this SSDAE-NCL-based fault diagnosis method has been verified by experimental results on the vibration signal data from a gearbox test rig. The results illustrate that SSDAE-NCL learns effective discriminative features from vibration signals and achieves the better diagnosis accuracy in comparison with those typical DNNs (e.g.; SDAE, deep belief network (DBN)). (C) 2019 The Author. Published by Elsevier B.V.

Many pathogens (virus, bacteria, fungi, or parasites) have developed a wide variety of mechanisms to evade their host immune system. The budding yeast Saccharomyces cerevisiae has successfully been used to decipher some of these immune evasion strategies. This includes the cis-acting mechanism that limits the expression of the oncogenic Epstein-Barr virus (EBV)-encoded EBNA1 and thus of antigenic peptides derived from this essential but highly antigenic viral protein. Studies based on budding yeast have also revealed the molecular bases of epigenetic switching or recombination underlying the silencing of all except one members of extended families of genes that encode closely related and highly antigenic surface proteins. This mechanism is exploited by several parasites (that include pathogens such as Plasmodium, Trypanosoma, Candida, or Pneumocystis) to alternate their surface antigens, thereby evading the immune system. Yeast can itself be a pathogen, and pathogenic fungi such as Candida albicans, which is phylogenetically very close to S. cerevisiae, have developed stealthiness strategies that include changes in their cell wall composition, or epitope-masking, to control production or exposure of highly antigenic but essential polysaccharides in their cell wall. Finally, due to the high antigenicity of its cell wall, yeast has been opportunistically exploited to create adjuvants and vectors for vaccination.