We sought to determine whether STAT3 mediated tamoxifen resistance of breast cancer stem cells in vitro. The capacities for mammosphere formation and STAT3 expression of CD44+CD24−/low MCF-7 and MCF-7 were observed. The CD44+CD24−/low subpopulation ratio and its sensitivity to adriamycin were analyzed in MCF-7 and TAM resistant (TAM-R) cells. Cell cycle, apoptosis, STAT3 and phospho-STAT3 changes were observed after treatment with tamoxifen. Small interference RNA-mediated knockdown of STAT3 in TAM-R cells was also performed. CD44+CD24−/low MCF-7 showed higher capacities for mammosphere formation and STAT3 expression than total MCF-7. The CD44+CD24−/low subpopulation was also upregulated in TAM-R cells with less sensitivity to adriamycin than MCF-7. Cell cycle changes, anti-apoptotic effects and STAT3 changes were also found. Meanwhile, the knock-down of STAT3 in TAM-R resulted in an increase in sensitivity to tamoxifen. It is concluded that STAT3 plays an essential role in breast cancer stem cells, which correlated with tamoxifen resistance.

In practice, investigations for bone metastasis of breast cancer rely heavily on models in vivo. Lacking of such ideal model makes it difficult to study the whole process or accurate mechanism of each step of this metastatic disease. Development of xenograft mouse models has made great contributions in this area. Currently, the best animal model of breast cancer metastasizing to bone is NOD/SCID-hu models containing human bone, which makes it possible to let the breast cancer cells and the bone target of osteotropic metastasis be both of human origin. We have developed a novel mouse model containing both human bone and breast, and proved it functional and reliable. In this study, a set of human breast cancer cell line including MDA-MB-231, MDA-MB-231BO, MCF-7, ZR-75-1 and SUM1315 were characterized their osteotropism in this model. A specific cell line SUM1315 made species-specific bone metastasis, certifying the osteotropism-identification utility of the novel mouse model. Furthermore, gene expression and microRNA expression profiling analysis were done to the two SUM1315 derived sub lines isolated and purified from the orthotopic and metastatic xenograft. In addition, to demonstrate the disparity between the "spontaneous" and "forced" bone metastasis in mouse model, MDA-MB-231 cells were inoculated into both the human implants in this model simultaneously, and then primary cultured and profiling analyzed. Supported by overall results of profiling analyses, this study suggested the novel model was a useful tool for understanding, preventing and treating bone metastasis of breast cancer, meanwhile it had provided significant information for further investigations.

Multi-mode process monitoring is a key issue often raised in industrial process control. Most multivariate statistical process monitoring strategies, such as principal component analysis (PCA) and partial least squares, make an essential assumption that the collected data follow a unimodal or Gaussian distribution. However, owing to the complexity and the multi-mode feature of industrial processes, the collected data usually follow different distributions. This paper proposes a novel multi-mode data processing method called weighted k neighbourhood standardisation (WKNS) to address the multi-mode data problem. This method can transform multi-mode data into an approximately unimodal or Gaussian distribution. The results of theoretical analysis and discussion suggest that the WKNS strategy is more suitable for multi-mode data normalisation than the z-score method is. Furthermore, a new fault detection approach called WKNS-PCA is developed and applied to detect process outliers. This method does not require process knowledge and multi-mode modelling; only a single model is required for multi-mode process monitoring. The proposed method is tested on a numerical example and the Tennessee Eastman process. Finally, the results demonstrate that the proposed data preprocessing and process monitoring methods are particularly suitable and effective in multi-mode data normalisation and industrial process fault detection.A complete fault monitoring scheme called WKNS-PCA is introduced in this section, the goal of which is to illustrate the validity and effectiveness of the WKNS data preprocessing method.

In industrial processes, investigating the root causes of abnormal events is a crucial task when process faults are detected; isolating the faulty variables provides additional information for investigating the root causes of the faults. The traditional contribution plot is a popular and perspicuous tool to isolate faulty variables. However, this method can only determine one faulty variable (the biggest contributor) when there are several variables out of control at the same time. In the presented work, a novel fault diagnosis method is derived using k-nearest neighbor (kNN) reconstruction on maximize reduce index (MRI) sensors; it is aimed at identifying all fault variables precisely. This method can identify the faulty variables effectively through reconstructing MRI variables one by one. A numerical example focuses on validating the performance of kNN missing data analysis method firstly, then multi-sensors fault identification results are also given. Tennessee Eastman process is provided to demonstrate that the proposed approach can identify the responsible variables for the multiple sensors fault. Figure 1. Flow chart of the proposed method. 400wileyonlinelibrary.com/journal/cem

No abstract

Combination of Atmospheric and Room Temperature Plasma (ARTP) mutagenesis, Genome shuffling and Dimethyl sulfoxide (DMSO) feeding to improve FK506 production in Streptomyces tsukubaensis

No abstract

No abstract