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Background Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. Methods Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores. Results TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) – (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823. Conclusions The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

With the deepening of energy transformation, developing electricity-centered integrated energy services has become an inevitable choice for integrated energy service providers (IESP). Implementation of renewable portfolio standards (RPS) has changed the competition pattern of electricity industry. The enhancement of consumer environmental awareness will also affect the vertical relation- ship of the electricity supply chain. Both of them will cause a profound change in the decision-making behavior of IESP. Based on this, considering RPS and consumer preference, the optimal decision of the IESP in trans-provinces electricity trading is explored. Firstly, the electricity trading structure of IESP under RPS is established, and the complex electricity supply-demand relationship between the trading subjects is clarified. Then, taking the maximum profit as the goal, the optimal decision model of each subject under the condition of non-cooperation and cooperation is established respectively. Finally, validity of the proposed model and method is verified with example analysis. The result shows that the model established helps IESP to obtain cleaner and more economical electricity in the context of electric-centered integrated energy services business model, which can promote the coordinated development of electricity market，integrating energy services and renewable energy industry.

Based on the analysis of local energy internet architecture, an overall architecture of integrated energy management and control system, which consists of four layers, is proposed in this paper. The main functions and construction contents of each layer in the overall architecture are analyzed in turn, such as the control, service and transaction applications for the application layer. On this basis, a case for industrial park is discussed. The architecture proposed in this paper can be more flexible, adaptable, extensible and comprehensive than traditional solutions, satisfying the needs of local energy internet.

End-tidal carbon dioxide (P(ET)CO₂) monitoring has become an important tool in clinical monitoring, but there are still limitations in practice. Low-frequency modulation was used to reliably acquire respiratory information. Then the disturbances of humidity and flow rate were removed by signal decomposition. Finally, the real-time concentration of CO₂ was calculated and displayed by an adjusted calibration function. Targeted experiments confirm that a period of 180 ms and a depth of 50% was the optimal choice. In this case, the effects of humidity and flow rate reflected by different components were removed effectively from the capnography. This capnometer obtains capnography with excellent accuracy and stability in long-term continuous monitoring.