During evolution, proteins containing newly emerged domains and the increasing proportion of multidomain proteins in the full genome-encoded proteome (GEP) have substantially contributed to increasing biological complexity. However, it is not known how these two potential structural factors are preferentially utilized at given physiological states. Here, we classified proteins according to domain number and domain age and explored the general trends across species for the utilization of proteins from GEP to various certain-state proteomes (CSPs, i.e., all the proteins expressed at certain physiological states). We found that multidomain proteins or only older domain-containing proteins are significantly overrepresented in CSPs compared with GEP, which is a trend that is stronger in multicellular organisms than in unicellular organisms. Interestingly, the strengths of overrepresentation decreased during evolution of multicellular eukaryotes. When comparing across CSPs, we found that multidomain proteins are more overrepresented in complex tissues than in simpler ones, whereas no difference among proteins with domains of different ages is evident between complex and simple tissues. Thus, biological complexity under certain conditions is more significantly realized by diverse domain organization than by the emergence of new types of domain. In addition, we found that multidomain or only older domain-containing proteins tend to evolve slowly and generally are under stronger purifying selection, which may partly result from their general overrepresentation trends in CSPs.
The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.
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