Insulin/IGF-1 Signaling (IIS) is known to constrain longevity by inhibiting the transcription factor FOXO. How phosphorylation mediated by IIS kinases regulates lifespan beyond FOXO remains unclear. Here, we profile IIS-dependent phosphorylation changes in a large-scale quantitative phosphoproteomic analysis of wild-type and three IIS mutant Caenorhabditis elegans strains. We quantify more than 15,000 phosphosites and find that 476 of these are differentially phosphorylated in the long-lived daf-2/insulin receptor mutant. We develop a machine learning-based method to prioritize 25 potential lifespan-related phosphosites. We perform validations to show that AKT-1 pT492 inhibits DAF-16/FOXO and compensates the loss of daf-2 function, that EIF-2α pS49 potently inhibits protein synthesis and daf-2 longevity, and that reduced phosphorylation of multiple germline proteins apparently transmits reduced DAF-2 signaling to the soma. In addition, an analysis of kinases with enriched substrates detects that casein kinase 2 (CK2) subunits negatively regulate lifespan. Our study reveals detailed functional insights into longevity.

In order to diagnose bearing fault with less samples,combined improved EEMD with SVM in the bearing fault intelligent diagnosis under low dimensional small sample is researched in this paper.It is applied to the binary classification and identification in bearing normal and fault state.The results show that depend only on less sample data 5d feature vector classification after training, SVM using linear kernel function and polynomial kernel function classification accuracy is still up to 100. classification accuracy under the less sample data in less 5d characteristic vectors by RBF kernel function under Sigmoid kernel function is relatively low.Choose appropriate SVM kernel function completely can realize low dimensional small sample right binary classification.

Though the Support Vector Regression Machines (SVRM) is considered to be an effective method for time series prediction, its performance is greatly influenced by its parameters. In order to improve the rationality of parameter setting, the influences of the parameters (the number of support vectorsNSand the prediction lengthNE) and signal characteristics on the SVRM performance were discussed. The results proved that the existence of confliction between prediction accuracy and prediction efficiency, and SVRM may inappropriate to long-term prediction, andNSshould be greater than a threshold which depends on the signal characteristics for an accurate prediction result. The research results may provide a theoretical basis for the improvments of SVRM algorithm.

In order to perform the bearing intelligent fault diagnosis,combined improved EEMD with SVM respectively applied to the binary classification identification of bearing normal and ball fault, normal and inner circle fault,normal and outer ring fault in this paper.Improve EEMD decomposed 9d normalized energy for characteristic vector,the SVM binary classification and recognition of bearings normal and ball fault, normal and inner circle fault, normal and outer ring fault is researched.Compared to the SVM classification accuracy using different kernel functions that is linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear kernel function and polynomial kernel function is a hundred percent.Bearing normal and ball fault,normal and inner circle fault,normal and outer ring fault is completely correct apart.And there are the classification errors based on RBF kernel function and Sigmoid kernel functions.