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Background Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management. Methods We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF). Results A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in ‘cell cycle’ and ‘oocyte meiosis’ related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients. Conclusions The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients’ prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Battery screening is the key segment in secondary applications. The benchmark for conventional methods is mainly based on the series connection and makes parameter difference as the screening index a gold standard. However, because of self-balancing current in parallel connection, the existence of a certain degree of parameter difference is allowed and parameter difference may not be the best option, which leads to lower screening efficiency due to the higher uniform of parameters. This work first identifies the boundary of parameter difference and provides the ideal working point (IWP), which is related to maximum capacity utilization efficiency, as a novel screening index for parallel connection derived from the current distribution. A modified shepherd model is employed to calculate the IWPs and is verified that the maximum dynamic error is below 1.1%. Therefore, it is quick to achieve screening by judging whether the IWP falls within the normal working range. The results show that the maximum capacity utilization efficiency always occurs at the load current close to the IWP, which verifies the validity of IWP. Compared with the conventional method, the proposed method is validated based on a case study to improve screening efficiency and provides different ideas for flexible grouping.

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