Phosphorylation of Kif26b Promotes Its Polyubiquitination and Subsequent Proteasomal Degradation during Kidney Development

Kif26b, a member of the kinesin superfamily proteins (KIFs), is essential for kidney development. Kif26b expression is restricted to the metanephric mesenchyme, and its transcription is regulated by a zinc finger transcriptional regulator Sall1. However, the mechanism(s) by which Kif26b protein is regulated remain unknown. Here, we demonstrate phosphorylation and subsequent polyubiquitination of Kif26b in the developing kidney. We find that Kif26b interacts with an E3 ubiquitin ligase, neural precursor cell expressed developmentally down-regulated protein 4 (Nedd4) in developing kidney. Phosphorylation of Kif26b at Thr-1859 and Ser-1962 by the cyclin-dependent kinases (CDKs) enhances the interaction of Kif26b with Nedd4. Nedd4 polyubiquitinates Kif26b and thereby promotes degradation of Kif26b via the ubiquitin-proteasome pathway. Furthermore, Kif26b lacks ATPase activity but does associate with microtubules. Nocodazole treatment not only disrupts the localization of Kif26b to microtubules but also promotes phosphorylation and polyubiquitination of Kif26b. These results suggest that the function of Kif26b is microtubule-based and that Kif26b degradation in the metanephric mesenchyme via the ubiquitin-proteasome pathway may be important for proper kidney development

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Abstract Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number