The contents of numerous membrane lipids change upon ageing. However, it is unknown whether and how any of these changes are causally linked to lifespan regulation. Acyl chains contribute to the functional specificity of membrane lipids. In this study, working with C. elegans, we identified an acyl chain-specific sphingolipid, C22 glucosylceramide, as a longevity metabolite. Germline deficiency, a conserved lifespan-extending paradigm, induces somatic expression of the fatty acid elongase ELO-3, and behenic acid (22:0) generated by ELO-3 is incorporated into glucosylceramide for lifespan regulation. Mechanistically, C22 glucosylceramide is required for the membrane localization of clathrin, a protein that regulates membrane budding. The reduction in C22 glucosylceramide impairs the clathrin-dependent autophagic lysosome reformation, which subsequently leads to TOR activation and longevity suppression. These findings reveal a mechanistic link between membrane lipids and ageing and suggest a model of lifespan regulation by fatty acid-mediated membrane configuration.

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In this study, we established mathematical model of the carbon-containing pellet reduction process and used the neural network model to speed up the prediction process for actual production in the rotary hearth furnace (RHF). In order to obtain enough data to make a neural network, we calculated some results under different conditions by the pellet reduction mathematical model. Then, we developed and trained a feed-forward back-propagation neural network model using MATLAB software. The input parameters of the model included the temperature in the furnace, the reduction time, size and C/O ratio of the carboncontaining pellet and the output parameter was the final degree of metallization of the carbon-containing pellet. Beside, we optimized initial weights and thresholds of the model utilizing genetic algorithm, and also compared and analyzed the number of hidden layer neurons, training algorithm, learning rate, and population size of it. Finally, we chose 4-10-1 as the modeling structure of the neural network, the Levenberg-Marquardt training algorithm, the learning rate of 0.1 and population size of 150 as the optimal configuration. The coefficient correlation of training set and test set data calculated by the model indicates that the established neural network model has a high degree of suitability. Therefore, the neural network model combined with genetic algorithm has superiority as a reliable and efficient tool for predicting the reduction metallization rate of carbon-containing pellet in the RHF.

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