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Prognostics and health management (PHM) is widely applied to assess the reliability, safety and operation of systems particularly in spacecraft systems. However, spacecraft systems are very complex with intangibility and uncertainty, and it is difficult to model and analyze the complex degradation process, and thus there is no single prognostic method for solving the critical and complicated problem. This paper presents a novel hierarchical multi-class classification method using deep neural networks (DNN) and weighted support vector machine (WSVM) in order to achieve a highly discriminative feature representation for classifying the multimodal spacecraft data. First, the stack auto-Encoder (SAE) or deep belief network is adopted to initialize the initial weights and offsets of the hierarchical multi-layer neural network in order to reduce the dimension of the original multimodal data, and the optimal depth of multi-layer neural network and the discriminative features are also obtained. Second, in order to make the high dimensional spacecraft data more separable, the initialization parameters are online monitored by using a gradient descent method. Finally, a flexible hierarchical estimation method of a multi-class weighted support vector machines (MCWSVM) is applied to classify the multimodal spacecraft data. The performance of the proposed work is evaluated by the classification accuracy, sensitivity, specificity and execution time, respectively. The results demonstrate that the proposed DNN with MCWSVM is efficient in terms of better classification accuracy at a lesser execution time when compared to K-nearest neighbors (KNN), SVM and naive Bayes method (NBM).

As the most prevalent cancer for females, breast cancer is also the second most popular cancer type overall. More efforts are needed to research new drugs and combination therapies for this disease. A naturally derived transient receptor potential melastatin-like 7 channel (TRPM7) inhibitor, carvacrol, was found to have anti-cancer potentials. We hypothesized that carvacrol affects breast cancer cells through TRPM7 mediated cell cycle regulation. Cell viability and apoptosis of breast cancer cell lines BT-483, BT-474, MCF-7, MDA-MB-231, and MDA-MB-453 were determined using the CCK-8 assay and ELISA respectively. TRPM7 in MDA-MB-231, MCF-7 was knocked down. Functional TRPM7 in MDA-MB-231, MCF-7, and HEK293 cells were tested with western blotting, patch-clamp, and fura-2 quench assay. The cell cycle and the regulatory proteins were determined by flow cytometry and western blotting. Results showed that carvacrol inhibited the viability of breast cancer cells with different potency. At 200 μM, MDA-MB-231 was the most sensitive, and MCF-7 was the least sensitive. At >200 μM, the apoptosis was dramatically induced. Carvacrol inhibited TRPM7 functions in MDA-MB-231, MCF-7, and HEK293. Carvacrol at 200 μM increased cells in the G1/G0 phase and decreased cells in the S and G2/M phase by regulating some cyclin proteins in MDA-MB-231. These effects were blocked by the knockdown of TRPM7. This study demonstrated that carvacrol suppresses breast cancer cells by cell cycle regulation and the TRPM7 pathway is one of the pharmacological mechanisms.