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The demand for complex equipment aftermarket parts is mostly sporadic, showing typical intermittent characteristics as a whole, resulting in the evolution law of a single demand series having insufficient information, which restricts the prediction effect of existing methods. To solve this problem, this paper proposes a prediction method of intermittent feature adaptation from the perspective of transfer learning. Firstly, to extract the intermittent features of the demand series, an intermittent time series domain partitioning algorithm is proposed by mining the demand occurrence time and demand interval information in the series, then constructing the metrics, and using a hierarchical clustering algorithm to divide all the series into different sub-source domains. Secondly, the intermittent and temporal characteristics of the sequence are combined to construct a weight vector, and the learning of common information between domains is accomplished by weighting the distance of the output features of each cycle between domains. Finally, experiments are conducted on the actual after-sales datasets of two complex equipment manufacturing enterprises. Compared with various prediction methods, the method in this paper can effectively predict future demand trends, and the prediction's stability and accuracy are significantly improved. [ABSTRACT FROM AUTHOR]

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results. [ABSTRACT FROM AUTHOR]

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved. [ABSTRACT FROM AUTHOR]

Highlights: [•] Upregulation of LSD1 correlates with lymph node metastasis in ESCC. [•] LSD1 overexpression predicts poor prognosis in ESCC patients. [•] LSD1 knockdown using LSD1-specific shRNAs inhibited motility and invasion in ESCC. [•] A LSD1 inhibitor tranylcypromine attenuated migration and invasiveness. [ABSTRACT FROM AUTHOR]

Abstract: Tumor necrosis factor-related apoptosis induced ligand (TRAIL) is an important apoptosis inducer in a variety of tumor cells. In the present study, we determined the underlying molecular mechanisms by which certain gastric cancer cells are resistant to TRAIL. We first detected expression of programmed cell death 4 (PDCD4) in three gastric cancer cell lines and identified its association with the sensitivity of gastric cancer cells to TRAIL. We then stably transfected PDCD4 cDNA or shRNA into these gastric cell lines. Our data showed that restoration of PDCD4 expression induced TRAIL sensitivity, whereas knockdown of PDCD4 expression reduced the sensitivity of these tumor cells to TRAIL treatment. PDCD4 was able to suppress expression of FLICE-inhibiting protein (FLIP), a negative regulator of apoptosis. Knockdown of FLIP expression using FLIP shRNA had similar effects as those of restored PDCD4 expression. Furthermore, the proteasome inhibitor MG132 was able to inhibit expression of FLIP mRNA and protein and upregulate the sensitivity of these cells to TRAIL treatment. Taken together, the results from the current study demonstrated that PDCD4 plays an important role in mediating the sensitivity of gastric cancer cells to TRAIL-induced apoptosis through FLIP suppression. Therefore, the proteasome inhibitor MG132 should be further evaluated for combination therapy with TRAIL. [Copyright &y& Elsevier]