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The coverage of traditional wireless sensor networks (WSNs) is limited by node energy and data redundancy, forcing WSNs to be interrupted abnormally. To this end, a novel optimization cooperative coverage algorithm with controllable threshold-parameters (OCC-CT) is proposed. The algorithm first determines the location information of the focus target nodes (FTNs), and uses the genetic algorithm (GA) to give the node path planning. Second, the controllable threshold parameters and mutation parameters are used to control event domain nodes. Clustering is optimized to make the clustering of the nodes more uniform, so as to reduce the energy consumption of the nodes and improve the search ability of the global target nodes. Third, the adaptation function is used to cover the continuity of the covered target locations and the monitoring range of the nodes. Optimization is performed to achieve the goals of increasing network coverage and extending the network lifetime. Finally, simulation results show that the OCC-CT algorithm, compared with other three algorithms in this paper, improves 0.11 and 0.16 on average in terms of network coverage, network lifetime, and 0.14 in terms of network energy overhead, thereby further verifying OCC-CT algorithm has strong stability and effectiveness.

Background: Recent research has found a new way of cell death: disulfidptosis. Under glucose starvation, abnormal accumulation of disulfide molecules such as Cystine in Solute Carrier Family 7 Member 11 (SLC7A11) overexpression cells induced disulfide stress to trigger cell death. The research on disulfidptosis is still in its early stages, and its role in the occurrence and development of colorectal malignancies is still unclear., Method: In this study, we employed bioinformatics methods to analyze the expression and mutation characteristics of disulfidptosis-related genes (DRGs) in colorectal cancer. Consensus clustering analysis was used to identify molecular subtypes of Colorectal Adenocarcinoma (COAD) associated with disulfidptosis. The biological behaviors between subtypes were analyzed to explore the impact of disulfidptosis on the tumor microenvironment. Constructing and validating a prognostic risk model for COAD using diverse data. The influence of key genes on prognosis was evaluated through SHapley Additive exPlanations (SHAP) analysis, and the predictive capability of the model was assessed using Overall Survival analysis, Area Under Curve and risk curves. The immunological status of different patients and the prediction of drug treatment response were determined through immune cell infiltration, TMB, MSI status, and drug sensitivity analysis. Single-cell analysis was employed to explore the expression of genes at the cellular level, and finally validated the expression of key genes in clinical samples., Result: By integrating the public data from two platforms, we identified 2 colorectal cancer subtypes related to DRGs. Ultimately, we established a prognosis risk model for COAD using 7 genes (FABA4+GIPC2+EGR3+HOXC6+CCL11+CXCL10+ITLN1). SHAP analysis can further explained the positive or negative impact of gene expression on prognosis. By dividing patients into high-risk and low-risk groups, we found that patients in the high-risk group had poorer prognosis, higher TMB, and a higher proportion of MSI-H and MSI-L statuses. We also predicted that drugs such as 5-Fluorouracil, Oxaliplatin, Gefitinib, and Sorafenib would be more effective in low-risk patients, while drugs like Luminesib and Staurosporine would be more effective in high-risk patients. Single-cell analysis revealed that these 7 genes not only differ at the level of immune cells but also in epithelial cells, fibroblasts, and myofibroblasts, among other cell types. Finally, the expression of these key genes was verified in clinical samples, with consistent results., Conclusions: Our research findings provide evidence for the role of disulfidptosis in COAD and offer new insights for personalized and precise treatment of COAD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Lai, Huang, Li, Peng, Wu, Xin, Lu, Ouyang, Bai, Lei, He and Lin.)